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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (20 December 2023) | Viewed by 14644

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Prof. Dr. Grover Zurita Villarroel

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Guest Editor

Lab. De Innovation Tecnologica Industrial y Robotica (LITIR), Universidad Privada Boliviana (UPB), Cochabamba, Bolivia, Sweden
Interests: vvibration analysis; machine diagnostic; artificial intelligence; modal analysis; digital signal processing; noise vibration hardness
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Örjan Johansson

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Guest Editor

Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology, Luleå, Sweden
Interests: architectural & building acoustics; sound & vibration quality; ultrasound & acoustic cavitation

Special Issue Information

Dear Colleagues,

The main scope of this special issue, in machine diagnostics and vibration analysis, is to gather state-of-the-art of the research adding latest scientific advancements in the field. The presented papers are expected to make demonstrable original contributions to scientific world.

The difficulties, usually, are the failures that initiate unplanned machinery stops. In this context, it is necessary to predict possible failures to anticipate them and to be able to plan a predictive machine maintenance, “just before it fails”. The benefices of a proper condition monitoring are: Increasing lifespan of machinery, maximizing production output, and lowering maintenance cost.

Some of the most promising approaches for the continuous advancement of machine diagnosis and vibration analysis are: Next-generation active vibration control systems, machinery diagnostics and prognostics using intelligent analysis, signal processing in machine health monitoring, vibration-based condition monitoring, modal and operational mode analysis, neural networks analysis, and machine learning.

Finally, vibration analysis for machine diagnosis based on machine learning has become one of the most efficient tool, with high accuracy, precision automated learning, robustness, and the capacity to handle complex data are some of the attributes.

Prof. Dr. Grover Zurita Villarroel
Prof. Dr. Örjan Johansson
Guest Editors

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Keywords

next-generation active vibration control systems
machinery diagnostics and prognostics using intelligent analysis
signal processing in machine health monitoring
vibration analysis and condition based maintenance
machine diagnosis by using modal and operational modal analysis
finite element analysis for structure resonances
vibration-acoustic based structural health monitoring
neural networks methods for machine diagnosis
artificial intelligence for machine diagnosis
deep learning techniques for fault detection and diagnosis
development of vibration measurement systems for machine diagnosis

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Published Papers (6 papers)

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Research

23 pages, 7677 KiB

Open AccessArticle

Prediction of Vertical Vibrations of a CNC Router Type Geometry

by Carlos Renato Vázquez and Alejandro Guajardo-Cuéllar

Appl. Sci. 2024, 14(2), 621; https://doi.org/10.3390/app14020621 - 11 Jan 2024

Viewed by 1497

Abstract

Mechanical vibrations represent an important problem in machining processes performed by machine tools. They affect surface quality, tool life, and productivity. In extreme situations, chattering may appear, which can dramatically reduce the tool life. CNC router machines are particularly sensitive to vibrations, with their structure bearing resemblance to a composition of beams that are uniform in cross-section. These CNC machines are commonly used for different tasks, like engraving, cutting, and 3D printing. This work proposes a modeling methodology for vibration systems that consist of coupled thin beams subjected to vertical vibration. This methodology is used to model vertical vibrations in a CNC router machine. For this, the geometry is decomposed into beams of uniform cross-sections that are coupled at their ends. Each beam is modeled by means of the classical theory of Bernoulli–Euler for thin beams. The boundary conditions are determined by the beam couplings. In the system thus defined, fundamental frequencies are calculated using the bisection method, and then the modes are computed for the corresponding frequencies. The modal amplitudes, being time-dependent, are modeled as a state space system, considering the first m frequencies. In order to provide support to the modeling methodology, simulation experiments are performed for validation, comparing the results provided by models built with the proposed methodology against finite element models and an experimental setting with a real structure. Moreover, an analysis of the vibration model focusing on a specific component of the equipment is presented to illustrate the usefulness and flexibility of the models obtained with the proposed methodology. Full article

(This article belongs to the Special Issue Machine Diagnostics and Vibration Analysis)

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19 pages, 2850 KiB

Open AccessArticle

Hot Strip Mill Gearbox Monitoring and Diagnosis Based on Convolutional Neural Networks Using the Pseudo-Labeling Method

by Myung-Kyo Seo and Won-Young Yun

Appl. Sci. 2024, 14(1), 450; https://doi.org/10.3390/app14010450 - 4 Jan 2024

Cited by 2 | Viewed by 1323

Abstract

The steel industry is typical process manufacturing, and the quality and cost of the products can be improved by efficient operation of equipment. This paper proposes an efficient diagnosis and monitoring method for the gearbox, which is a key piece of mechanical equipment in steel manufacturing. In particular, an equipment maintenance plan for stable operation is essential. Therefore, equipment monitoring and diagnosis to prevent unplanned plant shutdowns are important to operate the equipment efficiently and economically. Most plant data collected on-site have no precise information about equipment malfunctions. Therefore, it is difficult to directly apply supervised learning algorithms to diagnose and monitor the equipment with the operational data collected. The purpose of this paper is to propose a pseudo-label method to enable supervised learning for equipment data without labels. Pseudo-normal (PN) and pseudo-abnormal (PA) vibration datasets are defined and labeled to apply classification analysis algorithms to unlabeled equipment data. To find an anomalous state in the equipment based on vibration data, the initial PN vibration dataset is compared with a PA vibration dataset collected over time, and the equipment is monitored for potential failure. Continuous wavelet transform (CWT) is applied to the vibration signals collected to obtain an image dataset, which is then entered into a convolutional neural network (an image classifier) to determine classification accuracy and detect equipment abnormalities. As a result of Steps 1 to 4, abnormal signals have already been detected in the dataset, and alarms and warnings have already been generated. The classification accuracy was over 0.95 at

d = 4

, confirming quantitatively that the status of the equipment had changed significantly. In this way, a catastrophic failure can be avoided by performing a detailed equipment inspection in advance. Lastly, a catastrophic failure occurred in Step 9, and the classification accuracy ranged from 0.95 to 1.0. It was possible to prevent secondary equipment damage, such as motors connected to gearboxes, by identifying catastrophic failures promptly. This case study shows that the proposed procedure gives good results in detecting operation abnormalities of key unit equipment. In the conclusion, further promising topics are discussed. Full article

(This article belongs to the Special Issue Machine Diagnostics and Vibration Analysis)

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21 pages, 4537 KiB

Open AccessArticle

Vibration Energy at Damage-Based Statistical Approach to Detect Multiple Damages in Roller Bearings

by Xiaoqing Yuan, Naqash Azeem, Azka Khalid and Jahanzeb Jabbar

Appl. Sci. 2022, 12(17), 8541; https://doi.org/10.3390/app12178541 - 26 Aug 2022

Cited by 3 | Viewed by 1748

Abstract

This study proposes a statistical approach based on vibration energy at damage to detect multiple damages occurring in roller bearings. The analysis was performed at four different rotating speeds—1002, 1500, 2400, and 3000 RPM—following four different damages—inner race, outer race, ball, and combination damage—and under two types of loading conditions. These experiments were performed on a SpectraQuest Machinery Fault Simulator™ by acquiring the vibration data through accelerometers under two operating conditions: with the bearing loader on the rotor shaft and without the bearing loader on the rotor shaft. The histograms showed diversity in the defected bearing as compared to the intact bearing. There was a marked increase in the kurtosis values of each damaged roller bearing. This research article proposes that histograms, along with kurtosis values, represent changes in vibration energy at damage that can easily detect a damaged bearing. This study concluded that the vibration energy at damage-based statistical technique is an outstanding approach to detect damages in roller bearings, assisting Industry 4.0 to diagnose faults automatically. Full article

(This article belongs to the Special Issue Machine Diagnostics and Vibration Analysis)

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19 pages, 6634 KiB

Open AccessArticle

Random Vibration Fatigue Life Analysis of Airborne Electrical Control Box

by Daqian Zhang and Yueyang Chen

Appl. Sci. 2022, 12(14), 7335; https://doi.org/10.3390/app12147335 - 21 Jul 2022

Cited by 3 | Viewed by 3063

Abstract

To study the influence of random vibration on the fatigue life of airborne equipment, an aircraft electrical control box was selected as the research object. First, finite element software was used to model the dynamics of the airborne electrical control box to investigate its mode frequencies. The accuracy of finite element simulations was verified by performing mode experiments. Second, the mode superposition method was used to analyze the flight direction (X axis), side direction (Y axis), and altitude direction (Z axis) random vibration responses of the electrical control box. The analysis results were combined with the Miner linear cumulative damage criterion and the Gaussian-distribution Steinberg method to estimate the fatigue life of the electrical control box in the three directions. Finally, the calculation results were verified by performing the random vibration durability test on the electrical control box. The finite element mode analysis results show good agreement with the vibration experiment results, and the maximum error is 13.4%, indicating that the finite element model established in this paper is acceptable. The fatigue life of the electrical control box in the three axes meets the user requirements, and random vibration along the side direction (Y axis) has the greatest impact on the fatigue life, which is consistent with the results of the actual experimental data. The research method can be extended to predict the fatigue life of other airborne equipment and thus has practical significance for structural design and reliability analysis of airborne equipment. Full article

(This article belongs to the Special Issue Machine Diagnostics and Vibration Analysis)

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19 pages, 18356 KiB

Open AccessArticle

The Influence of Ribbing of Mechanical Crank Press Cast C-Frames over the Stress State in Critical Areas

by Cristian Pisarciuc, Ioan Dan and Romeo Cioară

Appl. Sci. 2022, 12(11), 5619; https://doi.org/10.3390/app12115619 - 1 Jun 2022

Cited by 3 | Viewed by 3434

Abstract

The research presented is directed to identify the ways to reduce stress state in critical areas of the C-frame of mechanical presses. Ribbing is a way to increase rigidity and a means to reduce energy consumption in operation. Hence, the objective is to design new frame models that would ensure that presented features are obtained. Starting from a reference model that was maintained as such, within the conducted study, an important part of the new solutions resulted by ribbing the lateral walls of the frame and determining three different distances between the ribs, as well as their various orientations. The finite element-FEM study of the resulted stress states was conducted for all devised models. FEA revealed that, in the new models with ribbed lateral walls, the maximum values of the stress have different evolutions for each of the three critical areas considered. In most new models, the maximum stress decreased by 2–5% in two of the critical areas and increased in the third. The study carried out allows the selection of the most performant of the new solutions and provides valuable information for application and further studies. Full article

(This article belongs to the Special Issue Machine Diagnostics and Vibration Analysis)

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13 pages, 3899 KiB

Open AccessArticle

Applications of Operational Modal Analysis in Gearbox and Induction Motor, Based on Random Decrement Technique and Enhanced Ibrahim Time Method

by Gabriel Castro and Grover Zurita

Appl. Sci. 2022, 12(10), 5284; https://doi.org/10.3390/app12105284 - 23 May 2022

Cited by 6 | Viewed by 2371

Abstract

There have been steadily growing requirements from the academia and industry, demanding non-invasive methods and reliable measurement systems of research devoted to operational mode analysis (OMA). Due to the simplicity of performing only structures surface vibration measurements, OMA is frequently applied in machine fault diagnosis (MFD) and structure health monitoring (SHM). OMA can handle big structures, such as bridges, buildings, machines, etc. However, there is still an open question: how to properly handle the harmonic effects of rotating components and the difficulty of closely estimating space modes are still a nightmare to deal with. Therefore, the main objective of this paper is to identify the structure of natural frequencies by the regeneration of frequency response functions (FRFs) for complex structures based on OMA. The novelty of our approach is to use the random decrement technique (RDT), correlation function estimation (CFE), and enhanced Ibrahim time method (EITM) to overcome OMA’s difficulties and limitations. To reduce further rotational harmonics effects, gear mesh and side band frequencies, digital signal processing techniques based on notching filters, and liftering analysis techniques were also used. All the experiments were performed at the laboratory test rig and conducted by using three accelerometers, one impedance hammer, one force sensor, and one data acquisition board. To reduce data’s variabilities, each test was measured three times for 5 min. The data sampling frequency for all the experiments was 25.6 kHz. To validate the proposed methodology, extensive OMA tests were performed for the generation of FRFs. The measured objects were a steel bar, induction motor, and gearbox. Five structural natural frequencies for the induction motor and eight structural natural frequencies for the gearbox were generated, respectively. Full article

(This article belongs to the Special Issue Machine Diagnostics and Vibration Analysis)

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