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Signal Processing and Sensing Technologies for Fault Diagnosis

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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: 30 September 2024 | Viewed by 4360

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Special Issue Editors

Dr. Yi Wang

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Guest Editor

1. State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China
2. College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China
Interests: fault diagnosis; signal processing; life prediction; condition monitoring

Dr. Longting Chen

E-Mail Website
Guest Editor

1. State Key Laboratory of High Performance Complex Manufacturing, Central South University, Changsha 410083, China
2. College of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China
Interests: fault diagnosis; signal processing; deep learning

Dr. Liang Guo

E-Mail Website
Guest Editor

School of Mechanical Engineering, Southwest Jiaotong University, No. 111, North Section 1, Chengdu 610031, China
Interests: prognostics and health management

Special Issue Information

Dear Colleagues,

Recently, we have seen a growing amount of interest in signal processing and sensing methods for fault diagnosis. However, more effective sensing technology and more powerful signal processing methods are still required to make fault diagnosis more reliable and practical in industrial applications. Recent advances in signal processing and sensing technologies have made highly effective condition monitoring and prognosis available for key equipment, such as aircraft engines, wind turbines, high-speed trains, CNC machines, etc.

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 signal processing and sensing technologies.

Potential topics include but are not limited to:

Sensing technologies for condition monitoring and fault diagnosis;
Multisensory signal fusion methods;
Signal enhancement methods;
Data analytics and condition monitoring via ML/AI techniques;
Dynamic models and model-based techniques;
Time–frequency analysis methods;
Angular approaches;
Predictive maintenance using artificial intelligence;
Feature fusion methods;
Health indicators for condition monitoring and fault diagnosis;
Digital-twin-based fault diagnosis;
Interpretable deep learning for fault diagnosis.

Dr. Yi Wang
Dr. Longting Chen
Dr. Liang Guo
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (7 papers)

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Research

19 pages, 3117 KiB

Open AccessArticle

An Adaptive Multi-D-Norm-Driven Sparse Unfolding Deconvolutional Network for Bearing Fault Diagnosis

by Jianbo Lin, Han Zhang, Yunfei Li and Zhaohui Du

Sensors 2024, 24(8), 2624; https://doi.org/10.3390/s24082624 - 19 Apr 2024

Viewed by 272

Abstract

Impulsive blind deconvolution (IBD) is a popular method to recover impulsive sources for bearing fault diagnosis. Its underpinnings are in the design of objective functions based on prior knowledge of impulsive sources and a transfer function to describe transmission path influences. However, popular objective functions cannot retain waveform impulsiveness and periodicity cyclostationarity simultaneously, and the single convolution operation of IBD methods is insufficient to describe transmission paths composed of multiple linear and nonlinear units. Inspired by the MaxPooling period modulation intensity (MPMI) and convolutional sparse learning (CSL), an adaptive multi-D-norm-driven sparse unfolding deconvolution network (AMD-SUDN) is proposed in this paper. The core strategy is that one target vector with simultaneous impulsiveness and cyclostationarity is constructed automatically through the MPMI; then, this vector is substituted into the multi D-norm to design objective functions. Moreover, an iterative soft threshold algorithm (ISTA) for the CSL model is derived, and its iterative steps are unfolded into one deconvolution network. The algorithm’s performance and the hyperparameter configuration are investigated by a set of numerical simulations. Finally, the proposed AMD-SUDN is applied to detect the impulsive features of bearing faults. All comparative results verify that the proposed AMD-SUDN achieves a better deconvolution accuracy than state-of-the-art IBD methods. Full article

(This article belongs to the Special Issue Signal Processing and Sensing Technologies for Fault Diagnosis)

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16 pages, 1921 KiB

Open AccessArticle

The Methodology for Evaluating the Operating State of SF6 HVCBs Based on IDDA

by Tong Bai, Chenhao Sun, Wenqing Feng, Yajing Liu, Huanzhen Zhang and Yujia Wang

Sensors 2024, 24(8), 2513; https://doi.org/10.3390/s24082513 - 14 Apr 2024

Viewed by 299

Abstract

To enhance the precision of evaluating the operational status of SF6 high-voltage circuit breakers (HVCBs) and devise judicious maintenance strategies, this study introduces an operational state assessment method for SF6 HVCBs grounded in the integrated data-driven analysis (IDDA) model. The relative degradation weight (RDW) is introduced as a metric for quantifying the relative significance of distinct indicators concerning the operational condition of SF6 HVCBs. A data-driven model, founded on critical factor stability (CFS), is formulated to convert environmental indicators into quantitative computations. Furthermore, an optimized fuzzy inference (OFI) system is devised to streamline the system architecture and enhance the processing speed of continuous indicators. Ultimately, the efficacy of the proposed model is substantiated through validation, and results from instance analyses underscore that the presented approach not only attains heightened accuracy in assessment compared to extant analytical methodologies but also furnishes a dependable foundation for prioritizing maintenance sequences across diverse components. Full article

(This article belongs to the Special Issue Signal Processing and Sensing Technologies for Fault Diagnosis)

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10 pages, 2144 KiB

Open AccessCommunication

Incremental Clustering for Predictive Maintenance in Cryogenics for Radio Astronomy

by Alessandro Cabras, Pierluigi Ortu, Tonino Pisanu, Paolo Maxia and Roberto Caocci

Sensors 2024, 24(7), 2278; https://doi.org/10.3390/s24072278 - 03 Apr 2024

Viewed by 427

Abstract

In a cooling system for radio astronomy receivers, maintaining cold heads and compressors is essential for consistent performance. This project focuses on monitoring the power currents of the cold head’s motor to address potential mechanical deterioration, which could jeopardize the overall functionality of the system. Using Hall effect sensors, a microcontroller-based electronic board, and artificial intelligence, the system detects and predicts anomalies. The model operates using an unsupervised approach based on incremental clustering. Since potential fault scenarios can be multiple and often challenging to simulate or identify during training, the system is initially trained using known operational categories. Over time, the system adapts and evolves by incorporating new data, which can be assigned to existing categories or, in the case of new anomalies, form new categories. This incremental approach enables the system to enhance its performance over the years, adapting to new anomaly scenarios and ensuring precise and reliable monitoring of the cold head’s health. Full article

(This article belongs to the Special Issue Signal Processing and Sensing Technologies for Fault Diagnosis)

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21 pages, 11187 KiB

Open AccessArticle

Hybrid Multimodal Feature Fusion with Multi-Sensor for Bearing Fault Diagnosis

by Zhenzhong Xu, Xu Chen, Yilin Li and Jiangtao Xu

Sensors 2024, 24(6), 1792; https://doi.org/10.3390/s24061792 - 11 Mar 2024

Cited by 1 | Viewed by 849

Abstract

Aiming at the traditional single sensor vibration signal cannot fully express the bearing running state, and in the high noise background, the traditional algorithm is insufficient for fault feature extraction. This paper proposes a fault diagnosis algorithm based on multi-sensor and hybrid multimodal feature fusion to achieve high-precision fault diagnosis by leveraging the operating state information of bearings in a high-noise environment to the fullest extent possible. First, the horizontal and vertical vibration signals from two sensors are fused using principal component analysis, aiming to provide a more comprehensive description of the bearing’s operating condition, followed by data set segmentation. Following fusion, time-frequency feature maps are generated using a continuous wavelet transform for global time-frequency feature extraction. A first diagnostic model is then developed utilizing a residual neural network. Meanwhile, the feature data is normalized, and 28 time-frequency feature indexes are extracted. Subsequently, a second diagnostic model is constructed using a support vector machine. Lastly, the two diagnosis models are integrated to derive the final model through an ensemble learning algorithm fused at the decision level and complemented by a genetic algorithm solution to improve the diagnosis accuracy. Experimental results demonstrate the effectiveness of the proposed algorithm in achieving superior diagnostic performance with a 97.54% accuracy rate. Full article

(This article belongs to the Special Issue Signal Processing and Sensing Technologies for Fault Diagnosis)

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18 pages, 10062 KiB

Open AccessArticle

An Open-Circuit Fault Diagnosis Method for Three-Level Neutral Point Clamped Inverters Based on Multi-Scale Shuffled Convolutional Neural Network

by Yan Yan, Jiaqi Wu, Yanfei Cao, Bo Liu, Chen Li and Tingna Shi

Sensors 2024, 24(6), 1745; https://doi.org/10.3390/s24061745 - 07 Mar 2024

Viewed by 509

Abstract

This study constructs a power switching device open-circuit fault diagnosis model for a three-level neutral point clamped inverter based on the multi-scale shuffled convolutional neural network (MSSCNN) and extracts and classifies the fault information contained in the output current of inverters. The model employs depthwise separable convolution and channel shuffle techniques to improve diagnostic accuracy and reduce model complexity. The experimental results show that the new model has lower model complexity, better noise resistance and higher average diagnostic accuracy compared with fault diagnosis models based on CNN, ResNet, ShuffleNet V2 and Mobilenet V3 networks. Full article

(This article belongs to the Special Issue Signal Processing and Sensing Technologies for Fault Diagnosis)

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17 pages, 2589 KiB

Open AccessArticle

A Current Noise Cancellation Method Based on Fractional Linear Prediction for Bearing Fault Detection

by Kaijin Xu and Xiangjin Song

Sensors 2024, 24(1), 52; https://doi.org/10.3390/s24010052 - 21 Dec 2023

Viewed by 592

Abstract

The stator current in an induction motor contains a large amount of information, which is unrelated to bearing faults. This information is considered as the noise component for the detection of bearing faults. When there is noise information in the current signal, it can affect the detection of motor bearing faults and lead to the possibility of false alarms. Therefore, to accomplish an effective bearing fault detection, all or some of these noise components must be properly eliminated. This paper proposes the use of fractional linear prediction (FLP) as a noise elimination method in bearing fault diagnosis, which makes these noise components the predictable components and this bearing fault information the unpredictable components. The basis of the FLP is to eliminate noise components in the current signal by predicting predictable components through linear prediction theory and optimal prediction order. Meanwhile, this paper adopts the FLP model with limited memory samples. After determining the optimal number of memories, only the fractional derivative order parameter needs to be optimized, which greatly reduces the computational complexity and difficulty in parameter optimization. In addition, this paper uses spectral analysis of the current signals through experimental simulation to compare the FLP method with the linear prediction (LP) method and the time-shifting (TS) method that have been successfully applied to bearing fault diagnosis. Based on the analysis results, the FLP method can better extract fault features and achieve better bearing fault diagnosis results, verifying the effectiveness and superiority of the FLP method in the field of bearing fault diagnosis. Additionally, the predictive performance of thevFLP and LP was compared based on experimental data, verifying the advantages of the FLP method in predictive performance, indicating that this method has a better noise cancellation effect. Full article

(This article belongs to the Special Issue Signal Processing and Sensing Technologies for Fault Diagnosis)

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16 pages, 486 KiB

Open AccessArticle

Turbofan Engine Health Assessment Based on Spatial–Temporal Similarity Calculation

by Cheng Peng, Xin Hu and Zhaohui Tang

Sensors 2023, 23(24), 9748; https://doi.org/10.3390/s23249748 - 11 Dec 2023

Viewed by 668

Abstract

Aiming at the problem of the remaining useful life prediction accuracy being too low due to the complex operating conditions of the aviation turbofan engine data set and the original noise of the sensor, a residual useful life prediction method based on spatial–temporal similarity calculation is proposed. The first stage is adaptive sequence matching, which uses the constructed spatial–temporal trajectory sequence to match the sequence to find the optimal matching sample and calculate the similarity between the two spatial–temporal trajectory sequences. In the second stage, the weights of each part are assigned by the two weight allocation algorithms of the weight training module, and then the final similarity is calculated by the similarity calculation formula of the life prediction module, and the final predicted remaining useful life is determined according to the size of the similarity and the corresponding remaining life. Compared with a single model, the proposed method emphasizes the consistency of the test set and the training set, increases the similarity between samples by sequence matching with other spatial–temporal trajectories, and further calculates the final similarity and predicts the remaining use through the weight allocation module and the life prediction module. The experimental results show that compared with other methods, the root mean square error (RMSE) index and the remaining useful life health score (Score) index are reduced by 12.6% and 14.8%, respectively, on the FD004 dataset, and the RMSE index is similar to that in other datasets; the Score index is reduced by about 10%, which improves the prediction accuracy of the remaining useful life and can provide favorable support for the operation and maintenance decision of turbofan engines. Full article

(This article belongs to the Special Issue Signal Processing and Sensing Technologies for Fault Diagnosis)

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