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Sensors
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Applications of Sensors in Condition Monitoring and Fault Diagnosis
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Applications of Sensors in Condition Monitoring and Fault Diagnosis

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 20 December 2025 | Viewed by 1006

Special Issue Editor

Dr. Guoliang Lu

E-Mail Website
Guest Editor
School of Mechanical Engineering, Shandong University, Shandong 250061, China
Interests: machine vision for inspection and measurement in industrial applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As awareness grows regarding safety consciousness and technological development, innovative technologies for the health monitoring and diagnostics of machinery have garnered increasing attention. The application of sensors has emerged as a new opportunity for improving condition monitoring and fault diagnosis in various machines. In particular, the integration of sensors with IoT devices has enabled the collection of vast amounts of real-time data, which can be processed by advanced artificial intelligence algorithms to detect anomalies, predict failures, and enable intelligent diagnosis. Recent developments in artificial intelligence algorithms, such as signal processing techniques; machine learning techniques; deep learning techniques, have shown significant potential in enhancing the accuracy and efficiency of fault detection, condition assessment, and prognostics.

This special issue aims to showcase high-quality original research articles, comprehensive review papers, and insightful communications highlighting the latest advancements in sensor technologies and their applications in industrial machinery fault diagnosis. We encourage contributions that push the boundaries of current knowledge and offer innovative solutions for the enhancement of health monitoring, prediction, and fault detection. We also welcome scholars to contribute to advancing the field and achieving further milestones in the realm of fault diagnosis and condition monitoring.

Dr. Guoliang Lu
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. Sensors 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 2600 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
  • intelligent diagnosis
  • health monitoring
  • fault detection
  • fault diagnosis
 

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Published Papers (2 papers)

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Research

21 pages, 5539 KB  
Article
Multimodal Large Language Model-Enabled Machine Intelligent Fault Diagnosis Method with Non-Contact Dynamic Vision Data
by Zihan Lu, Cuiying Sun and Xiang Li
Sensors 2025, 25(18), 5898; https://doi.org/10.3390/s25185898 (registering DOI) - 20 Sep 2025
Abstract
Smart manufacturing demands ever-increasing equipment reliability and continuous availability. Traditional fault diagnosis relies on attached sensors and complex wiring to collect vibration signals. This approach suffers from poor environmental adaptability, difficult maintenance, and cumbersome preprocessing. This study pioneers the use of high-temporal-resolution dynamic [...] Read more.
Smart manufacturing demands ever-increasing equipment reliability and continuous availability. Traditional fault diagnosis relies on attached sensors and complex wiring to collect vibration signals. This approach suffers from poor environmental adaptability, difficult maintenance, and cumbersome preprocessing. This study pioneers the use of high-temporal-resolution dynamic visual information captured by an event camera to fine-tune a multimodal large model for the first time. Leveraging non-contact acquisition with an event camera, sparse pulse events are converted into event frames through time surface processing. These frames are then reconstructed into a high-temporal-resolution video using spatiotemporal denoising and region of interest definition. The study introduces the multimodal model Qwen2.5-VL-7B and employs two distinct LoRA fine-tuning strategies for bearing fault classification. Strategy A utilizes OpenCV to extract key video frames for lightweight parameter injection. In contrast, Strategy B calls the model’s built-in video processing pipeline to fully leverage rich temporal information and capture dynamic details of the bearing’s operation. Classification experiments were conducted under three operating conditions and four rotational speeds. Strategy A and Strategy B achieved classification accuracies of 0.9247 and 0.9540, respectively, successfully establishing a novel fault diagnosis paradigm that progresses from non-contact sensing to end-to-end intelligent analysis. Full article
(This article belongs to the Special Issue Applications of Sensors in Condition Monitoring and Fault Diagnosis)
26 pages, 6731 KB  
Article
Deep Ensemble Learning Based on Multi-Form Fusion in Gearbox Fault Recognition
by Xianghui Meng, Qingfeng Wang, Chunbao Shi, Qiang Zeng, Yongxiang Zhang, Wanhao Zhang and Yinjun Wang
Sensors 2025, 25(16), 4993; https://doi.org/10.3390/s25164993 - 12 Aug 2025
Viewed by 452
Abstract
Considering the problems of having insufficient fault identification from single information sources in actual industrial environments, and different information sensitivity in multi-information source data, and different sensitivity of artificial feature extraction, which can lead to difficulties of effective fusion of equipment information, insufficient [...] Read more.
Considering the problems of having insufficient fault identification from single information sources in actual industrial environments, and different information sensitivity in multi-information source data, and different sensitivity of artificial feature extraction, which can lead to difficulties of effective fusion of equipment information, insufficient state representation ability, low fault identification accuracy, and poor robustness, a multi-information fusion fault identification network model based on deep ensemble learning is proposed. The network is composed of multiple sub-feature extraction units and feature fusion units. Firstly, the fault feature mapping information of each information source is extracted and stored in different sub-models, and then, the features of each sub-model are fused by the feature fusion unit. Finally, the fault recognition results are obtained. The effectiveness of the proposed method is evaluated by using two gearbox datasets. Compared with the method of simple stacking fusion and single measuring point without fusion, the accuracy of each type of fault recognition of the proposed method is close to 100%. The results show that the proposed method is feasible and effective in the application of gearbox fault recognition. Full article
(This article belongs to the Special Issue Applications of Sensors in Condition Monitoring and Fault Diagnosis)
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