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Advanced Sensor Fault Detection and Diagnosis Approaches

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".

Deadline for manuscript submissions: closed (20 May 2023) | Viewed by 8276

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Prof. Dr. Shunyi Zhao

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Guest Editor

Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Institute of Automation, Jiangnan University, Wuxi 214122, China
Interests: fault detection and diagnosis; Bayesian estimation; state estimation; statistical signal processing

Special Issue Information

Dear Colleagues,

Although hardware measurement equipment and soft sensing technologies have become more and more precise with the development of science and technology, we will inevitably face latent failures in sensors and measurement errors brought by these soft sensing technologies. With the aim to prevent the faults from damaging the stable industrial processes, many scholars have proposed a number of methodologies for fault detection and diagnosis, the target of which is to evaluate whether potential faults occur or not and to estimate the magnitude, location, and type of the failures.

This Special Issue will focus on all types of methods designed for sensor fault detection and diagnosis.

Prof. Dr. Shunyi Zhao
Guest Editor

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Published Papers (6 papers)

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Research

17 pages, 802 KiB

Open AccessArticle

Intelligent Modeling for Batch Polymerization Reactors with Unknown Inputs

by Zhuangyu Liu and Xiaoli Luan

Sensors 2023, 23(13), 6021; https://doi.org/10.3390/s23136021 - 29 Jun 2023

Cited by 1 | Viewed by 995

Abstract

While system identification methods have developed rapidly, modeling the process of batch polymerization reactors still poses challenges. Therefore, designing an intelligent modeling approach for these reactors is important. This paper focuses on identifying actual models for batch polymerization reactors, proposing a novel recursive approach based on the expectation-maximization algorithm. The proposed method pays special attention to unknown inputs (UIs), which may represent modeling errors or process faults. To estimate the UIs of the model, the recursive expectation-maximization (EM) technique is used. The proposed algorithm consists of two steps: the E-step and the M-step. In the E-step, a Q-function is recursively computed based on the maximum likelihood framework, using the UI estimates from the previous time step. The Kalman filter is utilized to calculate the estimates of the states using the measurements from sensor data. In the M-step, analytical solutions for the UIs are found through local optimization of the recursive Q-function. To demonstrate the effectiveness of the proposed algorithm, a practical application of modeling batch polymerization reactors is presented. The performance of the proposed recursive EM algorithm is compared to that of the augmented state Kalman filter (ASKF) using root mean squared errors (RMSEs). The RMSEs obtained from the proposed method are at least

6.52 %

lower than those from the ASKF method, indicating superior performance. Full article

(This article belongs to the Special Issue Advanced Sensor Fault Detection and Diagnosis Approaches)

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10 pages, 1943 KiB

Open AccessArticle

Applicability Evaluation of Surface and Sub-Surface Defects for Railway Wheel Material Using Induced Alternating Current Potential Drops

by Seok-Jin Kwon, Jung-Won Seo, Min-Soo Kim and Young-Sam Ham

Sensors 2022, 22(24), 9981; https://doi.org/10.3390/s22249981 - 18 Dec 2022

Cited by 1 | Viewed by 1467

Abstract

The majority of catastrophic wheelset failures are caused by surface opening fatigue cracks in either the wheel tread or wheel inner. Since failures in railway wheelsets can cause disasters, regular inspections to check for defects in wheels and axles are mandatory. Currently, ultrasonic testing, acoustic emissions, and the eddy current testing method are regularly used to check railway wheelsets in service. Yet, in many cases, despite surface and subsurface defects of the railroad wheels developing, the defects are not clearly detected by the conventional non-destructive inspection system. In the present study, a new technique was applied to the detection of surface and subsurface defects in railway wheel material. The results indicate that the technique can detect surface and subsurface defects of railway wheel specimens using the distribution of the alternating current (AC) electromagnetic field. In the wheelset cases presented, surface cracks with depths of 0.5 mm could be detected using this method. Full article

(This article belongs to the Special Issue Advanced Sensor Fault Detection and Diagnosis Approaches)

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18 pages, 3705 KiB

Open AccessArticle

A Fast Sparse Decomposition Based on the Teager Energy Operator in Extraction of Weak Fault Signals

by Baokang Yan, Zhiqian Li, Fengqi Zhou, Xu Lv and Fengxing Zhou

Sensors 2022, 22(20), 7973; https://doi.org/10.3390/s22207973 - 19 Oct 2022

Cited by 1 | Viewed by 1158

Abstract

In order to diagnose an incipient fault in rotating machinery under complicated conditions, a fast sparse decomposition based on the Teager energy operator (TEO) is proposed in this paper. In this proposed method, firstly, the TEO is employed to enhance the envelope of the impulses, which is more sensitive to frequency and can eliminate the low-frequency harmonic component and noise; secondly, a smoothing filtering algorithm was adopted to suppress the noise in the TEO envelope; thirdly, the fault signal was reconstructed by multiplication of the filtered TEO envelope and the original fault signal; finally, sparse decomposition was used based on a generalized S-transform (GST) to obtain the sparse representation of the signal. The proposed preprocessing method using the filtered TEO can overcome the interference of high-frequency noise while maintaining the structure of fault impulses, which helps the processed signal perform better on sparse decomposition; sparse decomposition based on GST was used to represent the fault signal more quickly and more accurately. Simulation and application prove that the proposed method has good accuracy and efficiency, especially in conditions of very low SNR, such as impulses with anSNR of −8.75 dB that are submerged by noise of the same amplitude. Full article

(This article belongs to the Special Issue Advanced Sensor Fault Detection and Diagnosis Approaches)

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10 pages, 1271 KiB

Open AccessArticle

Sorting Center Value Identification of “Internet + Recycling” Based on Transfer Clustering

by Cheng Cheng and Xiaoli Luan

Sensors 2022, 22(19), 7629; https://doi.org/10.3390/s22197629 - 8 Oct 2022

Viewed by 1227

Abstract

As the core link of the “Internet + Recycling” process, the value identification of the sorting center is a great challenge due to its small and imbalanced data set. This paper utilizes transfer fuzzy c-means to improve the value assessment accuracy of the sorting center by transferring the knowledge of customers clustering. To ensure the transfer effect, an inter-class balanced data selection method is proposed to select a balanced and more qualified subset of the source domain. Furthermore, an improved RFM (Recency, Frequency, and Monetary) model, named GFMR (Gap, Frequency, Monetary, and Repeat), has been presented to attain a more reasonable attribute description for sorting centers and consumers. The application in the field of electronic waste recycling shows the effectiveness and advantages of the proposed method. Full article

(This article belongs to the Special Issue Advanced Sensor Fault Detection and Diagnosis Approaches)

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26 pages, 8543 KiB

Open AccessArticle

Weak Fault Feature Extraction Method Based on Improved Stochastic Resonance

by Zhen Yang, Zhiqian Li, Fengxing Zhou, Yajie Ma and Baokang Yan

Sensors 2022, 22(17), 6644; https://doi.org/10.3390/s22176644 - 2 Sep 2022

Cited by 5 | Viewed by 1258

Abstract

Aiming at the problems of early weak fault feature extraction of bearings in rotating machinery, an improved stochastic resonance (SR) is proposed combined with the advantage of SR to enhance weak characteristic signals with noise energy. Firstly, according to the characteristics of the large parameters of the actual fault signal, the amplitude transform coefficient and frequency transform coefficient are introduced to convert the large parameter signal into small parameter signal which can be processed by SR, and the relationship of second-order parameters are introduced. Secondly, a comprehensive evaluation index (CEI) consisted of power spectrum kurtosis, correlation coefficient, structural similarity, root mean square error, and approximate entropy, is constructed through BP neural network. Moreover, this CEI is adopted as fitness function to search the optimal damping coefficient and amplitude transform coefficient with adaptive weight particle swarm optimization (PSO). Finally, according to the improved optimal SR system, the weak fault feature can be extracted. The simulation and experiment verify the effectiveness of the proposed method compared with traditional second-order general scale transform adaptive SR. Full article

(This article belongs to the Special Issue Advanced Sensor Fault Detection and Diagnosis Approaches)

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16 pages, 4615 KiB

Open AccessArticle

Ball Screw Fault Diagnosis Based on Wavelet Convolution Transfer Learning

by Yifan Xie, Chang Liu, Liji Huang and Hongchun Duan

Sensors 2022, 22(16), 6270; https://doi.org/10.3390/s22166270 - 20 Aug 2022

Cited by 4 | Viewed by 1578

Abstract

The ball screw is the core component of the CNC machine tool feed system, and its health plays an important role in the feed system and even in the entire CNC machine tool. This paper studies the fault diagnosis and health assessment of ball screws. Aiming at the problem that the ball screw signal is weak and susceptible to interference, using a wavelet convolution structure to improve the network can improve the mining ability of signal time domain and frequency domain features; aiming at the challenge of ball screw sensor installation position limitation, a transfer learning method is proposed, which adopts the domain adaptation method as jointly distributed adaptation (JDA), and realizes the transfer diagnosis across measurement positions by extracting the diagnosis knowledge of different positions of the ball screw. In this paper, the adaptive batch normalization algorithm (AdaBN) is introduced to enhance the proposed model so as to improve the accuracy of migration diagnosis. Experiments were carried out using a self-made lead screw fatigue test bench. Through experimental verification, the method proposed in this paper can extract effective fault diagnosis knowledge. By collecting data under different working conditions at the bearing seat of the ball screw, the fault diagnosis knowledge is extracted and used to identify and diagnose the position fault of the nut seat. In this paper, some background noise is added to the collected data to test the robustness of the proposed network model. Full article

(This article belongs to the Special Issue Advanced Sensor Fault Detection and Diagnosis Approaches)

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