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Machines
Special Issues
Machine Learning Based Predictive Maintenance and Condition Monitoring
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Machine Learning Based Predictive Maintenance and Condition Monitoring

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 934

Special Issue Editor

Prof. Dr. Wei Cheng

E-Mail Website
Guest Editor
School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Interests: predictive maintenance and health management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the advent of advanced data analytics and machine learning algorithms, industries across various sectors are leveraging these technologies to optimize maintenance practices and enhance operational safety. Machine learning has become an essential tool for predictive maintenance and condition monitoring. It enables the proactive identification of potential equipment failures, facilitates data-driven decision making, reduces downtime, and optimizes maintenance schedules, which can both enhance operational safety and reduce maintenance costs.

This Special Issue aims to explore the state of the art in machine learning for predictive maintenance and condition monitoring. We encourage the submission of original research articles, reviews, and short communications focused on the integration of machine learning into predictive maintenance strategies. Topics of interest for this Special Issue include, but are not limited to, the following:

  1. Industrial big data analysis and data mining;
  2. Intelligent fault detection and diagnosis of machines;
  3. Prognostics and health management of mechanical systems;
  4. Real-time condition monitoring and anomaly detection;
  5. Deep learning approaches for predictive maintenance;
  6. Industrial applications of machine learning-based maintenance strategies;
  7. Intelligent decision making for maintenance optimization;
  8. Predictive and forecasting techniques for equipment reliability;
  9. Sensor fusion and data preprocessing techniques in predictive maintenance.

Prof. Dr. Wei Cheng
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • machine learning
  • predictive maintenance
  • fault diagnosis
  • condition monitoring
  • health management

Published Papers (1 paper)

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Research

23 pages, 7868 KiB  
Article
An Advanced Diagnostic Approach for Broken Rotor Bar Detection and Classification in DTC Controlled Induction Motors by Leveraging Dynamic SHAP Interaction Feature Selection (DSHAP-IFS) GBDT Methodology
by Muhammad Amir Khan, Bilal Asad, Toomas Vaimann and Ants Kallaste
Machines 2024, 12(7), 495; https://doi.org/10.3390/machines12070495 - 22 Jul 2024
Viewed by 453
Abstract
This paper introduces a sophisticated approach for identifying and categorizing broken rotor bars in direct torque-controlled (DTC) induction motors. DTC is implemented in industrial drive systems as a suitable control method to preserve torque control performance, which sometimes shows its impact on fault-representing [...] Read more.
This paper introduces a sophisticated approach for identifying and categorizing broken rotor bars in direct torque-controlled (DTC) induction motors. DTC is implemented in industrial drive systems as a suitable control method to preserve torque control performance, which sometimes shows its impact on fault-representing frequencies. This is because of the DTC’s closed-loop control nature, whichtriesto reduce speed and torque ripples by changing the voltage profile. The proposed model utilizes the modified Shapley Additive exPlanations (SHAP) technique in combination with gradient-boosting decision trees (GBDT) to detect and classify the abnormalities in BRBs at diverse (0%, 25%, 50%, 75%, and 100%) loading conditions. To prevent overfitting of the proposed model, we used the adaptive fold cross-validation (AF-CV) technique, which can dynamically adjust the number of folds during the optimization process. By employing extensive feature engineering in the original dataset and then applying Shapely Additive exPlanations(SHAP)-based feature selection, our methodology effectively identifies informative features from signals (three-phase current, three-phase voltage, torque, and speed) and motor characteristics. The gradient-boosting decision tree (GBDT) classifier, trained using the given characteristics, extracts consistent and reliable classification performance under different loading circumstances and enables precise and accurate detection and classification of broken rotor bars. The proposed approach (SHAP-Fusion GBDT with AF-CV) is a major advancement in the field of machine learning in detecting motor anomalies at varying loading conditions and proved to be an effective mechanism for preventative maintenance and preventing faults in DTC-controlled induction motors byattaining an accuracy rate of 99% for all loading conditions. Full article
(This article belongs to the Special Issue Machine Learning Based Predictive Maintenance and Condition Monitoring)
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