Sensors

Editorial

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2 pages, 154 KiB

Open AccessEditorial

Trends in Sensors Fault Diagnosis

by Piotr Witczak

Sensors 2021, 21(6), 2224; https://doi.org/10.3390/s21062224 - 23 Mar 2021

Viewed by 1717

Abstract

Recently, the automation of processes has been widely demanded [...] Full article

(This article belongs to the Special Issue Sensors Fault Diagnosis Trends and Applications)

Research

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21 pages, 4658 KiB

Open AccessArticle

Development of a Methodology Using Artificial Neural Network in the Detection and Diagnosis of Faults for Pneumatic Control Valves

by Ana Andrade, Kennedy Lopes, Bernardo Lima and André Maitelli

Sensors 2021, 21(3), 853; https://doi.org/10.3390/s21030853 - 27 Jan 2021

Cited by 24 | Viewed by 3847

Abstract

To satisfy the market, competition in the industrial sector aims for productivity and safety in industrial plant control systems. The appearance of a fault can compromise the system’s proper functioning process. Therefore, Fault Detection and Diagnosis (FDD) methods contribute to avoiding any undesired [...] Read more.

To satisfy the market, competition in the industrial sector aims for productivity and safety in industrial plant control systems. The appearance of a fault can compromise the system’s proper functioning process. Therefore, Fault Detection and Diagnosis (FDD) methods contribute to avoiding any undesired events, as there are techniques and methods that study the detection, isolation, identification and, consequently, fault diagnosis. In this work, a new methodology that uses faults emulation to obtain parameters similar to the Development and Application of Methods for Diagnosis of Actuators in Industrial Control Systems (DAMADICS) benchmark model will be developed. This methodology uses previous information from tests on sensors with and without faults to detect and classify the situation of the plant and, in the presence of faults, perform the diagnosis through a process of elimination in a hierarchical manner. In this way, the definition of residue signature is used as well as the creation of a decision tree. The whole process is carried out incorporating FDD techniques, through the Non-Linear Auto-Regressive Neural Network Model With Exogenous Inputs (NARX), in the diagnosis of the behavioral prediction of the signals to generate the residual values. Then, it is applied to the construction of the decision tree based on the most significant residue of a certain signal, enabling the process of acquisition and formation of the signature matrix. With the procedures in this article, it is possible to demonstrate a practical and systematic method of how to emulate faults for control valves and the possibility of carrying out an analysis of the data to acquire signatures of the fault behavior. Finally, simulations resulting from the most sensitized variables for the production of residuals that is generated by neural networks are presented, which are used to obtain signatures and isolate the flaws. The process proves to be efficient in computational time and makes it easy to present a fault diagnosis strategy that can be reproduced in other processes. Full article

(This article belongs to the Special Issue Sensors Fault Diagnosis Trends and Applications)

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23 pages, 3106 KiB

Open AccessArticle

Scan-Chain-Fault Diagnosis Using Regressions in Cryptographic Chips for Wireless Sensor Networks

by Hyunyul Lim, Minho Cheong and Sungho Kang

Sensors 2020, 20(17), 4771; https://doi.org/10.3390/s20174771 - 24 Aug 2020

Cited by 5 | Viewed by 3062

Abstract

Scan structures, which are widely used in cryptographic circuits for wireless sensor networks applications, are essential for testing very-large-scale integration (VLSI) circuits. Faults in cryptographic circuits can be effectively screened out by improving testability and test coverage using a scan structure. Additionally, scan [...] Read more.

Scan structures, which are widely used in cryptographic circuits for wireless sensor networks applications, are essential for testing very-large-scale integration (VLSI) circuits. Faults in cryptographic circuits can be effectively screened out by improving testability and test coverage using a scan structure. Additionally, scan testing contributes to yield improvement by identifying fault locations. However, faults in circuits cannot be tested when a fault occurs in the scan structure. Moreover, various defects occurring early in the manufacturing process are expressed as faults of scan chains. Therefore, scan-chain diagnosis is crucial. However, it is difficult to obtain a sufficiently high diagnosis resolution and accuracy through the conventional scan-chain diagnosis. Therefore, this article proposes a novel scan-chain diagnosis method using regression and fan-in and fan-out filters that require shorter training and diagnosis times than existing scan-chain diagnoses do. The fan-in and fan-out filters, generated using a circuit logic structure, can highlight important features and remove unnecessary features from raw failure vectors, thereby converting the raw failure vectors to fan-in and fan-out vectors without compromising the diagnosis accuracy. Experimental results confirm that the proposed scan-chain-diagnosis method can efficiently provide higher resolutions and accuracies with shorter training and diagnosis times. Full article

(This article belongs to the Special Issue Sensors Fault Diagnosis Trends and Applications)

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20 pages, 3650 KiB

Open AccessArticle

Fault-Tolerant Model Predictive Control Algorithm for Path Tracking of Autonomous Vehicle

by Keke Geng, Nikolai Alexandrovich Chulin and Ziwei Wang

Sensors 2020, 20(15), 4245; https://doi.org/10.3390/s20154245 - 30 Jul 2020

Cited by 15 | Viewed by 3686

Abstract

The fault detection and isolation are very important for the driving safety of autonomous vehicles. At present, scholars have conducted extensive research on model-based fault detection and isolation algorithms in vehicle systems, but few of them have been applied for path tracking control. [...] Read more.

The fault detection and isolation are very important for the driving safety of autonomous vehicles. At present, scholars have conducted extensive research on model-based fault detection and isolation algorithms in vehicle systems, but few of them have been applied for path tracking control. This paper determines the conditions for model establishment of a single-track 3-DOF vehicle dynamics model and then performs Taylor expansion for modeling linearization. On the basis of that, a novel fault-tolerant model predictive control algorithm (FTMPC) is proposed for robust path tracking control of autonomous vehicle. First, the linear time-varying model predictive control algorithm for lateral motion control of vehicle is designed by constructing the objective function and considering the front wheel declination and dynamic constraint of tire cornering. Then, the motion state information obtained by multi-sensory perception systems of vision, GPS, and LIDAR is fused by using an improved weighted fusion algorithm based on the output error variance. A novel fault signal detection algorithm based on Kalman filtering and Chi-square detector is also designed in our work. The output of the fault signal detector is a fault detection matrix. Finally, the fault signals are isolated by multiplication of signal matrix, fault detection matrix, and weight matrix in the process of data fusion. The effectiveness of the proposed method is validated with simulation experiment of lane changing path tracking control. The comparative analysis of simulation results shows that the proposed method can achieve the expected fault-tolerant performance and much better path tracking control performance in case of sensor failure. Full article

(This article belongs to the Special Issue Sensors Fault Diagnosis Trends and Applications)

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18 pages, 4020 KiB

Open AccessArticle

Towards Simultaneous Actuator and Sensor Faults Estimation for a Class of Takagi-Sugeno Fuzzy Systems: A Twin-Rotor System Application

by Marcin Pazera, Marcin Witczak, Norbert Kukurowski and Mariusz Buciakowski

Sensors 2020, 20(12), 3486; https://doi.org/10.3390/s20123486 - 19 Jun 2020

Cited by 11 | Viewed by 2696

Abstract

The paper is devoted to the problem of estimating simultaneously states, as well as actuator and sensor faults for Takagi–Sugeno systems. The proposed scheme is intended to cope with multiple sensor and actuator faults. To achieve such a goal, the original Takagi–Sugeno system [...] Read more.

The paper is devoted to the problem of estimating simultaneously states, as well as actuator and sensor faults for Takagi–Sugeno systems. The proposed scheme is intended to cope with multiple sensor and actuator faults. To achieve such a goal, the original Takagi–Sugeno system is transformed into a descriptor one containing all state and fault variables within an extended state vector. Moreover, to facilitate the overall design procedure an auxiliary fault vector is introduced. In comparison to the approaches proposed in the literature, a usual restrictive assumption concerning fixed fault rate of change is removed. Finally, the robust convergence of the whole observer is guaranteed by the so-called quadratic boundedness approach which assumes that process and measurement uncertainties are unknown but bounded within an ellipsoid. The last part of the paper portrays an exemplary application concerning a nonlinear twin-rotor system. Full article

(This article belongs to the Special Issue Sensors Fault Diagnosis Trends and Applications)

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25 pages, 11644 KiB

Open AccessArticle

A Reliable Fault Diagnosis Method for a Gearbox System with Varying Rotational Speeds

by Cong Dai Nguyen, Alexander Prosvirin and Jong-Myon Kim

Sensors 2020, 20(11), 3105; https://doi.org/10.3390/s20113105 - 31 May 2020

Cited by 15 | Viewed by 5232

Abstract

The vibration signals of gearbox gear fault signatures are informative components that can be used for gearbox fault diagnosis and early fault detection. However, the vibration signals are normally non-linear and non-stationary, and they contain background noise caused by data acquisition systems and [...] Read more.

The vibration signals of gearbox gear fault signatures are informative components that can be used for gearbox fault diagnosis and early fault detection. However, the vibration signals are normally non-linear and non-stationary, and they contain background noise caused by data acquisition systems and the interference of other machine elements. Especially in conditions with varying rotational speeds, the informative components are blended with complex, unwanted components inside the vibration signal. Thus, to use the informative components from a vibration signal for gearbox fault diagnosis, the noise needs to be properly distilled from the informational signal as much as possible before analysis. This paper proposes a novel gearbox fault diagnosis method based on an adaptive noise reducer–based Gaussian reference signal (ANR-GRS) technique that can significantly reduce noise and improve classification from a one-against-one, multiclass support vector machine (OAOMCSVM) for the fault types of a gearbox. The ANR-GRS processes the shaft rotation speed to access and remove noise components in the narrowbands between two consecutive sideband frequencies along the frequency spectrum of a vibration signal, enabling the removal of enormous noise components with minimal distortion to the informative signal. The optimal output signal from the ANR-GRS is then extracted into many signal feature vectors to generate a qualified classification dataset. Finally, the OAOMCSVM classifies the health states of an experimental gearbox using the dataset of extracted features. The signal processing and classification paths are generated using the experimental testbed. The results indicate that the proposed method is reliable for fault diagnosis in a varying rotational speed gearbox system. Full article

(This article belongs to the Special Issue Sensors Fault Diagnosis Trends and Applications)

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16 pages, 5016 KiB

Open AccessArticle

The Enhancement of Leak Detection Performance for Water Pipelines through the Renovation of Training Data

by Tu T.N. Luong and Jong-Myon Kim

Sensors 2020, 20(9), 2542; https://doi.org/10.3390/s20092542 - 29 Apr 2020

Cited by 10 | Viewed by 3306

Abstract

Leakage detection is a fundamental problem in water management. Its importance is expressed not only in avoiding resource wastage, but also in protecting the environment and the safety of water resources. Therefore, early leak detection is increasingly urged. This paper used an intelligent [...] Read more.

Leakage detection is a fundamental problem in water management. Its importance is expressed not only in avoiding resource wastage, but also in protecting the environment and the safety of water resources. Therefore, early leak detection is increasingly urged. This paper used an intelligent leak detection method based on a model using statistical parameters extracted from acoustic emission (AE) signals. Since leak signals depend on many operation conditions, the training data in real-life situations usually has a small size. To solve the problem of a small sample size, a data improving method based on enhancing the generalization ability of the data was proposed. To evaluate the effectiveness of the proposed method, this study used the datasets obtained from two artificial leak cases which were generated by pinholes with diameters of 0.3 mm and 0.2 mm. Experimental results show that the employment of the additional data improving block in the leak detection scheme enhances the quality of leak detection in both terms of accuracy and stability. Full article

(This article belongs to the Special Issue Sensors Fault Diagnosis Trends and Applications)

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20 pages, 5123 KiB

Open AccessArticle

A Novel Intelligent Fault Diagnosis Method for Rolling Bearing Based on Integrated Weight Strategy Features Learning

by Jun He, Ming Ouyang, Chen Yong, Danfeng Chen, Jing Guo and Yan Zhou

Sensors 2020, 20(6), 1774; https://doi.org/10.3390/s20061774 - 23 Mar 2020

Cited by 17 | Viewed by 3210

Abstract

Intelligent methods have long been researched in fault diagnosis. Traditionally, feature extraction and fault classification are separated, and this process is not completely intelligent. In addition, most traditional intelligent methods use an individual model, which cannot extract the discriminate features when the machines [...] Read more.

Intelligent methods have long been researched in fault diagnosis. Traditionally, feature extraction and fault classification are separated, and this process is not completely intelligent. In addition, most traditional intelligent methods use an individual model, which cannot extract the discriminate features when the machines work in a complex condition. To overcome the shortcomings of traditional intelligent fault diagnosis methods, in this paper, an intelligent bearing fault diagnosis method based on ensemble sparse auto-encoders was proposed. Three different sparse auto-encoders were used as the main architecture. To improve the robustness and stability, a novel weight strategy based on distance metric and standard deviation metric was employed to assign the weights of three sparse auto-encodes. Softmax classifier is used to classify the fault types of integrated features. The effectiveness of the proposed method is validated with extensive experiments, and comparisons with the related methods and researches on the widely-used motor bearing dataset verify the superiority of the proposed method. The results show that the testing accuracy and the standard deviation are 99.71% and 0.05%. Full article

(This article belongs to the Special Issue Sensors Fault Diagnosis Trends and Applications)

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21 pages, 7435 KiB

Open AccessArticle

Learning Attention Representation with a Multi-Scale CNN for Gear Fault Diagnosis under Different Working Conditions

by Yong Yao, Sen Zhang, Suixian Yang and Gui Gui

Sensors 2020, 20(4), 1233; https://doi.org/10.3390/s20041233 - 24 Feb 2020

Cited by 78 | Viewed by 6139

Abstract

The gear fault signal under different working conditions is non-linear and non-stationary, which makes it difficult to distinguish faulty signals from normal signals. Currently, gear fault diagnosis under different working conditions is mainly based on vibration signals. However, vibration signal acquisition is limited [...] Read more.

The gear fault signal under different working conditions is non-linear and non-stationary, which makes it difficult to distinguish faulty signals from normal signals. Currently, gear fault diagnosis under different working conditions is mainly based on vibration signals. However, vibration signal acquisition is limited by its requirement for contact measurement, while vibration signal analysis methods relies heavily on diagnostic expertise and prior knowledge of signal processing technology. To solve this problem, a novel acoustic-based diagnosis (ABD) method for gear fault diagnosis under different working conditions based on a multi-scale convolutional learning structure and attention mechanism is proposed in this paper. The multi-scale convolutional learning structure was designed to automatically mine multiple scale features using different filter banks from raw acoustic signals. Subsequently, the novel attention mechanism, which was based on a multi-scale convolutional learning structure, was established to adaptively allow the multi-scale network to focus on relevant fault pattern information under different working conditions. Finally, a stacked convolutional neural network (CNN) model was proposed to detect the fault mode of gears. The experimental results show that our method achieved much better performance in acoustic based gear fault diagnosis under different working conditions compared with a standard CNN model (without an attention mechanism), an end-to-end CNN model based on time and frequency domain signals, and other traditional fault diagnosis methods involving feature engineering. Full article

(This article belongs to the Special Issue Sensors Fault Diagnosis Trends and Applications)

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18 pages, 12965 KiB

Open AccessArticle

HKF-SVR Optimized by Krill Herd Algorithm for Coaxial Bearings Performance Degradation Prediction

by Fang Liu, Liubin Li, Yongbin Liu, Zheng Cao, Hui Yang and Siliang Lu

Sensors 2020, 20(3), 660; https://doi.org/10.3390/s20030660 - 24 Jan 2020

Cited by 16 | Viewed by 2831

Abstract

In real industrial applications, bearings in pairs or even more are often mounted on the same shaft. So the collected vibration signal is actually a mixed signal from multiple bearings. In this study, a method based on Hybrid Kernel Function-Support Vector Regression (HKF–SVR) [...] Read more.

In real industrial applications, bearings in pairs or even more are often mounted on the same shaft. So the collected vibration signal is actually a mixed signal from multiple bearings. In this study, a method based on Hybrid Kernel Function-Support Vector Regression (HKF–SVR) whose parameters are optimized by Krill Herd (KH) algorithm was introduced for bearing performance degradation prediction in this situation. First, multi-domain statistical features are extracted from the bearing vibration signals and then fused into sensitive features using Kernel Joint Approximate Diagonalization of Eigen-matrices (KJADE) algorithm which is developed recently by our group. Due to the nonlinear mapping capability of the kernel method and the blind source separation ability of the JADE algorithm, the KJADE could extract latent source features that accurately reflecting the performance degradation from the mixed vibration signal. Then, the between-class and within-class scatters (SS) of the health-stage data sample and the current monitored data sample is calculated as the performance degradation index. Second, the parameters of the HKF–SVR are optimized by the KH (Krill Herd) algorithm to obtain the optimal performance degradation prediction model. Finally, the performance degradation trend of the bearing is predicted using the optimized HKF–SVR. Compared with the traditional methods of Back Propagation Neural Network (BPNN), Extreme Learning Machine (ELM) and traditional SVR, the results show that the proposed method has a better performance. The proposed method has a good application prospect in life prediction of coaxial bearings. Full article

(This article belongs to the Special Issue Sensors Fault Diagnosis Trends and Applications)

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Review

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21 pages, 958 KiB

Open AccessReview

Fault Detection, Isolation, Identification and Recovery (FDIIR) Methods for Automotive Perception Sensors Including a Detailed Literature Survey for Lidar

by Thomas Goelles, Birgit Schlager and Stefan Muckenhuber

Sensors 2020, 20(13), 3662; https://doi.org/10.3390/s20133662 - 30 Jun 2020

Cited by 39 | Viewed by 10450

Abstract

Perception sensors such as camera, radar, and lidar have gained considerable popularity in the automotive industry in recent years. In order to reach the next step towards automated driving it is necessary to implement fault diagnosis systems together with suitable mitigation solutions in [...] Read more.

Perception sensors such as camera, radar, and lidar have gained considerable popularity in the automotive industry in recent years. In order to reach the next step towards automated driving it is necessary to implement fault diagnosis systems together with suitable mitigation solutions in automotive perception sensors. This is a crucial prerequisite, since the quality of an automated driving function strongly depends on the reliability of the perception data, especially under adverse conditions. This publication presents a systematic review on faults and suitable detection and recovery methods for automotive perception sensors and suggests a corresponding classification schema. A systematic literature analysis has been performed with focus on lidar in order to review the state-of-the-art and identify promising research opportunities. Faults related to adverse weather conditions have been studied the most, but often without providing suitable recovery methods. Issues related to sensor attachment and mechanical damage of the sensor cover were studied very little and provide opportunities for future research. Algorithms, which use the data stream of a single sensor, proofed to be a viable solution for both fault detection and recovery. Full article

(This article belongs to the Special Issue Sensors Fault Diagnosis Trends and Applications)

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Other

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18 pages, 881 KiB

Open AccessLetter

Sensor Fault-Tolerant Control Design for Magnetic Brake System

by Krzysztof Patan, Maciej Patan and Kamil Klimkowicz

Sensors 2020, 20(16), 4598; https://doi.org/10.3390/s20164598 - 16 Aug 2020

Cited by 6 | Viewed by 2996

Abstract

The purpose of the paper is to develop an efficient approach to fault-tolerant control for nonlinear systems of magnetic brakes. The challenging problems of accurate modeling, reliable fault detection and a control design able to compensate for potential sensor faults are addressed. The [...] Read more.

The purpose of the paper is to develop an efficient approach to fault-tolerant control for nonlinear systems of magnetic brakes. The challenging problems of accurate modeling, reliable fault detection and a control design able to compensate for potential sensor faults are addressed. The main idea here is to make use of the repetitive character of the control task and apply iterative learning control based on the observational data to accurately tune the system models for different states of the system. The proposed control scheme uses a learning controller built on a mixture of neural networks that estimate system responses for various operating points; it is then able to adapt to changing working conditions of the device. Then, using the tracking error norm as a sufficient statistic for detection of sensor fault, a simple thresholding technique is provided for verification of the hypothesis on abnormal sensor states. This also makes it possible to start the reconstruction of faulty sensor signals to properly compensate for the control of the system. The paper highlights the components of the complete iterative learning procedure including the system identification, fault detection and fault-tolerant control. Additionally, a series of experiments was conducted for the developed control strategy applied to a magnetic brake system to track the desired reference with the acceptable accuracy level, taking into account various fault scenarios. Full article

(This article belongs to the Special Issue Sensors Fault Diagnosis Trends and Applications)

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