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Machines
Special Issues
Condition Monitoring and Fault Diagnosis
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Condition Monitoring and Fault Diagnosis

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 1393

Special Issue Editors

Prof. Dr. Yancai Xiao

E-Mail Website
Guest Editor
School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China
Interests: condition monitoring; fault diagnosis
Dr. Tianyang Wang

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
Interests: fault diagnosis and signal analysis of large rotating machinery such as wind turbines and aeroengines
Dr. Shaodan Zhi

E-Mail Website
Guest Editor
School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China
Interests: intelligent detection; fault diagnosis of machines

Special Issue Information

Dear Colleagues,

Condition monitoring and fault diagnosis techniques for machines and equipment have witnessed substantial advancements in recent decades, driven by the increasing demands for enhanced reliability, efficiency, and safety in industrial operations. Condition monitoring of valuable and high-cost machinery is crucial for performance tracking, reducing maintenance costs, boosting efficiency and reliability, and minimizing mechanical failures. Fault diagnosis represents the advanced analysis and interpretation of monitoring data. It has a wide range of applications and is of immense significance for equipment management.

This is a call for papers for a Special Issue on "Condition Monitoring and Fault Diagnosis". This Special Issue will provide a venue for scholars and researchers to share their most recent theoretical and technical successes, as well as to highlight key topics and difficulties for future study in the field. The submitted papers are expected to provide original ideas and potential theoretical and practical contributions. The following research topics are included, but not limited to:

  • Predictive maintenance and health management of equipment.
  • Equipment condition monitoring and intelligent maintenance.
  • Analysis of complex non-stationary signals in electromechanical systems.
  • Intelligent perception and fault diagnosis.
  • Mechanical system dynamics and failure simulation.

Prof. Dr. Yancai Xiao
Dr. Tianyang Wang
Dr. Shaodan Zhi
Guest Editors

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

  • condition monitoring
  • fault diagnosis
  • predictive maintenance
  • health management
  • failure simulation

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

34 pages, 15651 KiB  
Article
Intelligent Diagnosis of Rolling Element Bearings Under Various Operating Conditions Using an Enhanced Envelope Technique and Transfer Learning
by Ali Davoodabadi, Mehdi Behzad, Hesam Addin Arghand, Somaye Mohammadi and Len Gelman
Machines 2025, 13(5), 351; https://doi.org/10.3390/machines13050351 - 23 Apr 2025
Abstract
Rolling element bearings (REBs) are vital in rotating machinery, making fault detection essential for optimal performance and system reliability. This study assesses the effectiveness of a simple convolutional neural network (SCNN) and a transfer learning-based convolutional neural network (TL-CNN) for diagnosing REB faults [...] Read more.
Rolling element bearings (REBs) are vital in rotating machinery, making fault detection essential for optimal performance and system reliability. This study assesses the effectiveness of a simple convolutional neural network (SCNN) and a transfer learning-based convolutional neural network (TL-CNN) for diagnosing REB faults using time-domain signals, frequency-domain spectra, and envelope frequency spectrum analysis. The study uses diverse datasets, including laboratory and industrial data under various operating conditions, covering fault types like inner race fault (IRF), outer race fault (ORF), rolling element fault (REF), and healthy (H) states. The main innovation is applying Transfer Learning (TL) with fine-tuning to improve model accuracy in identifying REB conditions by leveraging features learned from diverse datasets. An innovative algorithm is also introduced to identify resonance regions for optimal filter selection in envelope analysis, improving fault-related feature extraction and reducing noise. A preprocessing step that removes speed-related variations further enhances model accuracy by isolating fault features and minimizing the impact of rotational speed. The results show that transfer learning with fine-tuning, combined with the resonance region identification algorithm, significantly enhances fault detection accuracy. The TL-CNN model with envelope signal input achieves the highest accuracy across all scenarios, especially under variable operating conditions, and performs reliably on industrial data. Full article
(This article belongs to the Special Issue Condition Monitoring and Fault Diagnosis)
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15 pages, 5515 KiB  
Article
Combining Sensor Fusion and a Machine Learning Framework for Accurate Tool Wear Prediction During Machining
by Swathi Kotha Amarnath, Vamsi Inturi, Sabareesh Geetha Rajasekharan and Amrita Priyadarshini
Machines 2025, 13(2), 132; https://doi.org/10.3390/machines13020132 - 10 Feb 2025
Viewed by 761
Abstract
Effective cutting tool condition monitoring (TCM) is critical for achieving precision, cost efficiency, and minimizing unplanned downtime. This study proposes a sophisticated sensor fusion framework for accurate tool fault prediction during machining. Experimental data were collected while turning AISI 410-grade steel bars with [...] Read more.
Effective cutting tool condition monitoring (TCM) is critical for achieving precision, cost efficiency, and minimizing unplanned downtime. This study proposes a sophisticated sensor fusion framework for accurate tool fault prediction during machining. Experimental data were collected while turning AISI 410-grade steel bars with uncoated carbide inserts under dry-cutting conditions. Force and vibration signals were captured across five tool health states (one healthy and four faulty) using a sensor network and data acquisition systems. The raw signals were decomposed using discrete wavelet transform, and key statistical features were extracted. Three distinct input datasets are constructed: Dataset I comprises statistical parameters extracted exclusively from the force signals, Dataset II consists of statistical parameters derived from the vibration signals, and Dataset III integrates the individual statistical parameters from both force and vibration signals through feature-level fusion. These datasets are then utilized for training ML classifiers (Support Vector Machine, Random Forest, and Naive Bayes) to perform feature learning and subsequent classification. Among the considered classifiers, the RF classifier yielded better classification accuracies of 96% and 97% while discriminating among the tool health scenarios through dataset I and II. Also, the RF and SVM classifiers achieved a classification accuracy of 98% and 88% in distinguishing tool health scenarios for dataset III. This method demonstrates exceptional suitability for real-time, in situ fault diagnostics and provides a strong foundation for developing online TCM systems, advancing the objectives of Industry 4.0 and smart manufacturing. Full article
(This article belongs to the Special Issue Condition Monitoring and Fault Diagnosis)
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