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Fault Diagnosis and Prognosis for Electromechanical Actuators and Sensors
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Fault Diagnosis and Prognosis for Electromechanical Actuators and Sensors

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

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 2720

Special Issue Editors

Dr. Yong Zhou

E-Mail Website
Guest Editor
Department of Integrated Technology and Control Engineering, School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, China
Interests: system fault diagnosis; modeling and control; motion control systems; electric vehicles
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chao Zhang

E-Mail Website
Guest Editor
Department of Integrated Technology and Control Engineering, School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, Shaanxi, China
Interests: system fault diagnosis and prognosis; testability design and safety analysis; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Elector-mechanical actuators (EMA) and sensors have been increasingly applied in aerospace due to their advantages of higher reliability, a lower weight and better maintainability. To guarantee the operational safety and reliability of EMAs and sensors, fault diagnosis and health management can be utilized to acquire reliable information on potential failures. Traditional machine learning (ML)-based fault diagnosis techniques are limited in their ability to process natural data in their raw form. In recent years, deep learning (DL) methods have been increasingly used in fault diagnosis and prediction. Deep learning is an algorithm based on data representation learning in machine learning. The most obvious difference between DL-based models and traditional DL-based models is that DL can learn the abstract representation features of the raw data automatically.

This Special Issue, “Fault Diagnosis and Prognosis for Electromechanical Actuators and Sensors”, seeks original research articles presenting novel approaches to DL-based fault diagnosis, DL-based fault prediction and health management using EMAs and sensors.

Dr. Yong Zhou
Prof. Dr. Chao Zhang
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. 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

  • electromechanical actuators and sensors
  • fault detection and fault diagnosis
  • fault prediction and health management
  • deep-learning based fault diagnosis method

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Published Papers (2 papers)

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Research

20 pages, 4393 KiB  
Article
Tool State Recognition Based on POGNN-GRU under Unbalanced Data
by Weiming Tong, Jiaqi Shen, Zhongwei Li, Xu Chu, Wenqi Jiang and Liguo Tan
Sensors 2024, 24(16), 5433; https://doi.org/10.3390/s24165433 - 22 Aug 2024
Cited by 1 | Viewed by 898
Abstract
Accurate recognition of tool state is important for maximizing tool life. However, the tool sensor data collected in real-life scenarios has unbalanced characteristics. Additionally, although graph neural networks (GNNs) show excellent performance in feature extraction in the spatial dimension of data, it is [...] Read more.
Accurate recognition of tool state is important for maximizing tool life. However, the tool sensor data collected in real-life scenarios has unbalanced characteristics. Additionally, although graph neural networks (GNNs) show excellent performance in feature extraction in the spatial dimension of data, it is difficult to extract features in the temporal dimension efficiently. Therefore, we propose a tool state recognition method based on the Pruned Optimized Graph Neural Network-Gated Recurrent Unit (POGNN-GRU) under unbalanced data. Firstly, design the Improved-Majority Weighted Minority Oversampling Technique (IMWMOTE) by introducing an adaptive noise removal strategy and improving the MWMOTE to alleviate the unbalanced problem of data. Subsequently, propose a POG graph data construction method based on a multi-scale multi-metric basis and a Gaussian kernel weight function to solve the problem of one-sided description of graph data under a single metric basis. Then, construct the POGNN-GRU model to deeply mine the spatial and temporal features of the data to better identify the state of the tool. Finally, validation and ablation experiments on the PHM 2010 and HMoTP datasets show that the proposed method outperforms the other models in terms of identification, and the highest accuracy improves by 1.62% and 1.86% compared with the corresponding optimal baseline model. Full article
(This article belongs to the Special Issue Fault Diagnosis and Prognosis for Electromechanical Actuators and Sensors)
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19 pages, 6425 KiB  
Article
Fault Diagnosis Methods for an Artillery Loading System Driving Motor in Complex Noisy Environments
by Wenkuan Huang, Yong Li, Jinsong Tang and Linfang Qian
Sensors 2024, 24(3), 847; https://doi.org/10.3390/s24030847 - 28 Jan 2024
Cited by 2 | Viewed by 1337
Abstract
With the development of modern military technology, electrical drive technology has become a power source for modern artillery. In fault monitoring of a driving motor mounted on a piece of artillery, various sensors are susceptible to interference from the complex environment, both inside [...] Read more.
With the development of modern military technology, electrical drive technology has become a power source for modern artillery. In fault monitoring of a driving motor mounted on a piece of artillery, various sensors are susceptible to interference from the complex environment, both inside and outside the artillery itself. In this study, we creatively propose a fault diagnosis model based on an attention mechanism, the AdaBoost method and a wavelet noise reduction network to address the difficulty in obtaining high-quality motor signals in complex noisy interference environments. First, multiple fusion wavelet basis, soft thresholding, and index soft filter optimization were used to train multiple wavelet noise reduction networks that could recover sample signals under different noise conditions. Second, a convolutional neural network (CNN) classification module was added to construct end-to-end classification models that could correctly identify faults. The above basis classification models were then integrated into the AdaBoost method with an improved attention mechanism to develop a fault diagnosis model suitable for complex noisy environments. Finally, two experiments were conducted to validate the proposed method. Under motor signals with varying signal-to-noise ratios (SNRs) noises, the proposed method achieved an average accuracy of 92%, surpassing the conventional method by over 8.5%. Full article
(This article belongs to the Special Issue Fault Diagnosis and Prognosis for Electromechanical Actuators and Sensors)
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