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Machine Health Monitoring and Fault Diagnosis Techniques (Volume II)

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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: 31 May 2024 | Viewed by 9132

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Special Issue Editors

Dr. Shilong Sun

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Guest Editor

Harbin Institute of Technology, Shenzhen, China
Interests: vibration energy harvesting design; fault diagnosis and prognosis; decision-making with artificial intelligence; deep learning for industrial data
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Changqing Shen

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Guest Editor

School of Rail Transportation, Soochow University, Suzhou 215131, China
Interests: signal processing; data mining; fault diagnosis; mechanical engineering
Special Issues, Collections and Topics in MDPI journals

Dr. Dong Wang

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Guest Editor

The State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: intelligent operation and maintenance; mathematical basis of fault feature extraction and sparse measure; prognostic and health management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine health monitoring and fault diagnosis have recently come to play a crucial role in automatic and intelligent industrial plants. Intelligent fault diagnosis has been proposed based on machine learning, deep learning, and artificial intelligence. However, several issues still require further investigation, including intelligent fault diagnosis methodologies, e.g., early fault detection features, the few-shot sample machine learning algorithm, data augmentation techniques for deep learning, data fusion methods for domain adaptation, feature representation with self-supervision, and interpretable deep learning algorithms.

This Special Issue aims to highlight the state-of-the-art techniques used for machine health monitoring and fault diagnosis, especially for intelligent fault diagnosis algorithm development, fault feature extraction, and intelligent machine monitoring. Topics of interest include, but are not limited to:

Rotational machine monitoring and vibration signal processing;
Intelligent early fault detection and diagnosis;
Few-shot sample learning for fault detection;
Feature construction with intelligent algorithms;
Data-efficient domain adaptation and transfer learning;
Interpretable deep learning for fault diagnosis;
Data augmentation techniques for fault diagnosis;
Sensor data fusion for fault diagnosis;
Measurement methods, technologies, and systems for fault diagnosis.

Dr. Shilong Sun
Prof. Dr. Changqing Shen
Dr. Dong Wang
Guest Editors

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Published Papers (6 papers)

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Research

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19 pages, 5280 KiB

Open AccessArticle

A New Method for Bearing Fault Diagnosis across Machines Based on Envelope Spectrum and Conditional Metric Learning

by Xu Yang, Junfeng Yang, Yupeng Jin and Zhongchao Liu

Sensors 2024, 24(9), 2674; https://doi.org/10.3390/s24092674 - 23 Apr 2024

Viewed by 246

Abstract

In recent years, most research on bearing fault diagnosis has assumed that the source domain and target domain data come from the same machine. The differences in equipment lead to a decrease in diagnostic accuracy. To address this issue, unsupervised domain adaptation techniques have been introduced. However, most cross-device fault diagnosis models overlook the discriminative information under the marginal distribution, which restricts the performance of the models. In this paper, we propose a bearing fault diagnosis method based on envelope spectrum and conditional metric learning. First, envelope spectral analysis is used to extract frequency domain features. Then, to fully utilize the discriminative information from the label distribution, we construct a deep Siamese convolutional neural network based on conditional metric learning to eliminate the data distribution differences and extract common features from the source and target domain data. Finally, dynamic weighting factors are employed to improve the convergence performance of the model and optimize the training process. Experimental analysis is conducted on 12 cross-device tasks and compared with other relevant methods. The results show that the proposed method achieves the best performance on all three evaluation metrics. Full article

(This article belongs to the Special Issue Machine Health Monitoring and Fault Diagnosis Techniques (Volume II))

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27 pages, 10827 KiB

Open AccessArticle

Preventing Forklift Front-End Failures: Predicting the Weight Centers of Heavy Objects, Remaining Useful Life Prediction under Abnormal Conditions, and Failure Diagnosis Based on Alarm Rules

by Jeong-Geun Lee, Yun-Sang Kim and Jang Hyun Lee

Sensors 2023, 23(18), 7706; https://doi.org/10.3390/s23187706 - 06 Sep 2023

Viewed by 985

Abstract

This paper addresses the critical challenge of preventing front-end failures in forklifts by addressing the center of gravity, accurate prediction of the remaining useful life (RUL), and efficient fault diagnosis through alarm rules. The study’s significance lies in offering a comprehensive approach to enhancing forklift operational reliability. To achieve this goal, acceleration signals from the forklift’s front-end were collected and processed. Time-domain statistical features were extracted from one-second windows, subsequently refined through an exponentially weighted moving average to mitigate noise. Data augmentation techniques, including AWGN and LSTM autoencoders, were employed. Based on the augmented data, random forest and lightGBM models were used to develop classification models for the weight centers of heavy objects carried by a forklift. Additionally, contextual diagnosis was performed by applying exponentially weighted moving averages to the classification probabilities of the machine learning models. The results indicated that the random forest achieved an accuracy of 0.9563, while lightGBM achieved an accuracy of 0.9566. The acceleration data were collected through experiments to predict forklift failure and RUL, particularly due to repeated forklift use when the centers of heavy objects carried by the forklift were skewed to the right. Time-domain statistical features of the acceleration signals were extracted and used as variables by applying a 20 s window. Subsequently, logistic regression and random forest models were employed to classify the failure stages of the forklifts. The F1 scores (macro) obtained were 0.9790 and 0.9220 for logistic regression and random forest, respectively. Moreover, random forest probabilities for each stage were combined and averaged to generate a degradation curve and determine the failure threshold. The coefficient of the exponential function was calculated using the least squares method on the degradation curve, and an RUL prediction model was developed to predict the failure point. Furthermore, the SHAP algorithm was utilized to identify significant features for classifying the stages. Fault diagnosis using alarm rules was conducted by establishing a threshold derived from the significant features within the normal stage. Full article

(This article belongs to the Special Issue Machine Health Monitoring and Fault Diagnosis Techniques (Volume II))

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23 pages, 7592 KiB

Open AccessArticle

Research on Diesel Engine Fault Status Identification Method Based on Synchro Squeezing S-Transform and Vision Transformer

by Siyu Li, Zichang Liu, Yunbin Yan, Rongcai Wang, Enzhi Dong and Zhonghua Cheng

Sensors 2023, 23(14), 6447; https://doi.org/10.3390/s23146447 - 16 Jul 2023

Cited by 1 | Viewed by 1124

Abstract

The reliability and safety of diesel engines gradually decrease with the increase in running time, leading to frequent failures. To address the problem that it is difficult for the traditional fault status identification methods to identify diesel engine faults accurately, a diesel engine fault status identification method based on synchro squeezing S-transform (SSST) and vision transformer (ViT) is proposed. This method can effectively combine the advantages of the SSST method in processing non-linear and non-smooth signals with the powerful image classification capability of ViT. The vibration signals reflecting the diesel engine status are collected by sensors. To solve the problems of low time-frequency resolution and weak energy aggregation in traditional signal time-frequency analysis methods, the SSST method is used to convert the vibration signals into two-dimensional time-frequency maps; the ViT model is used to extract time-frequency image features for training to achieve diesel engine status assessment. Pre-set fault experiments are carried out using the diesel engine condition monitoring experimental bench, and the proposed method is compared with three traditional methods, namely, ST-ViT, SSST-2DCNN and FFT spectrum-1DCNN. The experimental results show that the overall fault status identification accuracy in the public dataset and the actual laboratory data reaches 98.31% and 95.67%, respectively, providing a new idea for diesel engine fault status identification. Full article

(This article belongs to the Special Issue Machine Health Monitoring and Fault Diagnosis Techniques (Volume II))

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26 pages, 10532 KiB

Open AccessArticle

Bayesian-Optimized Hybrid Kernel SVM for Rolling Bearing Fault Diagnosis

by Xinmin Song, Weihua Wei, Junbo Zhou, Guojun Ji, Ghulam Hussain, Maohua Xiao and Guosheng Geng

Sensors 2023, 23(11), 5137; https://doi.org/10.3390/s23115137 - 28 May 2023

Cited by 9 | Viewed by 1747

Abstract

We propose a new fault diagnosis model for rolling bearings based on a hybrid kernel support vector machine (SVM) and Bayesian optimization (BO). The model uses discrete Fourier transform (DFT) to extract fifteen features from vibration signals in the time and frequency domains of four bearing failure forms, which addresses the issue of ambiguous fault identification caused by their nonlinearity and nonstationarity. The extracted feature vectors are then divided into training and test sets as SVM inputs for fault diagnosis. To optimize the SVM, we construct a hybrid kernel SVM using a polynomial kernel function and radial basis kernel function. BO is used to optimize the extreme values of the objective function and determine their weight coefficients. We create an objective function for the Gaussian regression process of BO using training and test data as inputs, respectively. The optimized parameters are used to rebuild the SVM, which is then trained for network classification prediction. We tested the proposed diagnostic model using the bearing dataset of the Case Western Reserve University. The verification results show that the fault diagnosis accuracy is improved from 85% to 100% compared with the direct input of vibration signal into the SVM, and the effect is significant. Compared with other diagnostic models, our Bayesian-optimized hybrid kernel SVM model has the highest accuracy. In laboratory verification, we took sixty sets of sample values for each of the four failure forms measured in the experiment, and the verification process was repeated. The experimental results showed that the accuracy of the Bayesian-optimized hybrid kernel SVM reached 100%, and the accuracy of five replicates reached 96.7%. These results demonstrate the feasibility and superiority of our proposed method for fault diagnosis in rolling bearings. Full article

(This article belongs to the Special Issue Machine Health Monitoring and Fault Diagnosis Techniques (Volume II))

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15 pages, 21042 KiB

Open AccessArticle

Convolutional Neural Network-Based Transformer Fault Diagnosis Using Vibration Signals

by Chao Li, Jie Chen, Cheng Yang, Jingjian Yang, Zhigang Liu and Pooya Davari

Sensors 2023, 23(10), 4781; https://doi.org/10.3390/s23104781 - 16 May 2023

Cited by 5 | Viewed by 2092

Abstract

Fast and accurate fault diagnosis is crucial to transformer safety and cost-effectiveness. Recently, vibration analysis for transformer fault diagnosis is attracting increasing attention due to its ease of implementation and low cost, while the complex operating environment and loads of transformers also pose challenges. This study proposed a novel deep-learning-enabled method for fault diagnosis of dry-type transformers using vibration signals. An experimental setup is designed to simulate different faults and collect the corresponding vibration signals. To find out the fault information hidden in the vibration signals, the continuous wavelet transform (CWT) is applied for feature extraction, which can convert vibration signals to red-green-blue (RGB) images with the time–frequency relationship. Then, an improved convolutional neural network (CNN) model is proposed to complete the image recognition task of transformer fault diagnosis. Finally, the proposed CNN model is trained and tested with the collected data, and its optimal structure and hyperparameters are determined. The results show that the proposed intelligent diagnosis method achieves an overall accuracy of 99.95%, which is superior to other compared machine learning methods. Full article

(This article belongs to the Special Issue Machine Health Monitoring and Fault Diagnosis Techniques (Volume II))

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Review

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42 pages, 24578 KiB

Open AccessReview

Progress in Active Infrared Imaging for Defect Detection in the Renewable and Electronic Industries

by Xinfeng Zhao, Yangjing Zhao, Shunchang Hu, Hongyan Wang, Yuyan Zhang and Wuyi Ming

Sensors 2023, 23(21), 8780; https://doi.org/10.3390/s23218780 - 27 Oct 2023

Cited by 3 | Viewed by 2167

Abstract

In recent years, infrared thermographic (IRT) technology has experienced notable advancements and found widespread applications in various fields, such as renewable industry, electronic industry, construction, aviation, and healthcare. IRT technology is used for defect detection due to its non-contact, efficient, and high-resolution methods, which enhance product quality and reliability. This review offers an overview of active IRT principles. It comprehensively examines four categories based on the type of heat sources employed: pulsed thermography (PT), lock-in thermography (LT), ultrasonically stimulated vibration thermography (UVT), and eddy current thermography (ECT). Furthermore, the review explores the application of IRT imaging in the renewable energy sector, with a specific focus on the photovoltaic (PV) industry. The integration of IRT imaging and deep learning techniques presents an efficient and highly accurate solution for detecting defects in PV panels, playing a critical role in monitoring and maintaining PV energy systems. In addition, the application of infrared thermal imaging technology in electronic industry is reviewed. In the development and manufacturing of electronic products, IRT imaging is used to assess the performance and thermal characteristics of circuit boards. It aids in detecting potential material and manufacturing defects, ensuring product quality. Furthermore, the research discusses algorithmic detection for PV panels, the excitation sources used in electronic industry inspections, and infrared wavelengths. Finally, the review analyzes the advantages and challenges of IRT imaging concerning excitation sources, the PV industry, the electronics industry, and artificial intelligence (AI). It provides insights into critical issues requiring attention in future research endeavors. Full article

(This article belongs to the Special Issue Machine Health Monitoring and Fault Diagnosis Techniques (Volume II))

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