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Artificial Intelligence and Sensor-Enhanced Fault Diagnosis for Industrial Application
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Artificial Intelligence and Sensor-Enhanced Fault Diagnosis for Industrial Application

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

Deadline for manuscript submissions: 25 December 2024 | Viewed by 1590

Special Issue Editors

Prof. Dr. Jing Wang

E-Mail Website
Guest Editor
School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China
Interests: fault diagnosis and state monitoring; fault-tolerant control; iterative learning control; biochemical process synthesis
Special Issues, Collections and Topics in MDPI journals
Dr. Meng Zhou

E-Mail Website
Guest Editor
School of Electrical and Control Engineering, North China University of Technology, Beijing 100041, China
Interests: complex system fault diagnosis and fault tolerance control; multi-robot collaborative path planning and control; sewage treatment process
Dr. Shenghui Guo

E-Mail Website
Guest Editor
College of Electronics and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
Interests: fault diagnosis and state monitoring; fault-tolerant control; distributed control; intelligent connected vehicle
Dr. Weixin Han

E-Mail Website
Guest Editor
School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
Interests: fault diagnosis and fault-tolerant control; guidance and intelligent control

Special Issue Information

Dear Colleagues,

In Industry 4.0, a key goal is to digitize and intelligentize condition monitoring and fault diagnosis technologies in complex industrial production processes. In the past decade, artificial intelligence, as a supplement to traditional physics-based and signal-processing-based fault detection, diagnosis, and prediction technologies, has provided a promising tool for industrial production lines to achieve automated safety production and accurate fault prediction.

This Special Issue will focus on the advanced research and engineering innovations in the adoption of artificial intelligence technologies for fault detection, diagnosis and prediction. Researchers are welcome to publish original research on the latest findings related to intelligent techniques of fault diagnosis in manufacturing production processes.

Topics of interest include, but are not limited to, the following:

  • Fault mechanism analysis;
  • Data-driven fault diagnosis and state monitoring;
  • Fault-tolerant control for complex systems;
  • Statistical learning, machine learning, artificial intelligence, data mining, big data analysis, and signal processing techniques in fault diagnosis;
  • Reliability analysis, remaining life prognosis, and non-destructive testing;
  • Health maintenance and performance evaluation
  • Advanced imaging process and optimization method;
  • Sensors and instrument measurement;
  • Nonlinear system analysis and control.

Prof. Dr. Jing Wang
Dr. Meng Zhou
Dr. Shenghui Guo
Dr. Weixin Han
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.

Published Papers (2 papers)

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Research

17 pages, 1779 KiB  
Article
A Semi-Supervised Adaptive Matrix Machine Approach for Fault Diagnosis in Railway Switch Machine
by Wenqing Li, Zhongwei Xu, Meng Mei, Meng Lan, Chuanzhen Liu and Xiao Gao
Sensors 2024, 24(13), 4402; https://doi.org/10.3390/s24134402 - 7 Jul 2024
Viewed by 441
Abstract
The switch machine, an essential element of railway infrastructure, is crucial in maintaining the safety of railway operations. Traditional methods for fault diagnosis are constrained by their dependence on extensive labeled datasets. Semi-supervised learning (SSL), although a promising solution to the scarcity of [...] Read more.
The switch machine, an essential element of railway infrastructure, is crucial in maintaining the safety of railway operations. Traditional methods for fault diagnosis are constrained by their dependence on extensive labeled datasets. Semi-supervised learning (SSL), although a promising solution to the scarcity of samples, faces challenges such as the imbalance of pseudo-labels and inadequate data representation. In response, this paper presents the Semi-Supervised Adaptive Matrix Machine (SAMM) model, designed for the fault diagnosis of switch machine. SAMM amalgamates semi-supervised learning with adaptive technologies, leveraging adaptive low-rank regularizer to discern the fundamental links between the rows and columns of matrix data and applying adaptive penalty items to correct imbalances across sample categories. This model methodically enlarges its labeled dataset using probabilistic outputs and semi-supervised, automatically adjusting parameters to accommodate diverse data distributions and structural nuances. The SAMM model’s optimization process employs the alternating direction method of multipliers (ADMM) to identify solutions efficiently. Experimental evidence from a dataset containing current signals from switch machines indicates that SAMM outperforms existing baseline models, demonstrating its exceptional status diagnostic capabilities in situations where labeled samples are scarce. Consequently, SAMM offers an innovative and effective approach to semi-supervised classification tasks involving matrix data. Full article
(This article belongs to the Special Issue Artificial Intelligence and Sensor-Enhanced Fault Diagnosis for Industrial Application)
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18 pages, 13202 KiB  
Article
SSFLNet: A Novel Fault Diagnosis Method for Double Shield TBM Tool System
by Peng Zhou, Chang Liu, Jiacan Xu, Dazhong Ma, Zinan Wang and Enguang He
Sensors 2024, 24(8), 2631; https://doi.org/10.3390/s24082631 - 20 Apr 2024
Viewed by 567
Abstract
In tunnel boring projects, wear and tear in the tooling system can have significant consequences, such as decreased boring efficiency, heightened maintenance costs, and potential safety hazards. In this paper, a fault diagnosis method for TBM tooling systems based on SAV−SVDD failure location [...] Read more.
In tunnel boring projects, wear and tear in the tooling system can have significant consequences, such as decreased boring efficiency, heightened maintenance costs, and potential safety hazards. In this paper, a fault diagnosis method for TBM tooling systems based on SAV−SVDD failure location (SSFL) is proposed. The aim of this method is to detect faults caused by disk cutter wear during the boring process, which diminishes the boring efficiency and is challenging to detect during construction. This paper uses SolidWorks to create a complete three−dimensional model of the TBM hydraulic thrust system and tool system. Then, dynamic simulations are performed with Adams. This helps us understand how the load on the propulsion hydraulic cylinder changes as the TBM tunneling tool wears to different degrees during construction. The hydraulic propulsion system was modeled and simulated using AMESIM software. Utilizing the load on the hydraulic propulsion cylinder as an input signal, pressure signals from the two chambers of the hydraulic cylinder and the system’s flow signal were acquired. This enabled an in−depth exploration of the correlation between these acquired signals and the extent of the tooling system failure. Following this analysis, a collection of normal sample data and sample data representing different degrees of disk cutter abrasions was amassed for further study. Next, an SSFL network model for locating the failure area of the cutter was established. Fault sample data were used as the input, and the accuracy of the fault diagnosis model was tested. The test results show that the performance of the SSFL network model is better than that of the SAE−SVM and SVDD network models. The SSFL model achieves 90% accuracy in determining the failure area of the cutter head. The model effectively identifies the failure regions, enabling timely tool replacement to avoid decreased boring efficiency under wear conditions. The experimental findings validate the feasibility of this approach. Full article
(This article belongs to the Special Issue Artificial Intelligence and Sensor-Enhanced Fault Diagnosis for Industrial Application)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Evaluating the Role of Data Enrichment Approaches Towards Rare Event Analysis in Manufacturing
Author: Shyalika
Highlights: Propose a framework for investigating data enrichment in rare-event detection and prediction. Introduce a real-world manufacturing dataset. Conduct empirical and ablation experiments on this dataset for novel insights. Examine model interpretability in rare event prediction using multiple methods.

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