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Deep Learning-Based Sensors and Signal Processing for Fault Diagnosis and Remaining Useful Life Prediction

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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 October 2023) | Viewed by 20956

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Special Issue Editors

Prof. Dr. Yu Gu

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Guest Editor

College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
Interests: deep learning; sensors and sensing systems; intelligent information processing technology and artificial intelligence technology; data fusion and mining methods; pattern recognition

Dr. Lei Zhang

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Guest Editor

School of Artificial Intelligence, Hebei University of Technology, Tianjin 300130, China
Interests: high reliability control and predictive maintenance of wind turbines based on data-driven; top-level design of smart city based on big data analysis

Prof. Dr. Andreas Terfort

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Guest Editor

Institute of Inorganic and Analytical Chemistry, Goethe-University Frankfurt am Main, 60438 Frankfurt, Germany
Interests: surface chemistry; self-assembling monolayers; new methods for micro and nano fabrication on sensors

Special Issue Information

Dear Colleagues,

Deep learning (DL) has attracted increasing attention for its potential use in fault diagnosis and remaining useful life prediction. Traditional fault diagnosis and remaining useful life prediction methods, including physical-model-based methods and expert-knowledge-based methods, rely largely on the sufficient understanding of the mechanisms of the machinery, leading to variable diagnosis results due to the complexity of modern machinery and working conditions. DL-based methods offer new opportunities by automatically extracting features and establishing relationships between monitoring data collected from the sensors and the health state of the machines. Recently, due to the emergence of the big data era and the development of artificial intelligence, DL-based methods enable highly efficient fault diagnoses and remaining useful life predictions in intelligent industrial applications.

This Special Issue therefore aims to put together original research and review articles on recent advances, technologies, solutions, applications, and new challenges in the field of DL-based methods for fault detection and diagnosis.

Prof. Dr. Yu Gu
Dr. Lei Zhang
Prof. Dr. Andreas Terfort
Guest Editors

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Keywords

deep learning
sensor
signal processing
fault diagnosis
remaining useful life prediction
neural network

Published Papers (9 papers)

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Research

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21 pages, 9116 KiB

Open AccessArticle

Intelligent Fault Diagnosis of Hydraulic Multi-Way Valve Using the Improved SECNN-GRU Method with mRMR Feature Selection

by Hanlin Guan, Ren Yan, Hesheng Tang and Jiawei Xiang

Sensors 2023, 23(23), 9371; https://doi.org/10.3390/s23239371 - 23 Nov 2023

Cited by 2 | Viewed by 761

Abstract

Hydraulic multi-way valves as core components are widely applied in engineering machinery, mining machinery, and metallurgical industries. Due to the harsh working environment, faults in hydraulic multi-way valves are prone to occur, and the faults that occur are hidden. Moreover, hydraulic multi-way valves are expensive, and multiple experiments are difficult to replicate to obtain true fault data. Therefore, it is not easy to achieve fault diagnosis of hydraulic multi-way valves. To address this problem, an effective intelligent fault diagnosis method is proposed using an improved Squeeze-Excitation Convolution Neural Network and Gated Recurrent Unit (SECNN-GRU). The effectiveness of the method is verified by designing a simulation model for a hydraulic multi-way valve to generate fault data, as well as the actual data obtained by establishing an experimental platform for a directional valve. In this method, shallow statistical features are first extracted from data containing fault information, and then fault features with high correlation with fault types are selected using the Maximum Relevance Minimum Redundancy algorithm (mRMR). Next, spatial dimension features are extracted through CNN. By adding the Squeeze-Excitation Block, different weights are assigned to features to obtain weighted feature vectors. Finally, the time-dimension features of the weighted feature vectors are extracted and fused through GRU, and the fused features are classified using a classifier. The fault data obtained from the simulation model verifies that the average diagnostic accuracy of this method can reach 98.94%. The average accuracy of this method can reach 92.10% (A1 sensor as an example) through experimental data validation of the directional valve. Compared with other intelligent diagnostic algorithms, the proposed method has better stationarity and higher diagnostic accuracy, providing a feasible solution for fault diagnosis of the hydraulic multi-way valve. Full article

(This article belongs to the Special Issue Deep Learning-Based Sensors and Signal Processing for Fault Diagnosis and Remaining Useful Life Prediction)

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16 pages, 2709 KiB

Open AccessArticle

Fault Diagnosis of Rolling Bearing Based on HPSO Algorithm Optimized CNN-LSTM Neural Network

by He Tian, Huaicong Fan, Mingwen Feng, Ranran Cao and Dong Li

Sensors 2023, 23(14), 6508; https://doi.org/10.3390/s23146508 - 19 Jul 2023

Cited by 10 | Viewed by 1390

Abstract

The quality of rolling bearings is vital for the working state and rotation accuracy of the shaft. Timely and accurately acquiring bearing status and early fault diagnosis can effectively prevent losses, making it highly practical. To improve the accuracy of bearing fault diagnosis, this paper proposes a CNN-LSTM bearing fault diagnosis model optimized by hybrid particle swarm optimization (HPSO). The HPSO algorithm has a strong global optimization ability and can effectively solve nonlinear and multivariate optimization problems. It is used to optimize and match the parameters of the CNN-LSTM model and dynamically find the optimal value of the parameters. This model overcomes the problem that the parameters of the CNN-LSTM model depend on empirical settings and cannot be adjusted dynamically. This model is used for bearing fault diagnosis, and the accuracy rate of fault diagnosis classification reaches 99.2%. Compared with the traditional CNN, LSTM, and CNN-LSTM models, the accuracy rates are increased by 6.6%, 9.2%, and 5%, respectively. At the same time, comparing the models with different optimization parameters shows that the model proposed in this paper has the highest accuracy. The experimental results verified the superiority of the HPSO algorithm to optimize model parameters and the feasibility and accuracy of the HPSO-CNN-LSTM model for bearing fault diagnosis. Full article

(This article belongs to the Special Issue Deep Learning-Based Sensors and Signal Processing for Fault Diagnosis and Remaining Useful Life Prediction)

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20 pages, 5521 KiB

Open AccessArticle

Lite and Efficient Deep Learning Model for Bearing Fault Diagnosis Using the CWRU Dataset

by Yubin Yoo, Hangyeol Jo and Sang-Woo Ban

Sensors 2023, 23(6), 3157; https://doi.org/10.3390/s23063157 - 15 Mar 2023

Cited by 16 | Viewed by 3407

Abstract

Bearing defects are a common problem in rotating machines and equipment that can lead to unexpected downtime, costly repairs, and even safety hazards. Diagnosing bearing defects is crucial for preventative maintenance, and deep learning models have shown promising results in this field. On the other hand, the high complexity of these models can lead to high computational and data processing costs, making their practical implementation challenging. Recent studies have focused on optimizing these models by reducing their size and complexity, but these methods often compromise classification performance. This paper proposes a new approach that reduces the dimensionality of input data and optimizes the model structure simultaneously. A much lower input data dimension than that of existing deep learning models was achieved by downsampling the vibration sensor signals used for bearing defect diagnosis and constructing spectrograms. This paper introduces a lite convolutional neural network (CNN) model with fixed feature map dimensions that achieve high classification accuracy with low-dimensional input data. The vibration sensor signals used for bearing defect diagnosis were first downsampled to reduce the dimensionality of the input data. Next, spectrograms were constructed using the signals of the minimum interval. Experiments were conducted using the vibration sensor signals from the Case Western Reserve University (CWRU) dataset. The experimental results show that the proposed method could be highly efficient in terms of computation while maintaining outstanding classification performance. The results show that the proposed method outperformed a state-of-the-art model for bearing defect diagnosis under different conditions. This approach is not limited to the field of bearing failure diagnosis, but could be applied potentially to other fields that require the analysis of high-dimensional time series data. Full article

(This article belongs to the Special Issue Deep Learning-Based Sensors and Signal Processing for Fault Diagnosis and Remaining Useful Life Prediction)

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21 pages, 5713 KiB

Open AccessArticle

Induction Motor Fault Diagnosis Using Support Vector Machine, Neural Networks, and Boosting Methods

by Min-Chan Kim, Jong-Hyun Lee, Dong-Hun Wang and In-Soo Lee

Sensors 2023, 23(5), 2585; https://doi.org/10.3390/s23052585 - 26 Feb 2023

Cited by 14 | Viewed by 3013

Abstract

Induction motors are robust and cost effective; thus, they are commonly used as power sources in various industrial applications. However, due to the characteristics of induction motors, industrial processes can stop when motor failures occur. Thus, research is required to realize the quick and accurate diagnosis of faults in induction motors. In this study, we constructed an induction motor simulator with normal, rotor failure, and bearing failure states. Using this simulator, 1240 vibration datasets comprising 1024 data samples were obtained for each state. Then, failure diagnosis was performed on the acquired data using support vector machine, multilayer neural network, convolutional neural network, gradient boosting machine, and XGBoost machine learning models. The diagnostic accuracies and calculation speeds of these models were verified via stratified K-fold cross validation. In addition, a graphical user interface was designed and implemented for the proposed fault diagnosis technique. The experimental results demonstrate that the proposed fault diagnosis technique is suitable for diagnosing faults in induction motors. Full article

(This article belongs to the Special Issue Deep Learning-Based Sensors and Signal Processing for Fault Diagnosis and Remaining Useful Life Prediction)

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17 pages, 5824 KiB

Open AccessArticle

Fault Diagnosis of Rolling Bearing Based on a Priority Elimination Method

by Chuan Xiang, Jiahui Zhou, Bing Han, Weichen Li and Hongge Zhao

Sensors 2023, 23(4), 2320; https://doi.org/10.3390/s23042320 - 19 Feb 2023

Cited by 3 | Viewed by 1587

Abstract

Aiming at the fault diagnosis accuracy of rolling bearings is not high enough, and unknown faults cannot be correctly identified. A priority elimination (PE) method is proposed in this paper. First, the priority diagnosis sequence of faults was determined by comparing the ratios of the inter-class distance to the intra-class distance for all faults. Then, the model training and fault diagnosis were carried out in order of the priority sequence, and the samples of the fault that had been identified were eliminated from the data set until all faults were diagnosed. For the diagnosis model, the stacked sparse auto-encoder network (SSAE) was selected to extract the features of the vibration signal. The extreme gradient boosting algorithm (XGBoost) was chosen to identify the fault type. Finally, the method was tested and verified by experimental data and compared with classical algorithms. Research results indicate the following: (1) with the addition of PE based on SSAE-XGBoost, the fault diagnosis accuracy can be improved from 96.3% to 99.27%, which is higher than other methods; (2) for the test set with the samples of unknown faults, the diagnosis accuracy of SSAE-XGBoost with PE can reach 92.34%, which is nearly 6% higher than that without PE and is also obviously higher than other classical fault diagnosis methods with or without PE. The PE method can not only improve the diagnosis accuracy of faults but also identify unknown faults, which provides a new method and way for fault diagnosis. Full article

(This article belongs to the Special Issue Deep Learning-Based Sensors and Signal Processing for Fault Diagnosis and Remaining Useful Life Prediction)

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17 pages, 3587 KiB

Open AccessArticle

Remaining Useful Life Prediction of Rolling Bearings Using GRU-DeepAR with Adaptive Failure Threshold

by Jiahui Li, Zhihai Wang, Xiaoqin Liu and Zhengjiang Feng

Sensors 2023, 23(3), 1144; https://doi.org/10.3390/s23031144 - 19 Jan 2023

Cited by 9 | Viewed by 1779

Abstract

Aiming at the problem that a single neural network model has difficulty in accurately predicting trends of the remaining useful life of a rolling bearing, a method of predicting the remaining useful life of rolling bearings using a gated recurrent unit-deep autoregressive model (GRU-DeepAR) with an adaptive failure threshold was proposed. First, time domain and frequency domain features were extracted from the rolling bearing vibration signal. Second, its operation process was divided into a smooth operation stage and degradation stage according to the trend of the accumulated root mean square of maximum. Then, the failure threshold for different bearings were determined adaptively by the maximum of the smooth operation data. The degradation dataset of a rolling bearing was subsequently obtained. In the meantime, a GRU-DeepAR model was built to obtain predictions of the failure time and failure probability. Appropriate model parameters were determined after a large number of tests to assure the effectiveness and prediction accuracy. Finally, the trend of time series and failure times were predicted by inputting the degradation dataset into the GRU-DeepAR model. Experiments showed that the proposed method can effectively improve the accuracy of the remaining useful life prediction of a rolling bearing with good stability. Full article

(This article belongs to the Special Issue Deep Learning-Based Sensors and Signal Processing for Fault Diagnosis and Remaining Useful Life Prediction)

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22 pages, 6992 KiB

Open AccessArticle

Operational and Analytical Modal Analysis of a Bridge Using Low-Cost Wireless Arduino-Based Accelerometers

by Seyedmilad Komarizadehasl, Pierre Huguenet, Fidel Lozano, Jose Antonio Lozano-Galant and Jose Turmo

Sensors 2022, 22(24), 9808; https://doi.org/10.3390/s22249808 - 14 Dec 2022

Cited by 16 | Viewed by 2960

Abstract

Arduino-based accelerometers are receiving wide attention from researchers to make long-term Structural Health Monitoring (SHM) feasible for structures with a low SHM budget. The current low-cost solutions found in the literature share some of the following drawbacks: (1) high noise density, (2) lack of wireless synchronization, (3) lack of automatic data acquisition and data management, and (4) lack of dedicated field tests aiming to compare mode shapes from Operational Modal Analysis (OMA) with those of a digital model. To solve these problems, a recently built short-span footbridge in Barcelona is instrumented using four Low-cost Adaptable Reliable Accelerometers (LARA). In this study, the automatization of the data acquisition and management of these low-cost solutions is studied for the first time in the literature. In addition, a digital model of the bridge under study is generated in SAP2000 using the available drawings and reported characteristics of its materials. The OMA of the bridge is calculated using Frequency Domain Decomposition (FDD) and Covariance Stochastic Subspace Identification (SSI-cov) methods. Using the Modal Assurance Criterion (MAC), the mode shapes of OMA are compared with those of the digital model. Finally, the acquired eigenfrequencies of the bridge obtained with a high-precision commercial sensor (HI-INC) showed a good agreement with those obtained with LARA. Full article

(This article belongs to the Special Issue Deep Learning-Based Sensors and Signal Processing for Fault Diagnosis and Remaining Useful Life Prediction)

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18 pages, 3888 KiB

Open AccessArticle

Research on a Bearing Fault Enhancement Diagnosis Method with Convolutional Neural Network Based on Adaptive Stochastic Resonance

by Chen Wang, Zijian Qiao, Zhangjun Huang, Junchen Xu, Shitong Fang, Cailiang Zhang, Jinjun Liu, Ronghua Zhu and Zhihui Lai

Sensors 2022, 22(22), 8730; https://doi.org/10.3390/s22228730 - 11 Nov 2022

Cited by 2 | Viewed by 1407

Abstract

As a powerful feature extraction tool, a convolutional neural network (CNN) has strong adaptability for big data applications such as bearing fault diagnosis, whereas the classification performance is limited when the quality of raw signals is poor. In this paper, stochastic resonance (SR), which provides an advanced feature enhancement approach for weak signals with strong background noise, is introduced as a data pre-processing method for the CNN to improve its classification performance. First, a multiparameter adjusting bistable Duffing system that can achieve SR under large-parameter weak signals is introduced. A hybrid optimization algorithm (HOA) combining the genetic algorithm (GA) and the simulated annealing (SA) is proposed to adaptively obtain the optimized parameters and output signal-to-noise ratio (SNR) of the Duffing system. Therefore, the data optimization based on the multiparameter-adjusting SR of Duffing system can be realized. An SR-based mapping method is further proposed to convert the outputs of the Duffing system into grey images, which can be further processed by a normal CNN with batch normalization (BN) layers and dropout layers. After verifying the feasibility of the HOA in multiparameter optimization of the Duffing system, the bearing fault data set from the CWRU bearing data center was processed by the proposed fault enhancement classification and identification method. The research showed that the weak features of the bearing signals could be enhanced significantly through the adaptive multiparameter optimization of SR, and classification accuracies for 10 categories of bearing signals could achieve 100% and those for 20 categories could achieve more than 96.9%, which is better than other methods. The influences of the population number on the classification accuracies and calculation time were further studied, and the feature map and network visualization are presented. It was demonstrated that the proposed method can realize high-performance fault enhancement classification and identification. Full article

(This article belongs to the Special Issue Deep Learning-Based Sensors and Signal Processing for Fault Diagnosis and Remaining Useful Life Prediction)

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Review

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22 pages, 2600 KiB

Open AccessReview

Fault Prediction Based on Leakage Current in Contaminated Insulators Using Enhanced Time Series Forecasting Models

by Nemesio Fava Sopelsa Neto, Stefano Frizzo Stefenon, Luiz Henrique Meyer, Raúl García Ovejero and Valderi Reis Quietinho Leithardt

Sensors 2022, 22(16), 6121; https://doi.org/10.3390/s22166121 - 16 Aug 2022

Cited by 37 | Viewed by 3524

Abstract

To improve the monitoring of the electrical power grid, it is necessary to evaluate the influence of contamination in relation to leakage current and its progression to a disruptive discharge. In this paper, insulators were tested in a saline chamber to simulate the increase of salt contamination on their surface. From the time series forecasting of the leakage current, it is possible to evaluate the development of the fault before a flashover occurs. In this paper, for a complete evaluation, the long short-term memory (LSTM), group method of data handling (GMDH), adaptive neuro-fuzzy inference system (ANFIS), bootstrap aggregation (bagging), sequential learning (boosting), random subspace, and stacked generalization (stacking) ensemble learning models are analyzed. From the results of the best structure of the models, the hyperparameters are evaluated and the wavelet transform is used to obtain an enhanced model. The contribution of this paper is related to the improvement of well-established models using the wavelet transform, thus obtaining hybrid models that can be used for several applications. The results showed that using the wavelet transform leads to an improvement in all the used models, especially the wavelet ANFIS model, which had a mean RMSE of 1.58

\times 10^{- 3}

, being the model that had the best result. Furthermore, the results for the standard deviation were 2.18

\times 10^{- 19}

, showing that the model is stable and robust for the application under study. Future work can be performed using other components of the distribution power grid susceptible to contamination because they are installed outdoors. Full article

(This article belongs to the Special Issue Deep Learning-Based Sensors and Signal Processing for Fault Diagnosis and Remaining Useful Life Prediction)

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