Condition Monitoring and Fault Diagnosis for 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: 30 November 2024 | Viewed by 3149

Special Issue Editors


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Guest Editor
Institute of Rail Transit, Tongji University, Shanghai, China
Interests: dynamics; signal processing; machine learning
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Guest Editor
College of Mechanical Engineering, Donghua University, Shanghai, China
Interests: fault detection/diagnosis; remaining useful life prediction; maintenance scheduling optimization

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Guest Editor
Department of Mechanical Engineering, Northeast University, Shengyang, China
Interests: prognostics and health management; domain adaptation; machine learning
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Mechanical Engineering, Anhui Agricultural University, Hefei, China
Interests: mechanical fault diagnosis; prognostic and health management; signal processing

Special Issue Information

Dear Colleagues,

Rotating machinery, such as turbines, jet engines, compressors, and electric propulsion systems, comprises classical engineering units. Due to their dynamic nature and complex system structures, rotating machinery is prone to various faults, such as fatigue cracks, misalignment, eccentricities, and wear. The rapid detection and diagnosis of these faults, before they lead to a catastrophic failure, is necessary. Condition monitoring and fault diagnosis are essential for their reliability and safety. While industrial applied condition monitoring and fault diagnosis methodologies are limited to signal energy analysis and Fourier spectrum analysis, many sophisticated methodologies have been reported and developed. The recent advances in understanding the fault mechanism, condition monitoring, and fault diagnosis methodologies have been substantial. Many cutting-edge methodologies have been developed to obtain more accurate and trustworthy detection and diagnosis results.

This Special Issue aims to collect original research articles and reviews. The topics of interest include, but are not limited to:

  • Dynamics modeling;
  • Fault mechanism;
  • Condition monitoring techniques;
  • Signal processing methods;
  • Information fusion strategy;
  • Machine learning algorithms.

Dr. Yuejian Chen
Dr. Lei Xiao
Dr. Yongchao Zhang
Dr. Qing Li
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. Machines is an international peer-reviewed open access monthly 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

  • rotating machinery
  • condition monitoring
  • fault diagnosis
  • signal processing
  • machine learning

Published Papers (3 papers)

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Research

12 pages, 2429 KiB  
Article
An Analysis of the WPT Function for Pattern Optimization to Detect Defects in Bearings
by Marta Zamorano, María Jesús Gómez and Cristina Castejon
Machines 2024, 12(3), 207; https://doi.org/10.3390/machines12030207 - 20 Mar 2024
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Abstract
New trends in maintenance techniques are oriented to digitization and prognosis. The new electronic devices based on IoT (Internet of Things) technology among others that support the industry 4.0 paradigm let enhance the traditional condition monitoring techniques to better understand and predict the [...] Read more.
New trends in maintenance techniques are oriented to digitization and prognosis. The new electronic devices based on IoT (Internet of Things) technology among others that support the industry 4.0 paradigm let enhance the traditional condition monitoring techniques to better understand and predict the state of a machine in service. Related to maintenance applications, one of the important steps in condition monitoring tasks for fault diagnosis is the selection of the optimal pattern to provide accurate results (avoiding fault positives/negatives) with adequate computation time. When implementing this, the selection of optimal parameters and thresholds for setting alarms are important to detect problems in the machine before the failure occurs. Vibratory signals have been proved to be a good variable to determine their mechanical behavior. Nevertheless, parameters obtained from time domain measurements are not computationally efficient nor good patterns to compare different machine conditions. In this sense, tools that represent the frequency domain or time–frequency domain have been useful to detect defects in rotating elements such as bearings. In this work, defects in ball bearings are studied using wavelet packet transform. For this, a methodology will be developed for the optimal selection of the mother wavelet, incorporating intelligent classification systems, and using a medium Gaussian support vector machine model. In this way, it will be verified that the correct selection of this function influences both the results and the ease and reliability of detection. The results using the selected mother wavelet will be compared to those using Daubechies 6, since it is the mother wavelet that has been used in previous works and which was selected based on experience. For it, vibratory signals are obtained from a testbench with different bearing conditions: healthy bearings and defective bearings (inner and outer race). Full article
(This article belongs to the Special Issue Condition Monitoring and Fault Diagnosis for Rotating Machinery)
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22 pages, 793 KiB  
Article
Gearbox Condition Monitoring and Diagnosis of Unlabeled Vibration Signals Using a Supervised Learning Classifier
by Myung-Kyo Seo and Won-Young Yun
Machines 2024, 12(2), 127; https://doi.org/10.3390/machines12020127 - 11 Feb 2024
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Abstract
Data-based equipment fault detection and diagnosis is an important research area in the smart factory era, which began with the Fourth Industrial Revolution. Steel manufacturing is a typical processing industry, and efficient equipment operation can improve product quality and cost. Steel production systems [...] Read more.
Data-based equipment fault detection and diagnosis is an important research area in the smart factory era, which began with the Fourth Industrial Revolution. Steel manufacturing is a typical processing industry, and efficient equipment operation can improve product quality and cost. Steel production systems require precise control of the equipment, which is a complex process. A gearbox transmits power between shafts and is an essential piece of mechanical equipment. A gearbox malfunction can cause serious problems not only in production, quality, and delivery but in safety. Many researchers are developing methods for monitoring gearbox condition and for diagnosing failures in order to resolve problems. In most data-driven methods, the analysis data set is derived from a distribution of identical data with failure mode labels. Industrial sites, however, often collect data without information on the failure type or failure status due to varying operating conditions and periodic repair. Therefore, the data sets not only include frequent false alarms, but they cannot explain the causes of the alarms. In this paper, a framework called the Reduced Lagrange Method (R-LM) periodically assigns pseudolabels to vibration signals collected without labels and creates an input data set. In order to monitor the status of equipment and to diagnose failures, the input data set is fed into a supervised learning classifier. To verify the proposed method, we build a test rig using motors and gearboxes that are used on production sites in order to artificially simulate defects in the gears and to operate them to collect vibration data. Data features are extracted from the frequency domain and time domain, and pseudolabeling is applied. There were fewer false alarms when applying R-LM, and it was possible to explain which features were responsible for equipment status changes, which improved field applicability. It was possible to detect changes in equipment conditions before a catastrophic failure, thus providing meaningful alarm and warning information, as well as further promising research topics. Full article
(This article belongs to the Special Issue Condition Monitoring and Fault Diagnosis for Rotating Machinery)
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13 pages, 3171 KiB  
Article
A Deep-LSTM-Based Fault Detection Method for Railway Vehicle Suspensions
by Yuejian Chen, Xuemei Liu, Wenkun Fan, Ningyuan Duan and Kai Zhou
Machines 2024, 12(2), 116; https://doi.org/10.3390/machines12020116 - 07 Feb 2024
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Abstract
The timely detection of faults that occur in industrial machines and components can avoid possible catastrophic machine failure, prevent large financial losses, and ensure the safety of machine operators. A solution to tackle the fault detection problem is to start with modeling the [...] Read more.
The timely detection of faults that occur in industrial machines and components can avoid possible catastrophic machine failure, prevent large financial losses, and ensure the safety of machine operators. A solution to tackle the fault detection problem is to start with modeling the condition monitoring signals and then examine any deviation of real-time monitored data from the baseline model. The newly developed deep long short-term memory (LSTM) neural network has a high nonlinear flexibility and can simultaneously store long- and short-term memories. Thus, deep LSTM is a good option for representing underlying data-generating processes. This paper presents a deep-LSTM-based fault detection method. A goodness-of-fit criterion is innovatively used to quantify the deviation between the baseline model and the newly monitored vibrations as opposed to the mean squared value of the LSTM residual used in many reported works. A railway suspension fault detection case is studied. Benchmark studies have shown that the deep-LSTM-based fault detection method performs better than the vanilla-LSTM-based and linear-autoregression-model-based methods. Using the goodness-of-fit criterion, railway suspension faults can be better detected than when using the mean squared value of the LSTM residual. Full article
(This article belongs to the Special Issue Condition Monitoring and Fault Diagnosis for Rotating Machinery)
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