Artificial Intelligence for Fault Diagnosis of Rotating Machinery

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 15701

Special Issue Editors


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Guest Editor
Department of Computer Science, Politecnico di Milano, 20133 Milan, Italy
Interests: machine learning; deep learning; predictive maintenance
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Guest Editor
Department of Ingegneria Gestionale, Politecnico di Milano, 20133 Milan, Italy
Interests: machine learning, in particular deep learning, support vector machines, nonlinear dimensionality reduction, sentiment analysis

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Guest Editor
Department of Electrical, Electronics, and Computer Engineering, University of Ulsan, Ulsan 680749, Korea
Interests: condition monitoring of engineering systems using signal processing and ML/DL-based techniques

Special Issue Information

Dear Colleagues,

Various types of rotating machinery are recognized as some of the most widely used types of equipment in different industrial fields, such as manufacturing and energy production. With the rapid development of technologies and growing demands for fast industrialization, rotating machinery has become more advanced, more precise, and bigger, which inevitably leads to an increase in their construction complexity. As a result, many types of failures may occur during their operation, leading to unexpected breakdowns in the industrial systems that disrupt the manufacturing pipelines and lead to tremendous economic losses and even worker casualties. To avoid machine breakdown and its consequences, it is essential to develop and deploy robust and reliable approaches for the condition monitoring of these machines to increase their efficacy and provide timely maintenance.

The condition monitoring of complex engineering systems is of high importance and is a fast-growing research field. The convergence of artificial intelligence techniques and the field of condition monitoring allows researchers and industrial professionals to solve complex problems for predictive health maintenance of rotating machines, such as extracting features sensitive to their degradation from time-series data, selecting the most valuable features, and based on them, not only detecting the appearance of the faults but also differentiating the exact types of the faults within and estimating the remaining useful lifetime of the machine. Furthermore, advances in artificial intelligence provide the tools and foundations for creating fascinating data-driven end-to-end solutions for predictive health maintenance of engineering systems in general and rotating machines specifically.

This Special Issue aims at attracting researchers and industrial professionals to investigate and present recent advances and techniques addressing the problems of rotating machinery condition monitoring.

Prof. Carlotta Orsenigo
Prof. Carlo Vercellis
Dr. Prosvirin Alexander
Prof. Jongmyon Kim
Guest Editors

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Keywords

  • artificial intelligence
  • condition monitoring
  • deep learning
  • fault diagnosis and prognosis
  • machine learning
  • predictive health maintenance
  • rotating machinery
  • signal processing

Published Papers (4 papers)

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Research

22 pages, 9776 KiB  
Article
An Improved Convolutional-Neural-Network-Based Fault Diagnosis Method for the Rotor–Journal Bearings System
by Honglin Luo, Lin Bo, Chang Peng and Dongming Hou
Machines 2022, 10(7), 503; https://doi.org/10.3390/machines10070503 - 22 Jun 2022
Cited by 4 | Viewed by 2087
Abstract
More layers in a convolution neural network (CNN) means more computational burden and longer training time, resulting in poor performance of pattern recognition. In this work, a simplified global information fusion convolution neural network (SGIF-CNN) is proposed to improve computational efficiency and diagnostic [...] Read more.
More layers in a convolution neural network (CNN) means more computational burden and longer training time, resulting in poor performance of pattern recognition. In this work, a simplified global information fusion convolution neural network (SGIF-CNN) is proposed to improve computational efficiency and diagnostic accuracy. In the improved CNN architecture, the feature maps of all the convolutional and pooling layers are globally convoluted into a corresponding one-dimensional feature sequence, and then all the feature sequences are concatenated into the fully connected layer. On this basis, this paper further proposes a novel fault diagnosis method for a rotor–journal bearing system based on SGIF-CNN. Firstly, the time-frequency distributions of samples are obtained using the Adaptive Optimal-Kernel Time–Frequency Representation algorithm (AOK-TFR). Secondly, the time–frequency diagrams of the training samples are utilized to train the SGIF-CNN model using a shallow information fusion method, and the trained SGIF-CNN model can be tested using the time–frequency diagrams of the testing samples. Finally, the trained SGIF-CNN model is transplanted to the equipment’s online monitoring system to monitor the equipment’s operating conditions in real time. The proposed method is verified using the data from a rotor test rig and an ultra-scale air separator, and the analysis results show that the proposed SGIF-CNN improves the computing efficiency compared to the traditional CNN while ensuring the accuracy of the fault diagnosis. Full article
(This article belongs to the Special Issue Artificial Intelligence for Fault Diagnosis of Rotating Machinery)
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22 pages, 4029 KiB  
Article
Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinery
by Masoud Jalayer, Amin Kaboli, Carlotta Orsenigo and Carlo Vercellis
Machines 2022, 10(4), 237; https://doi.org/10.3390/machines10040237 - 28 Mar 2022
Cited by 14 | Viewed by 4472
Abstract
Fault diagnosis plays an essential role in reducing the maintenance costs of rotating machinery manufacturing systems. In many real applications of fault detection and diagnosis, data tend to be imbalanced, meaning that the number of samples for some fault classes is much less [...] Read more.
Fault diagnosis plays an essential role in reducing the maintenance costs of rotating machinery manufacturing systems. In many real applications of fault detection and diagnosis, data tend to be imbalanced, meaning that the number of samples for some fault classes is much less than the normal data samples. At the same time, in an industrial condition, accelerometers encounter high levels of disruptive signals and the collected samples turn out to be heavily noisy. As a consequence, many traditional Fault Detection and Diagnosis (FDD) frameworks get poor classification performances when dealing with real-world circumstances. Three main solutions have been proposed in the literature to cope with this problem: (1) the implementation of generative algorithms to increase the amount of under-represented input samples, (2) the employment of a classifier being powerful to learn from imbalanced and noisy data, (3) the development of an efficient data preprocessing including feature extraction and data augmentation. This paper proposes a hybrid framework which uses the three aforementioned components to achieve an effective signal based FDD system for imbalanced conditions. Specifically, it first extracts the fault features, using Fourier and wavelet transforms to make full use of the signals. Then, it employs Wasserstein Generative Adversarial with Gradient Penalty Networks (WGAN-GP) to generate synthetic samples to populate the rare fault class and enrich the training set. Moreover, to achieve a higher performance a novel combination of Convolutional Long Short-term Memory (CLSTM) and Weighted Extreme Learning Machine (WELM) is also proposed. To verify the effectiveness of the developed framework, different bearing datasets settings on different imbalance severities and noise degrees were used. The comparative results demonstrate that in different scenarios GAN-CLSTM-ELM significantly outperforms the other state-of-the-art FDD frameworks. Full article
(This article belongs to the Special Issue Artificial Intelligence for Fault Diagnosis of Rotating Machinery)
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15 pages, 6621 KiB  
Article
A Bearing Fault Diagnosis Method Using Multi-Branch Deep Neural Network
by Van-Cuong Nguyen, Duy-Tang Hoang, Xuan-Toa Tran, Mien Van and Hee-Jun Kang
Machines 2021, 9(12), 345; https://doi.org/10.3390/machines9120345 - 9 Dec 2021
Cited by 25 | Viewed by 3105
Abstract
Feature extraction from a signal is the most important step in signal-based fault diagnosis. Deep learning or deep neural network (DNN) is an effective method to extract features from signals. In this paper, a novel vibration signal-based bearing fault diagnosis method using DNN [...] Read more.
Feature extraction from a signal is the most important step in signal-based fault diagnosis. Deep learning or deep neural network (DNN) is an effective method to extract features from signals. In this paper, a novel vibration signal-based bearing fault diagnosis method using DNN is proposed. First, the measured vibration signals are transformed into a new data form called multiple-domain image-representation. By this transformation, the task of signal-based fault diagnosis is transferred into the task of image classification. After that, a DNN with a multi-branch structure is proposed to handle the multiple-domain image representation data. The multi-branch structure of the proposed DNN helps to extract features in multiple domains simultaneously, and to lead to better feature extraction. Better feature extraction leads to a better performance of fault diagnosis. The effectiveness of the proposed method was verified via the experiments conducted with actual bearing fault signals and its comparisons with well-established published methods. Full article
(This article belongs to the Special Issue Artificial Intelligence for Fault Diagnosis of Rotating Machinery)
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16 pages, 31912 KiB  
Article
2D CNN-Based Multi-Output Diagnosis for Compound Bearing Faults under Variable Rotational Speeds
by Minh-Tuan Pham, Jong-Myon Kim and Cheol-Hong Kim
Machines 2021, 9(9), 199; https://doi.org/10.3390/machines9090199 - 14 Sep 2021
Cited by 27 | Viewed by 3936
Abstract
Bearings prevent damage caused by frictional forces between parts supporting the rotation and they keep rotating shafts in their correct position. However, the continuity of work under harsh conditions leads to inevitable bearing failure. Thus, methods for bearing fault diagnosis (FD) that can [...] Read more.
Bearings prevent damage caused by frictional forces between parts supporting the rotation and they keep rotating shafts in their correct position. However, the continuity of work under harsh conditions leads to inevitable bearing failure. Thus, methods for bearing fault diagnosis (FD) that can predict and categorize fault type, as well as the level of degradation, are increasingly necessary for factories. Owing to the advent of deep neural networks, especially convolutional neural networks (CNNs), intelligent FD methods have achieved significantly higher performance in terms of accuracy. However, in addition to accuracy, the efficiency issue still needs to be weathered in complicated diagnosis scenarios to adapt to real industrial environments. Here, we introduce a method based on multi-output classification, which utilizes the correlated features extracted for bearing compound fault type classification and crack-size classification to serve both aims. Additionally, the synergy of a time–frequency signal processing method and the proposed two-dimensional CNN helped the method perform well under the condition of variable rotational speeds. Monitoring signals of acoustic emission also had advantages for incipient FD. The experimental results indicated that utilizing correlated features in multi-output classification improved both the accuracy and efficiency of multi-task diagnosis compared to conventional CNN-based multiclass classification. Full article
(This article belongs to the Special Issue Artificial Intelligence for Fault Diagnosis of Rotating Machinery)
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