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Advances in Machinery Fault Diagnosis and Condition Monitoring

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

Deadline for manuscript submissions: 30 May 2025 | Viewed by 951

Special Issue Editors

State Key Laboratory of Mechanical Transmissions for Advanced Equipment, Chongqing University, Chongqing, China
Interests: fault diagnostics; rolling element bearings; signal analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the ever-evolving landscape of modern industry, machinery plays a pivotal role. The scientific background of machinery fault diagnosis and condition monitoring is grounded in the need to ensure the reliable and efficient operation of complex industrial systems. As industries become more automated and reliant on advanced machinery, the importance of this research area cannot be overstated.

Effective machinery fault diagnosis and condition monitoring are crucial for preventing unexpected breakdowns, reducing maintenance costs, and maximizing productivity. By detecting faults early and accurately, businesses can avoid costly downtime and ensure the smooth running of their operations. This research area is not only essential for individual companies but also has a significant impact on the overall economy and sustainability of the industrial sectors.

We are pleased to invite you to contribute to our Special Issue on the Advances in Machinery Fault Diagnosis and Condition Monitoring. This Special Issue aims to showcase the latest research and innovations in this field, providing a platform for researchers and practitioners to share their findings and insights.

Suggested themes and article types for submissions:

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  1. Advanced signal processing techniques for fault detection: This could involve the application of innovative signal processing algorithms to extract meaningful information from machine signals and identify early signs of faults.
  2. Data-driven approaches to fault diagnosis: Researchers might explore the use of machine learning and artificial intelligence techniques to analyze large amounts of data and detect faults in real time.
  3. Prognostics and remaining useful life prediction: Considerations could include developing models to predict the remaining useful life of machinery components and plan maintenance activities accordingly.
  4. Case studies on successful fault diagnosis and condition monitoring implementations: Opportunities for research might lie in sharing real-world examples of effective strategies and solutions.

We look forward to receiving your contributions.

Dr. Lang Xu
Prof. Steven Chatterton
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. 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

  • machinery fault diagnosis
  • condition monitoring
  • signal processing
  • data-driven approaches
  • remaining useful life prognostics

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Published Papers (1 paper)

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Research

21 pages, 5217 KiB  
Article
Wavelet-Based Analysis of Motor Current Signals for Detecting Obstacles in Train Doors
by Yaojung Shiao, Premkumar Gadde and Chun-Yu Liu
Appl. Sci. 2025, 15(1), 25; https://doi.org/10.3390/app15010025 - 24 Dec 2024
Cited by 1 | Viewed by 704
Abstract
Trains used in urban mass passenger transit often have side entrance doors through which passengers can rapidly enter and exit the train. These doors are typically electrically powered and automated. Many incidents have occurred in which a passenger is trapped and injured while [...] Read more.
Trains used in urban mass passenger transit often have side entrance doors through which passengers can rapidly enter and exit the train. These doors are typically electrically powered and automated. Many incidents have occurred in which a passenger is trapped and injured while passing through the doors as they are closing. Existing solutions rely on sensitive-edge sensors and current signal peak detection in the time domain to detect door obstructions. However, these methods have notable limitations: sensors are expensive, and sensor failure can result in safety risks, while time-domain signal analysis is prone to noise, potentially leading to false peak detection. The proposed efficient and cost-effective method enhances safety by implementing the torque control of a DC motor which limits the door closing force to prevent potential injuries. In addition, it reduces reliance on traditional edge sensors, which are prone to failure and may result in undetected obstructions. By using a robust time–frequency domain approach, the system ensures more accurate detection, minimizing potential injury risks. An obstruction of the door causes a corresponding change in the motor current. These changes can be detected by using the discrete wavelet transform to decompose the current signal. The norm and peak of the current are used as obstacle detection features, and appropriate threshold values are obtained from a simulation. The simulation results were validated through an experiment. The proposed novel system effectively detects forces between 100 and 200 N (indicating the presence of an object) within 0.3 s and complies with EN14752 safety standard. It can also differentiate between soft and hard objects trapped in train doors. Full article
(This article belongs to the Special Issue Advances in Machinery Fault Diagnosis and Condition Monitoring)
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