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20 pages, 8380 KiB

Open AccessArticle

Controlling an Industrial Robot Using a Graphic Tablet in Offline and Online Mode

by Wojciech Kaczmarek, Bartłomiej Lotys, Szymon Borys, Dariusz Laskowski and Piotr Lubkowski

Sensors 2021, 21(7), 2439; https://doi.org/10.3390/s21072439 - 1 Apr 2021

Cited by 15 | Viewed by 4805

Abstract

The article presents the possibility of using a graphics tablet to control an industrial robot. The paper presents elements of software development for offline and online control of a robot. The program for the graphic tablet and the operator interface was developed in [...] Read more.

The article presents the possibility of using a graphics tablet to control an industrial robot. The paper presents elements of software development for offline and online control of a robot. The program for the graphic tablet and the operator interface was developed in C# language in Visual Studio environment, while the program controlling the industrial robot was developed in RAPID language in the RobotStudio environment. Thanks to the development of a digital twin of the real robotic workstation, tests were carried out on the correct functioning of the application in offline mode (without using the real robot). The obtained results were verified in online mode (on a real production station). The developed computer programmes have a modular structure, which makes it possible to easily adapt them to one’s needs. The application allows for changing the parameters of the robot and the parameters of the path drawing. Tests were carried out on the influence of the sampling frequency and the tool diameter on the quality of the reconstructed trajectory of the industrial robot. The results confirmed the correctness of the application. Thanks to the new method of robot programming, it is possible to quickly modify the path by the operator, without the knowledge of robot programming languages. Further research will focus on analyzing the influence of screen resolution and layout scale on the accuracy of trajectory generation. Full article

(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring)

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15 pages, 5602 KiB

Open AccessArticle

Module for Monitoring the Probe-Skin Contact Force in the Study of Vibration Perception on the Wrist

by Dorota Czopek, Robert Barański and Jerzy Wiciak

Sensors 2021, 21(6), 2128; https://doi.org/10.3390/s21062128 - 18 Mar 2021

Cited by 2 | Viewed by 2572

Abstract

This paper presents a module for monitoring the contact force between a probe for measuring vibration perception on the wrist and the skin. The module was designed for an original measuring stand for the automatic testing of the vibrotactile discrimination thresholds using the [...] Read more.

This paper presents a module for monitoring the contact force between a probe for measuring vibration perception on the wrist and the skin. The module was designed for an original measuring stand for the automatic testing of the vibrotactile discrimination thresholds using the psychophysical adaptive method of 1 up–2 down with two or three interval forced choices (2IFC, 3IFC). Measurement methods were implemented in LabVIEW software. The inspiration for the project was the need to check the possibility of building a vibrating interface for transmitting information through vibrations delivered to the wrist via a bracelet. The test procedure on the wrist is not standardized; however, during its development, the recommendations of the Polish Norm–International Organization for Standardization PN-ISO 13091-1, 2006 were adopted. This standard contains methods for measuring vibration sensation thresholds on the fingertips for the assessment of neural dysfunction. The key to the repeatability of measurements seems to be the ability to continuously control the pressure of the measuring probe on the skin. This article compares two solutions for measuring the contact force along with an analysis of their accuracy and the impact of vibrations on the measured values. Moreover, the results of measurements of vibrotactile amplitude and frequency discrimination thresholds obtained on the ventral wrist at five frequencies (25, 32, 63, 125 and 250 Hz) are presented. Full article

(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring)

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27 pages, 14220 KiB

Open AccessArticle

Issues of Data Acquisition and Interpretation of Paraseismic Measuring Signals Triggered by the Detonation of Explosive Charges

by Józef Pyra and Maciej Kłaczyński

Sensors 2021, 21(4), 1290; https://doi.org/10.3390/s21041290 - 11 Feb 2021

Cited by 6 | Viewed by 2414

Abstract

The paper tackles the issues of data acquisition during the measuring of vibrations caused by the detonation of explosive charges in various types of works (blasting in mines, demolition works, tunneling). Depending on the placement of an explosive charge (a charge detonated on [...] Read more.

The paper tackles the issues of data acquisition during the measuring of vibrations caused by the detonation of explosive charges in various types of works (blasting in mines, demolition works, tunneling). Depending on the placement of an explosive charge (a charge detonated on the surface or a charge placed in a hole), it triggers side effects in the form of mechanical vibrations, which are propagated in the environment and may pose a hazard to buildings. In the case of propagation in the air, there is an acoustic wave and an airblast wave. For the assessment analysis on the impact of vibrations on buildings, a ground-propagated signal is used, while what is propagated by air is a disturbance. Selected examples in the paper demonstrate how an acoustic wave and an airblast wave interferes with the signal recorded by geophones. Afterwards, the paper presents the results of the tests conducted at a training area, during which various masses of explosive charges placed in different ways were detonated. The examples demonstrate that this interference may lead to the misinterpretation of recorded measurements. This paper is the first of two papers that will present the results of research into this matter and the suggested resolutions in order to eliminate this interference. Full article

(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring)

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20 pages, 8565 KiB

Open AccessArticle

Use of Bispectrum Analysis to Inspect the Non-Linear Dynamic Characteristics of Beam-Type Structures Containing a Breathing Crack

by Li Cui, Hao Xu, Jing Ge, Maosen Cao, Yangmin Xu, Wei Xu and Dragoslav Sumarac

Sensors 2021, 21(4), 1177; https://doi.org/10.3390/s21041177 - 7 Feb 2021

Cited by 20 | Viewed by 3096

Abstract

A breathing crack is a typical form of structural damage attributed to long-term dynamic loads acting on engineering structures. Traditional linear damage identification methods suffer from the loss of valuable information when structural responses are essentially non-linear. To deal with this issue, bispectrum [...] Read more.

A breathing crack is a typical form of structural damage attributed to long-term dynamic loads acting on engineering structures. Traditional linear damage identification methods suffer from the loss of valuable information when structural responses are essentially non-linear. To deal with this issue, bispectrum analysis is employed to study the non-linear dynamic characteristics of a beam structure containing a breathing crack, from the perspective of numerical simulation and experimental validation. A finite element model of a cantilever beam is built with contact elements to simulate a breathing crack. The effects of crack depth and location, excitation frequency and magnitude, and measurement noise on the non-linear behavior of the beam are studied systematically. The result demonstrates that bispectral analysis can effectively identify non-linear damage in different states with strong noise immunity. Compared with existing methods, the bispectral non-linear analysis can efficiently extract non-linear features of a breathing crack, and it can overcome the limitations of existing linear damage detection methods used for non-linear damage detection. This study’s outcome provides a theoretical basis and a paradigm for damage identification in cracked structures. Full article

(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring)

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23 pages, 45133 KiB

Open AccessArticle

Structural Damage Identification Based on Integrated Utilization of Inverse Finite Element Method and Pseudo-Excitation Approach

by Tengteng Li, Maosen Cao, Jianle Li, Lei Yang, Hao Xu and Zhanjun Wu

Sensors 2021, 21(2), 606; https://doi.org/10.3390/s21020606 - 16 Jan 2021

Cited by 10 | Viewed by 2866

Abstract

The attempt to integrate the applications of conventional structural deformation reconstruction strategies and vibration-based damage identification methods is made in this study, where, more specifically, the inverse finite element method (iFEM) and pseudo-excitation approach (PE) are combined for the first time, to give [...] Read more.

The attempt to integrate the applications of conventional structural deformation reconstruction strategies and vibration-based damage identification methods is made in this study, where, more specifically, the inverse finite element method (iFEM) and pseudo-excitation approach (PE) are combined for the first time, to give rise to a novel structural health monitoring (SHM) framework showing various advantages, particularly in aspects of enhanced adaptability and robustness. As the key component of the method, the inverse finite element method (iFEM) enables precise reconstruction of vibration displacements based on measured dynamic strains, which, as compared to displacement measurement, is much more adaptable to existing on-board SHM systems in engineering practice. The PE, on the other hand, is applied subsequently, relying on the reconstructed displacements for the identification of structural damage. Delamination zones in a carbon fibre reinforced plastic (CFRP) laminate are identified using the developed method. As demonstrated by the damage detection results, the iFEM-PE method possesses apparently improved accuracy and significantly enhanced noise immunity compared to the original PE approach depending on displacement measurement. Extensive parametric study is conducted to discuss the influence of a variety of factors on the effectiveness and accuracy of damage identification, including the influence of damage size and position, measurement density, sensor layout, vibration frequency and noise level. It is found that different factors are highly correlated and thus should be considered comprehensively to achieve optimal detection results. The application of the iFEM-PE method is extended to better adapt to the structural operational state, where multiple groups of vibration responses within a wide frequency band are used. Hybrid data fusion is applied to process the damage index (DI) constructed based on the multiple responses, leading to detection results capable of indicating delamination positions precisely. Full article

(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring)

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17 pages, 4950 KiB

Open AccessArticle

Model of the Vibration Signal of the Vibrating Sieving Screen Suspension for Condition Monitoring Purposes

by Anna Michalak, Jacek Wodecki, Michał Drozda, Agnieszka Wyłomańska and Radosław Zimroz

Sensors 2021, 21(1), 213; https://doi.org/10.3390/s21010213 - 31 Dec 2020

Cited by 18 | Viewed by 3614

Abstract

Diagnostics of industrial machinery is a topic related to the need for damage detection, but it also allows to understand the process itself. Proper knowledge about the operational process of the machine, as well as identification of the underlying components, is critical for [...] Read more.

Diagnostics of industrial machinery is a topic related to the need for damage detection, but it also allows to understand the process itself. Proper knowledge about the operational process of the machine, as well as identification of the underlying components, is critical for its diagnostics. In this paper, we present a model of the signal, which describes vibrations of the sieving screen. This particular type is used in the mining industry for the classification of ore pieces in the material stream by size. The model describes the real vibration signal measured on the spring set being the suspension of this machine. This way, it is expected to help in better understanding how the overall motion of the machine can impact the efforts of diagnostics. The analysis of real vibration signals measured on the screen allowed to identify and parameterize the key signal components, which carry valuable information for the following stages of diagnostic process of that machine. In the proposed model we take into consideration deterministic components related to shaft rotation, stochastic Gaussian component related to external noise, stochastic

α

-stable component as a model of excitations caused by falling rocks pieces, and identified machine response to unitary excitations. Full article

(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring)

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33 pages, 15274 KiB

Open AccessArticle

Local Defect Detection in Bearings in the Presence of Heavy-Tailed Noise and Spectral Overlapping of Informative and Non-Informative Impulses

by Jakub Nowicki, Justyna Hebda-Sobkowicz, Radoslaw Zimroz and Agnieszka Wylomanska

Sensors 2020, 20(22), 6444; https://doi.org/10.3390/s20226444 - 11 Nov 2020

Cited by 10 | Viewed by 2477

Abstract

The problem of the informative frequency band (IFB) selection for local fault detection is considered in the paper. There are various approaches that are very effective in this issue. Most of the techniques are vibration-based and they are related to the cyclic impulses [...] Read more.

The problem of the informative frequency band (IFB) selection for local fault detection is considered in the paper. There are various approaches that are very effective in this issue. Most of the techniques are vibration-based and they are related to the cyclic impulses detection (associated with the local fault) in the background noise. However, when the background noise in the vibration signal has non-Gaussian impulsive behavior, the classical methods seem to be insufficient. Recently, new techniques were proposed by several authors and interesting approaches were tested for different non-Gaussian signals. We demonstrate the comparative analysis related to the results for three selected techniques proposed in recent years, namely the Alpha selector, Conditional Variance-based selector, and Spearman selector. The techniques seem to be effective for the IFB selection for the non-Gaussian distributed vibration signals. The main purpose of this article is to investigate how spectral overlapping of informative and non-informative impulsive components will affect diagnostic procedures. According to our knowledge, this problem was not considered in the literature for the non-Gaussian signals. Nevertheless, as we demonstrated by the simulations, the level of overlapping and the location of a center frequency of the mentioned frequency bands have a significant influence on the behavior of the considered selectors. The discussion about the effectiveness of each analyzed method is conducted. The considered problem is supported by real-world examples. Full article

(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring)

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21 pages, 10144 KiB

Open AccessArticle

Analysis on the Possibility of Eliminating Interference from Paraseismic Vibration Signals Induced by the Detonation of Explosive Materials

by Józef Pyra, Maciej Kłaczyński and Rafał Burdzik

Sensors 2020, 20(21), 6401; https://doi.org/10.3390/s20216401 - 9 Nov 2020

Cited by 2 | Viewed by 2684

Abstract

This article presents the results of studies on the impact of acoustic waves on geophones and microphones used to measure airblasts carried out in a reverberation chamber. During the tests, a number of test signals were generated, of which two are presented in [...] Read more.

This article presents the results of studies on the impact of acoustic waves on geophones and microphones used to measure airblasts carried out in a reverberation chamber. During the tests, a number of test signals were generated, of which two are presented in this article: frequency-modulated sine (sine sweep) waves in the 30–300 Hz range, and the result of detonating 3 g of pyrotechnic material inside the chamber. Then, based on the short-time Fourier transform, the spectral subtraction method was used to remove unwanted disruption interfering with the recorded signal. Using MATLAB software, a program was written that was calibrated and adapted to the specifics of the measuring equipment based on the collected test results. As a result, it was possible to clean the signals of interference and obtain a vibration signal propagated by the substrate. The results are based on signals registered in the laboratory and made in field conditions during the detonation of explosive materials. Full article

(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring)

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15 pages, 6197 KiB

Open AccessArticle

Industrial Robot Control by Means of Gestures and Voice Commands in Off-Line and On-Line Mode

by Wojciech Kaczmarek, Jarosław Panasiuk, Szymon Borys and Patryk Banach

Sensors 2020, 20(21), 6358; https://doi.org/10.3390/s20216358 - 7 Nov 2020

Cited by 37 | Viewed by 6533

Abstract

The paper presents the possibility of using the Kinect v2 module to control an industrial robot by means of gestures and voice commands. It describes the elements of creating software for off-line and on-line robot control. The application for the Kinect module was [...] Read more.

The paper presents the possibility of using the Kinect v2 module to control an industrial robot by means of gestures and voice commands. It describes the elements of creating software for off-line and on-line robot control. The application for the Kinect module was developed in the C# language in the Visual Studio environment, while the industrial robot control program was developed in the RAPID language in the RobotStudio environment. The development of a two-threaded application in the RAPID language allowed separating two independent tasks for the IRB120 robot. The main task of the robot is performed in Thread No. 1 (responsible for movement). Simultaneously, Thread No. 2 ensures continuous communication with the Kinect system and provides information about the gesture and voice commands in real time without any interference in Thread No. 1. The applied solution allows the robot to work in industrial conditions without the negative impact of the communication task on the time of the robot’s work cycles. Thanks to the development of a digital twin of the real robot station, tests of proper application functioning in off-line mode (without using a real robot) were conducted. The obtained results were verified on-line (on the real test station). Tests of the correctness of gesture recognition were carried out, and the robot recognized all programmed gestures. Another test carried out was the recognition and execution of voice commands. A difference in the time of task completion between the actual and virtual station was noticed; the average difference was 0.67 s. The last test carried out was to examine the impact of interference on the recognition of voice commands. With a 10 dB difference between the command and noise, the recognition of voice commands was equal to 91.43%. The developed computer programs have a modular structure, which enables easy adaptation to process requirements. Full article

(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring)

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19 pages, 7175 KiB

Open AccessArticle

Application of Wireless Accelerometer Mounted on Wheel Rim for Parked Car Monitoring

by Michal Borecki, Arkadiusz Rychlik, Arkadiusz Olejnik, Przemysław Prus, Jan Szmidt and Michael L. Korwin-Pawlowski

Sensors 2020, 20(21), 6088; https://doi.org/10.3390/s20216088 - 26 Oct 2020

Cited by 6 | Viewed by 3446

Abstract

Damages of different kinds that can be inflicted to a parked car. Among them, loosening of the car wheel bolts is difficult to detect during normal use of the car and is at the same time very dangerous to the health and life [...] Read more.

Damages of different kinds that can be inflicted to a parked car. Among them, loosening of the car wheel bolts is difficult to detect during normal use of the car and is at the same time very dangerous to the health and life of the driver. Moreover, in patents and publications, only little information is presented about electronic sensors available for activation from inside of the car to inform the driver about the mentioned dangerous situation. Thus, the main aim of this work is the proposition and examination of a sensing device using of a wireless accelerometer head to detect loosening of wheel fixing bolts before ride has been started. The proposed sensing device consists of a wireless accelerometer head, an assembly interface and a receiver unit. The assembly interface between the head and the inner part of the rim enables the correct operation of the system. The data processing algorithm developed for the receiver unit enables the proper detection of the unscrewing of bolts. Moreover, the tested algorithm is resistant to the interference signals generated in the accelerometer head by cars and men passing in close distance. Full article

(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring)

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17 pages, 6710 KiB

Open AccessArticle

Effect of Loading Rate on Tensile and Failure Behavior of Concrete

by Xiaocui Chen, Liguo Sun, Wenhu Zhao and Yuan Zheng

Sensors 2020, 20(21), 5994; https://doi.org/10.3390/s20215994 - 22 Oct 2020

Cited by 3 | Viewed by 3100

Abstract

Three-point bending experiments of concrete beams were conducted under the strain rate range of 10⁻⁶ s⁻¹ and 1.5 × 10⁻³ s⁻¹. A novel 3D laser scanner, Handy SCAN, was employed to detect the areas of interface, mortar and [...] Read more.

Three-point bending experiments of concrete beams were conducted under the strain rate range of 10⁻⁶ s⁻¹ and 1.5 × 10⁻³ s⁻¹. A novel 3D laser scanner, Handy SCAN, was employed to detect the areas of interface, mortar and aggregate on the crack surface after the experiment. In this paper, the inhomogeneity of materials and the inertial effect were considered as the main factors in the strength enhancement of concrete together with a proposed dynamic model. With the obtained experimental results, the initial elastic modulus and tensile strength of concrete showed obvious rate sensitivity. Moreover, an empirical relationship of dynamic increase factor and strain rate was established for the strain rate range of 10⁻⁶ s⁻¹ and 1.5 × 10⁻³ s⁻¹. The contributions of aggregate and inertia effect to the dynamic enhancement of concrete strength were quantified with respect to the loading rate. The rate effect of concrete obtained by the experiments was verified by the finite element analysis on the mesoscopic scale with the model built by the three-dimensional random aggregate software. Full article

(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring)

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19 pages, 4221 KiB

Open AccessArticle

Assessment of the Road Surface Condition with Longitudinal Acceleration Signal of the Car Body

by Krzysztof Prażnowski, Jarosław Mamala, Michał Śmieja and Mariusz Kupina

Sensors 2020, 20(21), 5987; https://doi.org/10.3390/s20215987 - 22 Oct 2020

Cited by 16 | Viewed by 3802

Abstract

On the basis of road tests, the authors assessed the feasibility of the vehicle body acceleration values for the purposes of assessing road surface characteristics in terms of its roughness. Short-term Fourier Transform (STFT) was used for the analysis of the recorded signal. [...] Read more.

On the basis of road tests, the authors assessed the feasibility of the vehicle body acceleration values for the purposes of assessing road surface characteristics in terms of its roughness. Short-term Fourier Transform (STFT) was used for the analysis of the recorded signal. The spectra obtained in successive frequency bands demonstrate the amplitudes originating from the natural vibrations of the rolling wheel and forces resulting from the interaction with the road roughness. The article focuses on the relationships between the road roughness and the ratios of individual amplitudes in a specific frequency band of the vehicle body acceleration values. Amplitude values derived on the basis of successive windows were averaged for analogous, arbitrarily assumed local frequency bands. The value characterizing the road surface condition provided the information regarding the mean amplitude value in specific frequency ranges depending on the instantaneous velocity of the car body and the condition of the road surface on which it was moving. In cases where the road was free of any visible roughness, the obtained mean amplitude value in the analyzed spectrum window, for the adopted vehicle velocity range from 50 km h to 100 km/h, did not exceed 0.02 m/s². It was also demonstrated that the road surface roughness leads to an increase in the mean amplitude value from 0.07 m/s² to 0.16 m/s². Full article

(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring)

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17 pages, 1252 KiB

Open AccessArticle

Decision Tree-Based Classification for Planetary Gearboxes’ Condition Monitoring with the Use of Vibration Data in Multidimensional Symptom Space

by Piotr Lipinski, Edyta Brzychczy and Radoslaw Zimroz

Sensors 2020, 20(21), 5979; https://doi.org/10.3390/s20215979 - 22 Oct 2020

Cited by 27 | Viewed by 4576

Abstract

Monitoring the condition of rotating machinery, especially planetary gearboxes, is a challenging problem. In most of the available approaches, diagnostic procedures are related to advanced signal pre-processing/feature extraction methods or advanced data (features) analysis by using artificial intelligence. In this paper, the second [...] Read more.

Monitoring the condition of rotating machinery, especially planetary gearboxes, is a challenging problem. In most of the available approaches, diagnostic procedures are related to advanced signal pre-processing/feature extraction methods or advanced data (features) analysis by using artificial intelligence. In this paper, the second approach is explored, so an application of decision trees for the classification of spectral-based 15D vectors of diagnostic data is proposed. The novelty of this paper is that by a combination of spectral analysis and the application of decision trees to a set of spectral features, we are able to take advantage of the multidimensionality of diagnostic data and classify/recognize the gearbox condition almost faultlessly even in non-stationary operating conditions. The diagnostics of time-varying systems are a complicated issue due to time-varying probability densities estimated for features. Using multidimensional data instead of an aggregated 1D feature, it is possible to improve the efficiency of diagnostics. It can be underlined that in comparison to previous work related to the same data, where the aggregated 1D variable was used, the efficiency of the proposed approach is around 99% (ca. 19% better). We tested several algorithms: classification and regression trees with the Gini index and entropy, as well as the random tree. We compare the obtained results with the K-nearest neighbors classification algorithm and meta-classifiers, namely: random forest and AdaBoost. As a result, we created the decision tree model with 99.74% classification accuracy on the test dataset. Full article

(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring)

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20 pages, 62999 KiB

Open AccessArticle

Development of Insert Condition Classification System for CNC Lathes Using Power Spectral Density Distribution of Accelerometer Vibration Signals

by Yi-Wen Huang and Syh-Shiuh Yeh

Sensors 2020, 20(20), 5907; https://doi.org/10.3390/s20205907 - 19 Oct 2020

Cited by 1 | Viewed by 4537

Abstract

Insert conditions significantly influence the product quality and manufacturing efficiency of lathe machining. This study used the power spectral density distribution of the vibration signals of a lathe machining accelerometer to design an insert condition classification system applicable to different machining conditions. For [...] Read more.

Insert conditions significantly influence the product quality and manufacturing efficiency of lathe machining. This study used the power spectral density distribution of the vibration signals of a lathe machining accelerometer to design an insert condition classification system applicable to different machining conditions. For four common lathe machining insert conditions (i.e., built-up edge, flank wear, normal, and fracture), herein, the insert condition classification system was established with two stages—insert condition modeling and machining model fusion. In the insert condition modeling stage, the magnitude features of the segmented frequencies were captured according to the power spectral density distributions of the accelerometer vibration signals. Principal component analysis and backpropagation neural networks were used to develop insert condition models for different machining conditions. In the machining model fusion stage, a backpropagation neural network was employed to establish the weight function between the machining conditions and insert condition models. Subsequently, the insert conditions were classified based on the calculated weight values of all the insert condition models. Cutting tests were performed on a computer numerical control (CNC) lathe and utilized to validate the feasibility of the designed insert condition classification system. The results of the cutting tests showed that the designed system could perform insert condition classification under different machining conditions, with a classification rate exceeding 80%. Using a triaxial accelerometer, the designed insert condition classification system could perform identification and classification online for four common insert conditions under different machining conditions, ensuring that CNC lathes could further improve manufacturing quality and efficiency in practice. Full article

(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring)

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19 pages, 4106 KiB

Open AccessArticle

Identification, Decomposition and Segmentation of Impulsive Vibration Signals with Deterministic Components—A Sieving Screen Case Study

by Karolina Gąsior, Hanna Urbańska, Aleksandra Grzesiek, Radosław Zimroz and Agnieszka Wyłomańska

Sensors 2020, 20(19), 5648; https://doi.org/10.3390/s20195648 - 2 Oct 2020

Cited by 14 | Viewed by 3261

Abstract

Condition monitoring is a well-established field of research; however, for industrial applications, one may find some challenges. They are mostly related to complex design, a specific process performed by the machine, time-varying load/speed conditions, and the presence of non-Gaussian noise. A procedure for [...] Read more.

Condition monitoring is a well-established field of research; however, for industrial applications, one may find some challenges. They are mostly related to complex design, a specific process performed by the machine, time-varying load/speed conditions, and the presence of non-Gaussian noise. A procedure for vibration analysis from the sieving screen used in the raw material industry is proposed in the paper. It is more for pre-processing than the damage detection procedure. The idea presented here is related to identification and extraction of two main types of components: (i) deterministic (D)—related to the unbalanced shaft(s) and (ii) high amplitude, impulsive component randomly (R) appeared in the vibration due to pieces of ore falling down of moving along the deck. If we could identify these components, then we will be able to perform classical diagnostic procedures for local damage detection in rolling element bearing. As deterministic component may be AM/FM modulated and each impulse may appear with different amplitude and damping, there is a need for an automatic procedure. We propose a method for signal processing that covers two main steps: (a) related to R/D decomposition and including signal segmentation to neglect AM/FM modulations, iterative sine wave fitting using the least square method (for each segment), signal filtering technique by subtraction fitted sine from the raw signal, the definition of the criterion to stop iteration by residuals analysis, (b) impulse segmentation and description (beginning, end, max amplitude) that contains: detection of the number of impulses in a decomposed random part of the raw signal, detection of the max value of each impulse, statistical analysis (probability density function) of max value to find regime-switching), modeling of the envelope of each impulse for samples that protrude from the signal, extrapolation (forecasting) envelope shape for samples hidden in the signal. The procedure is explained using simulated and real data. Each step is very easy to implement and interpret thus the method may be used in practice in a commercial system. Full article

(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring)

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14 pages, 3946 KiB

Open AccessArticle

Deep Quality Assessment of a Solar Reflector Based on Synthetic Data: Detecting Surficial Defects from Manufacturing and Use Phase

by Alexios Papacharalampopoulos, Konstantinos Tzimanis, Kyriakos Sabatakakis and Panagiotis Stavropoulos

Sensors 2020, 20(19), 5481; https://doi.org/10.3390/s20195481 - 24 Sep 2020

Cited by 20 | Viewed by 2845

Abstract

Vision technologies are used in both industrial and smart city applications in order to provide advanced value products due to embedded self-monitoring and assessment services. In addition, for the full utilization of the obtained data, deep learning is now suggested for use. To [...] Read more.

Vision technologies are used in both industrial and smart city applications in order to provide advanced value products due to embedded self-monitoring and assessment services. In addition, for the full utilization of the obtained data, deep learning is now suggested for use. To this end, the current work presents the implementation of image recognition techniques alongside the original the quality assessment of a Parabolic Trough Collector (PTC) reflector surface to locate and identify surface irregularities by classifying images as either acceptable or non-acceptable. The method consists of a three-step solution that promotes an affordable implementation in a relatively small time period. More specifically, a 3D Computer Aided Design (CAD) of the PTC was used for the pre-training of neural networks, while an aluminum reflector surface was used to verify algorithm performance. The results are promising, as this method proved applicable in cases where the actual part was manufactured in small batches or under the concept of customized manufacturing. Consequently, the algorithm is capable of being trained with a limited number of data. Full article

(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring)

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21 pages, 10801 KiB

Open AccessArticle

Selection and Optimization of the Parameters of the Robotized Packaging Process of One Type of Product

by Szymon Borys, Wojciech Kaczmarek and Dariusz Laskowski

Sensors 2020, 20(18), 5378; https://doi.org/10.3390/s20185378 - 19 Sep 2020

Cited by 15 | Viewed by 4201

Abstract

The article presents the results of computer simulations related to the selection and optimization of the parameters of robotic packing process of one type of product. Taking the required performance of the robotic production line as a basis, we proposed its configuration using [...] Read more.

The article presents the results of computer simulations related to the selection and optimization of the parameters of robotic packing process of one type of product. Taking the required performance of the robotic production line as a basis, we proposed its configuration using the RobotStudio environment for offline robot programming and virtual controller technology. Next, a methodology for the validation of the adopted assumptions was developed, based on a wide range of input data and a precise representation of the applicable conditions in the packaging process of one type of product. This methodology included test scenarios repeated an appropriate number of times in order to obtain the result data with the desired reliability and repeatability. The main element of the research was a computer simulation of the station based on the Picking PowerPac package. It was assumed that the products on the technological line are generated pseudo-randomly, thus reflecting the real working conditions. The result of the conducted works is the optimal operating speed of industrial robots and conveyors. The developed methodology allows for multifaceted analyses of the key parameters of the technological process (e.g., the number of active robots and their load, speed of conveyors, and station efficiency). We paid special attention to the occurrence of anomalies, i.e., emergency situations in the form of “halting” the operation of chosen robots and their impact on the obtained quality of the industrial process. As a result of the simulations, numerical values were obtained, maximum efficiency, with regard to maximum overflow of items of 5%, for LB algorithm was equal to 1188 completed containers per hour, with conveyors speeds of 270 mm/s and 165 mm/s. This efficiency was possible at robot speeds R1 = 6450 mm/s, R2 = 7500 mm/s, R3 = 6500 mm/s, R4 = 6375 mm/s, R5 = 5500 mm/s, R6 = 7200 mm/s. The ATC algorithm reached efficiency of 1332 containers per hour with less than 5% overflown items, with conveyor speeds of 310 mm/s and 185 mm/s. This efficiency was possible at robot speeds R1 = 7500 mm/s, R2 = 7500 mm/s, R3 = 7200 mm/s, R4 = 7000 mm/s, R5 = 6450 mm/s, R6 = 6300 mm/s. Tests carried out for emergency situations showed that the LB algorithm does not allow for automatic continuation of the process, while the ATC algorithm assured production efficiency of 94% to 98% of the maximum station efficiency. Full article

(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring)

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15 pages, 2736 KiB

Open AccessArticle

Electric Current Waveform of the Injector as a Source of Diagnostic Information

by Krzysztof Więcławski, Jędrzej Mączak and Krzysztof Szczurowski

Sensors 2020, 20(15), 4151; https://doi.org/10.3390/s20154151 - 26 Jul 2020

Cited by 10 | Viewed by 3983

Abstract

The article discusses the method of evaluation of the fuel injector operation based on the observation of the electric current parameters, which were measured with a current transducer using the Hall effect, during the dosing process. This method relies on comparison of the [...] Read more.

The article discusses the method of evaluation of the fuel injector operation based on the observation of the electric current parameters, which were measured with a current transducer using the Hall effect, during the dosing process. This method relies on comparison of the electric current-related values of the examined injector with the model characteristics, which are representing the properly functioning injector. A model of the fuel injector in the form of the electric current waveform that describes the changes in the electric current and voltage during its work is presented in this article. Complex equations describing the fuel injector model under discussion account for the characteristics of the current variations, with no damage-induced modifications. Due to these, the modeled electric current/voltage waveform mirrors the real conditions. The use of a mathematical model describing the voltage–current phenomena occurring during the injector operation allows determining the actual beginning and duration of the injection. The model can also be used to develop new injector diagnostic methods that can be implemented in the engine controller (ECU). Full article

(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring)

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16 pages, 4431 KiB

Open AccessArticle

Automated Calibration System for Digital Multimeters Not Equipped with a Communication Interface

by Grzegorz Grzeczka and Maciej Klebba

Sensors 2020, 20(13), 3650; https://doi.org/10.3390/s20133650 - 29 Jun 2020

Cited by 4 | Viewed by 3909

Abstract

This article is focused on the calibration of digital multimeters, in which the concept and practical solutions for stations with software for automatic calibration are presented. This paper also presents the general structure of the measuring system, the application scheme, and the technical [...] Read more.

This article is focused on the calibration of digital multimeters, in which the concept and practical solutions for stations with software for automatic calibration are presented. This paper also presents the general structure of the measuring system, the application scheme, and the technical implementation of measuring stations, together with the software. Full article

(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring)

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18 pages, 6072 KiB

Open AccessArticle

Research on the Single-Value Indicators for Centrifugal Pump Based on Vibration Signals

by Yin Luo, Yuejiang Han, Shouqi Yuan and Jianping Yuan

Sensors 2020, 20(11), 3283; https://doi.org/10.3390/s20113283 - 9 Jun 2020

Cited by 3 | Viewed by 3264

Abstract

Off-design operation conditions might not only seriously affect the internal flow status of a centrifugal pump, but also result in additional energy loss and potential mechanical damage. Therefore, early-stage monitoring and predication on off-design operation conditions for centrifugal pumps have become essential. Single-value [...] Read more.

Off-design operation conditions might not only seriously affect the internal flow status of a centrifugal pump, but also result in additional energy loss and potential mechanical damage. Therefore, early-stage monitoring and predication on off-design operation conditions for centrifugal pumps have become essential. Single-value indicators have favorable factors such as a smaller amount of calculation and easier identification. As a result, industries prefer the more straightforward approach: obtaining single-value indicators directly from the signals which could be easier compared with accepted standards. The possibility of applying the single-value indicators of vibration into operation condition monitoring for a centrifugal pump is studied theoretically and experimentally, which shows that the statistical features of vibration might be suitable for hydraulic instability detection for a centrifugal pump. Full article

(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring)

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18 pages, 8896 KiB

Open AccessArticle

A Simple Condition Monitoring Method for Gearboxes Operating in Impulsive Environments

by Stephan Schmidt, Radoslaw Zimroz, Fakher Chaari, P. Stephan Heyns and Mohamed Haddar

Sensors 2020, 20(7), 2115; https://doi.org/10.3390/s20072115 - 9 Apr 2020

Cited by 24 | Viewed by 3863

Abstract

Reliable condition indicators are necessary to perform effective diagnosis and prognosis. However, the vibration signals are often corrupted with non-Gaussian noise and rotating machines may operate under time-varying operating conditions. This impedes the application of conventional condition indicators. The synchronous average of the [...] Read more.

Reliable condition indicators are necessary to perform effective diagnosis and prognosis. However, the vibration signals are often corrupted with non-Gaussian noise and rotating machines may operate under time-varying operating conditions. This impedes the application of conventional condition indicators. The synchronous average of the squared envelope is a relatively simple yet effective method to perform fault detection, fault identification and fault trending under constant and time-varying operating conditions. However, its performance is impeded by the presence of impulsive signal components attributed to impulsive noise or the presence of other damage modes in the machine. In this work, it is proposed that the synchronous median of the squared envelope should be used instead of the synchronous average of the squared envelope for gearbox fault diagnosis. It is shown on numerical and experimental datasets that the synchronous median is more robust to the presence of impulsive signal components and is therefore more reliable for estimating the condition of specific machine components. Full article

(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring)

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23 pages, 8221 KiB

Open AccessArticle

Multi-objective Informative Frequency Band Selection Based on Negentropy-induced Grey Wolf Optimizer for Fault Diagnosis of Rolling Element Bearings

by Xiaohui Gu, Shaopu Yang, Yongqiang Liu, Rujiang Hao and Zechao Liu

Sensors 2020, 20(7), 1845; https://doi.org/10.3390/s20071845 - 26 Mar 2020

Cited by 16 | Viewed by 3998

Abstract

Informative frequency band (IFB) selection is a challenging task in envelope analysis for the localized fault detection of rolling element bearings. In previous studies, it was often conducted with a single indicator, such as kurtosis, etc., to guide the automatic selection. However, in [...] Read more.

Informative frequency band (IFB) selection is a challenging task in envelope analysis for the localized fault detection of rolling element bearings. In previous studies, it was often conducted with a single indicator, such as kurtosis, etc., to guide the automatic selection. However, in some cases, it is difficult for that to fully depict and balance the fault characters from impulsiveness and cyclostationarity of the repetitive transients. To solve this problem, a novel negentropy-induced multi-objective optimized wavelet filter is proposed in this paper. The wavelet parameters are determined by a grey wolf optimizer with two independent objective functions i.e., maximizing the negentropy of squared envelope and squared envelope spectrum to capture impulsiveness and cyclostationarity, respectively. Subsequently, the average negentropy is utilized in identifying the IFB from the obtained Pareto set, which are non-dominated by other solutions to balance the impulsive and cyclostationary features and eliminate the background noise. Two cases of real vibration signals with slight bearing faults are applied in order to evaluate the performance of the proposed methodology, and the results demonstrate its effectiveness over some fast and optimal filtering methods. In addition, its stability in tracking the IFB is also tested by a case of condition monitoring data sets. Full article

(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring)

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Review

Jump to: Research, Other

41 pages, 6848 KiB

Open AccessReview

A Critical Review of Nonlinear Damping Identification in Structural Dynamics: Methods, Applications, and Challenges

by Tareq Al-hababi, Maosen Cao, Bassiouny Saleh, Nizar Faisal Alkayem and Hao Xu

Sensors 2020, 20(24), 7303; https://doi.org/10.3390/s20247303 - 19 Dec 2020

Cited by 28 | Viewed by 8709

Abstract

In recent decades, nonlinear damping identification (NDI) in structural dynamics has attracted wide research interests and intensive studies. Different NDI strategies, from conventional to more advanced, have been developed for a variety of structural types. With apparent advantages over classical linear methods, these [...] Read more.

In recent decades, nonlinear damping identification (NDI) in structural dynamics has attracted wide research interests and intensive studies. Different NDI strategies, from conventional to more advanced, have been developed for a variety of structural types. With apparent advantages over classical linear methods, these strategies are able to quantify the nonlinear damping characteristics, providing powerful tools for the analysis and design of complex engineering structures. Since the current trend in many applications tends to more advanced and sophisticated applications, it is of great necessity to work on developing these methods to keep pace with this progress. Moreover, NDI can provide an effective and promising tool for structural damage detection purposes, where the changes in the dynamic features of structures can be correlated with damage levels. This review paper provides an overview of NDI methods by explaining the fundamental challenges and potentials of these methods based on the available literature. Furthermore, this research offers a comprehensive survey of different applications and future research trends of NDI. For potential development and application work for nonlinear damping methods, the anticipated results and recommendations of the current paper can assist researchers and developers worldwide to find out the gaps and unsolved issues in the field of NDI. Full article

(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring)

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Other

Jump to: Research, Review

16 pages, 1807 KiB

Open AccessLetter

Mechanical Fault Diagnostic in PMSM from Only One Current Measurement: A Tacholess Order Tracking Approach

by Abdallah Allouche, Erik Etien, Laurent Rambault, Thierry Doget, Sebastien Cauet and Anas Sakout

Sensors 2020, 20(17), 5011; https://doi.org/10.3390/s20175011 - 3 Sep 2020

Cited by 8 | Viewed by 2861

Abstract

This article presents a mechanical fault diagnosis methodology in synchronous machines using only a single current measurement in variable speed conditions. The proposed methodology uses order tracking in order to sample the analysis signal as a function of the rotor angle. The spectrum [...] Read more.

This article presents a mechanical fault diagnosis methodology in synchronous machines using only a single current measurement in variable speed conditions. The proposed methodology uses order tracking in order to sample the analysis signal as a function of the rotor angle. The spectrum of the signal is then independent of speed and it could be employed in frequency analysis. Order tracking is usually applied using rotor position measurement. In this work, the proposed method uses one current measurement to estimate the position as well as the analysis signal (rotation speed). Furthermore, a statistical approach is used to create a complete diagnosis protocol. At variable speed and with only one current measurement the diagnosis is challenging. However, order tracking will allow simpler analysis. The method is proved in simulations and experimental set-up. Full article

(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring)

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13 pages, 2159 KiB

Open AccessLetter

Chaotic Ensemble of Online Recurrent Extreme Learning Machine for Temperature Prediction of Control Moment Gyroscopes

by Luhang Liu, Qiang Zhang, Dazhong Wei, Gang Li, Hao Wu, Zhipeng Wang, Baozhu Guo and Jiyang Zhang

Sensors 2020, 20(17), 4786; https://doi.org/10.3390/s20174786 - 25 Aug 2020

Cited by 5 | Viewed by 2248

Abstract

Control moment gyroscopes (CMG) are crucial components in spacecrafts. Since the anomaly of bearing temperature of the CMG shows apparent correlation with nearly all critical fault modes, temperature prediction is of great importance for health management of CMGs. However, due to the complicity [...] Read more.

Control moment gyroscopes (CMG) are crucial components in spacecrafts. Since the anomaly of bearing temperature of the CMG shows apparent correlation with nearly all critical fault modes, temperature prediction is of great importance for health management of CMGs. However, due to the complicity of thermal environment on orbit, the temperature signal of the CMG has strong intrinsic nonlinearity and chaotic characteristics. Therefore, it is crucial to study temperature prediction under the framework of chaos time series theory. There are also several other challenges including poor data quality, large individual differences and difficulty in processing streaming data. To overcome these issues, we propose a new method named Chaotic Ensemble of Online Recurrent Extreme Learning Machine (CE-ORELM) for temperature prediction of control moment gyroscopes. By means of the CE-ORELM model, this proposed method is capable of dynamic prediction of temperature. The performance of the method was tested by real temperature data acquired from actual CMGs. Experimental results show that this method has high prediction accuracy and strong adaptability to the on-orbital temperature data with sudden variations. These superiorities indicate that the proposed method can be used for temperature prediction of control moment gyroscopes. Full article

(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring)

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12 pages, 3072 KiB

Open AccessLetter

A Novel Approach to Condition Monitoring of the Cutting Process Using Recurrent Neural Networks

by Rui Silva and António Araújo

Sensors 2020, 20(16), 4493; https://doi.org/10.3390/s20164493 - 11 Aug 2020

Cited by 14 | Viewed by 2629

Abstract

Condition monitoring is a fundamental part of machining, as well as other manufacturing processes where, generally, there are parts that wear out and have to be replaced. Devising proper condition monitoring has been a concern of many researchers, but there is still a [...] Read more.

Condition monitoring is a fundamental part of machining, as well as other manufacturing processes where, generally, there are parts that wear out and have to be replaced. Devising proper condition monitoring has been a concern of many researchers, but there is still a lack of robustness and efficiency, most often hindered by the system’s complexity or otherwise limited by the inherent noisy signals, a characteristic of industrial processes. The vast majority of condition monitoring approaches do not take into account the temporal sequence when modelling and hence lose an intrinsic part of the context of an actual time-dependent process, fundamental to processes such as cutting. The proposed system uses a multisensory approach to gather information from the cutting process, which is then modelled by a recurrent neural network, capturing the evolutive pattern of wear over time. The system was tested with realistic cutting conditions, and the results show great effectiveness and accuracy with just a few cutting tests. The use of recurrent neural networks demonstrates the potential of such an approach for other time-dependent industrial processes under noisy conditions. Full article

(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring)

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11 pages, 4297 KiB

Open AccessLetter

Object-Based Thermal Image Segmentation for Fault Diagnosis of Reciprocating Compressors

by Rongfeng Deng, Yubin Lin, Weijie Tang, Fengshou Gu and Andrew Ball

Sensors 2020, 20(12), 3436; https://doi.org/10.3390/s20123436 - 18 Jun 2020

Cited by 20 | Viewed by 3494

Abstract

As an essential mechanical device in many industrial applications, reciprocating compressors have a high demand for operating efficiency and availability. Because the temperature of each part of a reciprocating compressor depends considerably on operating conditions, faults in any parts will cause the variation [...] Read more.

As an essential mechanical device in many industrial applications, reciprocating compressors have a high demand for operating efficiency and availability. Because the temperature of each part of a reciprocating compressor depends considerably on operating conditions, faults in any parts will cause the variation of the temperature distribution, which provides the possibility to distinguish the fault type of reciprocating compressors by differentiating the distribution using infrared thermal imaging. In this paper, three types of common fault are laboratory experimented in an uncontrolled temperature environment. The temperature distribution signals of a reciprocating compressor are captured by a non-contact infrared camera remotely in the form of heat maps during the experimental process. Based on the temperature distribution under baseline condition, temperature fields of six main components were selected via Hue-Saturation-Value (HSV) image as diagnostic features. During the experiment, the average grayscale values of each component were calculated to form 6-dimension vectors to represent the variation of the temperature distribution. A computational efficient multiclass support vector machine (SVM) model is then used for classifying the differences of the distributions, and the classification results demonstrate that the average temperatures of six main components aided by SVM is a promising technique to diagnose the faults of reciprocating compressors under various operating conditions with a classification accuracy of more than 99%. Full article

(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring)

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