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Machinery Testing and Intelligent 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: closed (30 November 2022) | Viewed by 18080

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Special Issue Editor

Dr. Ai-jun Yin

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Guest Editor

College of Mechanical Enginerring, Chongqing University, Chongqing 400044, China
Interests: intelligent testing and instrumentation; big data and artificial intelligence; fault diagnosis and prediction; high-end equipment and machine vision

Special Issue Information

Dear Colleagues,

Fault diagnosis plays important role in reducing failure and downtime for equipment such as high-speed trains, wind turbines, and aircraft engines. Machinery testing and fault diagnosis have progressed over the past decades with the flourish of the Internet of Things (IOT) system and intelligent information technology (IIT). Based on fault mechanisms, machineries are monitored using various testing methods, such as vibration, acoustic emission, temperature, and current signature. The collected data are then analyzed to evaluate the operating conditions and trigger repair or replacement if there are symptoms of faults. The ever-growing complexity of machineries, as well as massive data, bring challenges for anomaly detection and fault identification. Intelligent fault diagnosis (IFD) uses artificial intelligence methods to assist feature extraction and decision making during the recognition of faults. It learns patterns from observations and can reduce the influence of human factors. IFD-based studies and applications have grown rapidly in recent years due to its potential in improving reliability and reducing maintenance costs.

This Special Issue of Sensors entitled "Machinery Testing and Intelligent Fault Diagnosis" welcomes submissions on topics including, but not limited to:

Machinery testing and condition monitoring;
Intelligent fault diagnosis methods;
Patter identification in fault diagnosis;
Signal processing and feature extraction;
Prognostic and health management (PHM) applications for condition monitoring and fault diagnosis.

Dr. Ai-jun Yin
Guest Editor

Manuscript Submission Information

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Keywords

condition monitoring
intelligent fault diagnosis
prognostic and health management
machinery testing

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Published Papers (7 papers)

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Research

10 pages, 4200 KiB

Open AccessArticle

Emissivity Correction and Thermal Pattern Reconstruction in Eddy Current Pulsed Thermography

by Kongjing Li, Gui Yun Tian and Junaid Ahmed

Sensors 2023, 23(5), 2646; https://doi.org/10.3390/s23052646 - 28 Feb 2023

Cited by 7 | Viewed by 2219

Abstract

Emissivity variations are one of the most critical challenges in thermography technologies; this is due to the temperature calculation strongly depending on emissivity settings for infrared signal extraction and evaluation. This paper describes an emissivity correction and thermal pattern reconstruction technique based on physical process modelling and thermal feature extraction, for eddy current pulsed thermography. An emissivity correction algorithm is proposed to address the pattern observation issues of thermography in both spatial and time domains. The main novelty of this method is that the thermal pattern can be corrected based on the averaged normalization of thermal features. In practice, the proposed method brings benefits in enhancing the detectability of the faults and characterization of the materials without the interference of the emissivity variation problem at the object’s surfaces. The proposed technique is verified in several experimental studies, such as the case-depth evaluation of heat-treatment steels, failures, and fatigues of gears made of the heat-treated steels that are used for rolling stock applications. The proposed technique can improve the detectability of the thermography-based inspection methods and would improve the inspection efficiency for high-speed NDT&E applications, such as rolling stock applications. Full article

(This article belongs to the Special Issue Machinery Testing and Intelligent Fault Diagnosis)

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15 pages, 6649 KiB

Open AccessArticle

Research on the Fault Diagnosis Method of an Internal Gear Pump Based on a Convolutional Auto-Encoder and PSO-LSSVM

by Jian Liao, Jianbo Zheng and Zongbin Chen

Sensors 2022, 22(24), 9841; https://doi.org/10.3390/s22249841 - 14 Dec 2022

Cited by 8 | Viewed by 1697

Abstract

The raw signals produced by internal gear pumps are susceptible to noises brought on by mechanical vibrations and the surrounding environment, and the sample count collected during the various operating periods is not distributed evenly. Accurately diagnosing faults in internal gear pumps is significantly complicated by these factors. In light of these issues, accelerated life testing was performed in order to collect signals from an internal gear pump during various operating periods. Based on the architecture of a convolutional auto-encoder network, preprocessing of the signals in the various operating periods was performed to suppress noise and enhance operating period-representing features. Thereafter, variational mode decomposition was utilized to decompose the preprocessed signal into multiple intrinsic mode functions, and the multi-scale permutation entropy value was extracted for each intrinsic mode function to form a feature set. The feature set was subsequently divided into a training set and a test set, with the training set being trained to utilize a particle swarm optimization–least squares support vector machine network. For pattern recognition, the test set samples were fed into the trained model. The results demonstrated a 99.2% diagnostic accuracy. Compared to other methods of fault diagnosis, the proposed method is more effective and accurate. Full article

(This article belongs to the Special Issue Machinery Testing and Intelligent Fault Diagnosis)

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20 pages, 5781 KiB

Open AccessArticle

Experimental Verification of the Impact of Radial Internal Clearance on a Bearing’s Dynamics

by Bartłomiej Ambrożkiewicz, Arkadiusz Syta, Anthimos Georgiadis, Alexander Gassner and Nicolas Meier

Sensors 2022, 22(17), 6366; https://doi.org/10.3390/s22176366 - 24 Aug 2022

Cited by 4 | Viewed by 1747

Abstract

This paper focuses on the influence of radial internal clearance on the dynamics of a rolling-element bearing. In the beginning, the 2—Degree of Freedom (DOF) model was studied, in which the clearance was treated as a bifurcation parameter. The derived nonlinear mathematical model is based on Hertzian contact theory and takes into consideration shape errors of rolling surfaces and eccentricity reflecting real operating conditions. The analysis showed characteristic dynamical behavior by specific clearance range, which reflects others in a low or high amplitude and can refer to the optimal clearance. The experimental validation was conducted with the use of a double row self-aligning ball bearing (SABB) NTN 2309SK in which the acceleration response was measured by various rotational velocities. The time series obtained from the mathematical model and the experiment were analyzed with the recurrence quantification analysis. Full article

(This article belongs to the Special Issue Machinery Testing and Intelligent Fault Diagnosis)

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24 pages, 10263 KiB

Open AccessArticle

Bearing Fault Diagnosis Using Lightweight and Robust One-Dimensional Convolution Neural Network in the Frequency Domain

by Mohammed Hakim, Abdoulhadi A. Borhana Omran, Jawaid I. Inayat-Hussain, Ali Najah Ahmed, Hamdan Abdellatef, Abdallah Abdellatif and Hassan Muwafaq Gheni

Sensors 2022, 22(15), 5793; https://doi.org/10.3390/s22155793 - 3 Aug 2022

Cited by 21 | Viewed by 2714

Abstract

The massive environmental noise interference and insufficient effective sample degradation data of the intelligent fault diagnosis performance methods pose an extremely concerning issue. Realising the challenge of developing a facile and straightforward model that resolves these problems, this study proposed the One-Dimensional Convolutional Neural Network (1D-CNN) based on frequency-domain signal processing. The Fast Fourier Transform (FFT) analysis is initially utilised to transform the signals from the time domain to the frequency domain; the data was represented using a phasor notation, which separates magnitude and phase and then fed to the 1D-CNN. Subsequently, the model is trained with White Gaussian Noise (WGN) to improve its robustness and resilience to noise. Based on the findings, the proposed model successfully achieved 100% classification accuracy from clean signals and simultaneously achieved considerable robustness to noise and exceptional domain adaptation ability. The diagnosis accuracy retained up to 97.37%, which was higher than the accuracy of the CNN without training under noisy conditions at only 43.75%. Furthermore, the model achieved an accuracy of up to 98.1% under different working conditions, which was superior to other reported models. In addition, the proposed model outperformed the state-of-art methods as the Signal-to-Noise Ratio (SNR) was lowered to −10 dB achieving 97.37% accuracy. In short, the proposed 1D-CNN model is a promising effective rolling bearing fault diagnosis. Full article

(This article belongs to the Special Issue Machinery Testing and Intelligent Fault Diagnosis)

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15 pages, 4328 KiB

Open AccessArticle

Weak Fault Feature Extraction of Rolling Bearings Based on Adaptive Variational Modal Decomposition and Multiscale Fuzzy Entropy

by Zhongliang Lv, Senping Han, Linhao Peng, Lin Yang and Yujiang Cao

Sensors 2022, 22(12), 4504; https://doi.org/10.3390/s22124504 - 14 Jun 2022

Cited by 6 | Viewed by 2141

Abstract

The working environment of rotating machines is complex, and their key components are prone to failure. The early fault diagnosis of rolling bearings is of great significance; however, extracting the single scale fault feature of the early weak fault of rolling bearings is not enough to fully characterize the fault feature information of a weak signal. Therefore, aiming at the problem that the early fault feature information of rolling bearings in a complex environment is weak and the important parameters of Variational Modal Decomposition (VMD) depend on engineering experience, a fault feature extraction method based on the combination of Adaptive Variational Modal Decomposition (AVMD) and optimized Multiscale Fuzzy Entropy (MFE) is proposed in this study. Firstly, the correlation coefficient is used to calculate the correlation between the modal components decomposed by VMD and the original signal, and the threshold of the correlation coefficient is set to optimize the selection of the modal number

K

. Secondly, taking Skewness (Ske) as the objective function, the parameters of MFE embedding dimension M, scale factor S and time delay T are optimized by the Particle Swarm Optimization (PSO) algorithm. Using optimized MFE to calculate the modal components obtained by AVMD, the MFE feature vector of each frequency band is obtained, and the MFE feature set is constructed. Finally, the simulation signals are used to verify the effectiveness of the Adaptive Variational Modal Decomposition, and the Drivetrain Dynamics Simulator (DDS) are used to complete the comparison test between the proposed method and the traditional method. The experimental results show that this method can effectively extract the fault features of rolling bearings in multiple frequency bands, characterize more weak fault information, and has higher fault diagnosis accuracy. Full article

(This article belongs to the Special Issue Machinery Testing and Intelligent Fault Diagnosis)

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18 pages, 3553 KiB

Open AccessArticle

Multi-Sensor Fusion by CWT-PARAFAC-IPSO-SVM for Intelligent Mechanical Fault Diagnosis

by Hanxin Chen and Shaoyi Li

Sensors 2022, 22(10), 3647; https://doi.org/10.3390/s22103647 - 10 May 2022

Cited by 35 | Viewed by 2856

Abstract

A new method of multi-sensor signal analysis for fault diagnosis of centrifugal pump based on parallel factor analysis (PARAFAC) and support vector machine (SVM) is proposed. The single-channel vibration signal is analyzed by Continuous Wavelet Transform (CWT) to construct the time–frequency representation. The multiple time–frequency data are used to construct the three-dimension data matrix. The 3-level PARAFAC method is proposed to decompose the data matrix to obtain the six features, which are the time domain signal (mode 3) and frequency domain signal (mode 2) of each level within the three-level PARAFAC. The eighteen features from three direction vibration signals are used to test the data processing capability of the algorithm models by the comparison among the CWT-PARAFAC-IPSO-SVM, WPA-PSO-SVM, WPA-IPSO-SVM, and CWT-PARAFAC-PSO-SVM. The results show that the multi-channel three-level data decomposition with PARAFAC has better performance than WPT. The improved particle swarm optimization (IPSO) has a great improvement in the complexity of the optimization structure and running time compared to the conventional particle swarm optimization (PSO.) It verifies that the proposed CWT-PARAFAC-IPSO-SVM is the most optimal hybrid algorithm. Further, it is characteristic of its robust and reliable superiority to process the multiple sources of big data in continuous condition monitoring in the large-scale mechanical system. Full article

(This article belongs to the Special Issue Machinery Testing and Intelligent Fault Diagnosis)

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14 pages, 6331 KiB

Open AccessArticle

Evaluation of Different Bearing Fault Classifiers in Utilizing CNN Feature Extraction Ability

by Wenlang Xie, Zhixiong Li, Yang Xu, Paolo Gardoni and Weihua Li

Sensors 2022, 22(9), 3314; https://doi.org/10.3390/s22093314 - 26 Apr 2022

Cited by 29 | Viewed by 3459

Abstract

In aerospace, marine, and other heavy industries, bearing fault diagnosis has been an essential part of improving machine life, reducing economic losses, and avoiding safety problems caused by machine bearing failures. Most existing bearing fault diagnosis methods face challenges in extracting the fault features from raw bearing fault data. Compared with traditional methods for bearing fault characteristics extraction, deep neural networks can automatically extract intrinsic features without expert knowledge. The convolutional neural network (CNN) was utilized most widely in extracting representative features of bearing faults. Fundamental to this, the hybrid models based on the CNN and individual classifiers were proposed to diagnose bearing faults. However, CNN may not be suitable for all bearing fault classifiers. It is crucial to identify the classifiers which can maximize the CNN feature extraction ability. In this paper, four hybrid models based on CNN were built, and their fault detection accuracy and efficiency were compared. The comparative analysis showed that the random forest (RF) and support vector machine (SVM) could make full use of the CNN feature extraction ability. Full article

(This article belongs to the Special Issue Machinery Testing and Intelligent Fault Diagnosis)

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