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Applied Sciences
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Intelligent Fault Diagnosis of Rotating Machinery
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Intelligent Fault Diagnosis of Rotating Machinery

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (28 March 2023) | Viewed by 6034

Special Issue Editors

Dr. Laifa Tao

E-Mail Website
Guest Editor
Institute of Reliability Engineering, Beihang University, Beijing 100191, China
Interests: machine learning; failure prognostics; fault diagnosis
Dr. Jie Liu

E-Mail Website
Guest Editor
School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
Interests: machine learning; failure prognostics; fault diagnosis
Special Issues, Collections and Topics in MDPI journals
Dr. Shuai Zhao

E-Mail Website
Guest Editor
AAU Energy, Aalborg University, 9220 Aalborg, Denmark
Interests: physics-informed machine learning; electronics reliability; condition monitoring; remaining useful life

Special Issue Information

Dear Colleagues,

Diverse rotating machineries, as crucial entities, are widely integrated in high-tech equipment of the Industry 4.0 era. Their performance is critical for the safety, precision, and availability of high-tech equipment. Timely diagnosing abnormal behavior and identifying fault locations are essential for in-service operation. To date, many new concepts, state-of-the-art deep learning tools, and smart IoT hardware have emerged and demonstrated great potential for intelligent fault diagnosis of rotating machinery.

This Special Issue will collect all research on intelligent fault diagnosis methods and applications in rotating machineries, including (but not limited to):

  • Advanced sensing and perception;
  • Advanced signal processing and deep feature mining;
  • Knowledge discovery;
  • Incipient anomaly detection;
  • Deep-learning-assisted methods in fault diagnosis;
  • Unbalanced dataset and mitigation methods;
  • Transfer learning and domain adaption;
  • Open-source datasets and dissemination;
  • Adaptive and online learning;
  • Physics-informed machine learning and hybrid methods;
  • Digital-twin-based fault diagnosis;
  • Failure prognosis;
  • Hardware and IoT system for intelligent fault diagnosis;
  • New applications of rotating machinery diagnosis.

Dr. Laifa Tao
Dr. Jie Liu
Dr. Shuai Zhao
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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.

Keywords

  • machine learning
  • artificial intelligence
  • digital twin (modeling and simulation)
  • data-driven modeling
  • physics-informed modeling
  • pattern recognition
  • condition monitoring
  • fault diagnosis
  • health assessment
  • prognostics

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Published Papers (3 papers)

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Research

15 pages, 2676 KiB  
Article
RFR-GA-BLS: A Feature Selection and Parameter Optimization Method for Fault Diagnosis of Rolling Bearing Using Infrared Images
by Jianmin Zhou, Lulu Liu, Xiwen Shen and Xiaotong Yang
Appl. Sci. 2023, 13(13), 7350; https://doi.org/10.3390/app13137350 - 21 Jun 2023
Cited by 3 | Viewed by 1111
Abstract
To overcome the problems of low machine learning fault diagnosis rate and long consumption time of deep learning in rolling bearing fault diagnosis, an RFR-GA-BLS model is proposed. The model is validated by infrared images of rolling bearings to find the most representative [...] Read more.
To overcome the problems of low machine learning fault diagnosis rate and long consumption time of deep learning in rolling bearing fault diagnosis, an RFR-GA-BLS model is proposed. The model is validated by infrared images of rolling bearings to find the most representative features, the most suitable parameters and the best diagnostic rate. Based on the pre-processed infrared thermal images of the faulty bearing, 72 second-order statistical features were obtained as information for fault diagnosis. RFR considered the robustness of the features, and new sequences were obtained. BLS was optimized by GA for fault diagnosis. New sequence features were added to the model sequentially, one at a time. After satisfying the model conditions, the most appropriate number of features was selected as the first 20. The search results for the number of feature nodes, the number of feature node windows and the number of enhancement nodes for the BLS were 24, 19 and 544, respectively, and the fault diagnosis rate of 98.8889% was achieved. According to a comparison with CFR-GA-BLS, BLS, PSO-BLS and Grdy-BLS, our proposed model is more advantageous in the search for the best performance. The fault diagnosis accuracy is higher compared to SVM and RF. The speed of our proposed model is 207 times faster than 1DCNN and 10,147 times faster than 2DCNN. Full article
(This article belongs to the Special Issue Intelligent Fault Diagnosis of Rotating Machinery)
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35 pages, 27966 KiB  
Article
A Comparison of Signal Analysis Techniques for the Diagnostics of the IMS Rolling Element Bearing Dataset
by Diletta Sacerdoti, Matteo Strozzi and Cristian Secchi
Appl. Sci. 2023, 13(10), 5977; https://doi.org/10.3390/app13105977 - 12 May 2023
Cited by 8 | Viewed by 2510
Abstract
In this paper, a comparison of signal analysis techniques for the diagnostics of rolling element bearings is carried out. Specifically, the comparison is performed in terms of fault detection, diagnosis and prognosis techniques with regards to the first rolling element bearing dataset released [...] Read more.
In this paper, a comparison of signal analysis techniques for the diagnostics of rolling element bearings is carried out. Specifically, the comparison is performed in terms of fault detection, diagnosis and prognosis techniques with regards to the first rolling element bearing dataset released by NASA IMS Center in 2014. As for fault detection, it is obtained that RMS value, Kurtosis and Detectivity, as statistical parameters, are able to properly detect the arising of the fault on the defective bearings. Then, several signal processing techniques, such as deterministic/random signal separation, time-frequency and cyclostationary analyses are applied to perform fault diagnosis. Among these techniques, it is found that the combination of Cepstrum Pre-Whitening and Squared Envelope Spectrum, and Improved Envelope Spectrum, allow the faults to be correctly identified on specific bearing components. Finally, the Correlation, Monotonicity and Robustness of the previous statistical parameters are computed to identify the most accurate tools for bearing fault prognosis. Full article
(This article belongs to the Special Issue Intelligent Fault Diagnosis of Rotating Machinery)
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21 pages, 12256 KiB  
Article
Semi-Supervised Adversarial Transfer Networks for Cross-Domain Intelligent Fault Diagnosis of Rolling Bearings
by Baisong Pan, Wuyan Wang, Juan Wen and Yifan Li
Appl. Sci. 2023, 13(4), 2626; https://doi.org/10.3390/app13042626 - 17 Feb 2023
Cited by 1 | Viewed by 1943
Abstract
In recent advances, deep learning-based methods have been broadly applied in fault diagnosis, while most existing studies assume that source domain and target domain data follow the same distribution. As differences in operating conditions lead to the deterioration of diagnosis performance, domain adaptation [...] Read more.
In recent advances, deep learning-based methods have been broadly applied in fault diagnosis, while most existing studies assume that source domain and target domain data follow the same distribution. As differences in operating conditions lead to the deterioration of diagnosis performance, domain adaptation technology has been introduced to bridge the distribution gap. However, most existing approaches generally assume that source domain labels are available under all health conditions during training, which is incompatible with the actual industrial situation. To this end, this paper proposes a semi-supervised adversarial transfer networks for cross-domain intelligent fault diagnosis of rolling bearings. Firstly, the Gramian Angular Field method is introduced to convert time domain vibration signals into images. Secondly, a semi-supervised learning-based label generating module is designed to generate artificial labels for unlabeled images. Finally, the dynamic adversarial transfer network is proposed to extract the domain-invariant features of all signal images and provide reliable diagnosis results. Two case studies were conducted on public rolling bearing datasets to evaluate the diagnostic performance. An experiment under variable operating conditions and an experiment with different numbers of source domain labels were carried out to verify the generalization and robustness of the proposed approach, respectively. Experiment results demonstrate that the proposed method can achieve high diagnosis accuracy when dealing with cross-domain tasks with deficient source domain labels, which may be more feasible in engineering applications than conventional methodologies. Full article
(This article belongs to the Special Issue Intelligent Fault Diagnosis of Rotating Machinery)
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