Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Artificial Intelligence and Sensor-Enhanced Fault Diagnosis for Industrial Application

Special Issue Editors
Special Issue Information
Benefits of Publishing in a Special Issue
Published Papers

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

Deadline for manuscript submissions: 25 June 2025 | Viewed by 9011

Share This Special Issue

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.

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (6 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

17 pages, 6825 KiB

Open AccessArticle

Concept Development for Bearing Fault Detection on Water-Cooled Electric Machines Using Infrared

by Stephanie Schamberger, Lukas Brandl, Hans-Christian Reuss and Alfons Wagner

Sensors 2025, 25(7), 2170; https://doi.org/10.3390/s25072170 - 29 Mar 2025

Viewed by 289

Abstract

Electric machines (EMs) of electrified vehicle drivetrains can be tested on drivetrain test benches at an early stage of development. In order to protect the EMs from premature damage or failure during testing, monitoring their thermal condition is important. Due to the package requirements of compact and powerful EMs with high-speed requirements and high-power densities, the heat build-up inside the motor during operation is particularly high. For this reason, fluid cooling with heat exchangers is increasingly being used in EMs. The EMs analysed in this work are water-cooled by a cooling jacket. This influences the heat flow inside the machine through heat transfer mechanisms, making it difficult to detect damage to the EMs. This paper presents a novel method for non-destructive and non-contact thermal condition monitoring of water-cooled EMs on drivetrain test benches using thermography. In an experimental setup, infrared images of an intact water-cooled EM are taken. A bearing of the EM’s rotor is then damaged synthetically, and the experiment is repeated. The infrared images are then processed and analysed using appropriate software. The analysis of the infrared images shows that the heat propagation of the motor with bearing damage differs significantly from the heat propagation of the motor without bearing damage. This means that thermography opens up another method of condition monitoring for water-cooled EMs. The results of the investigation serve as a basis for future condition monitoring of water-cooled EMs on powertrain test benches using artificial intelligence (AI). Full article

(This article belongs to the Special Issue Artificial Intelligence and Sensor-Enhanced Fault Diagnosis for Industrial Application)

► Show Figures

Figure 1

19 pages, 5942 KiB

Open AccessArticle

Research on Pipeline Stress Detection Method Based on Double Magnetic Coupling Technology

by Guoqing Wang, Qi Xia, Hong Yan, Shicheng Bei, Huakai Zhang, Hao Geng and Yuhan Zhao

Sensors 2024, 24(19), 6463; https://doi.org/10.3390/s24196463 - 7 Oct 2024

Cited by 1 | Viewed by 1168

Abstract

Oil and gas pipelines are subject to soil corrosion and medium pressure factors, resulting in stress concentration and pipe rupture and explosion. Non-destructive testing technology can identify the stress concentration and defect corrosion area of the pipeline to ensure the safety of pipeline transportation. In view of the problem that the traditional pipeline inspection cannot identify the stress signal at the defect, this paper proposes a detection method using strong and weak magnetic coupling technology. Based on the traditional J-A (Jiles–Atherton) model, the pinning coefficient is optimized and the stress demagnetization factor is added to establish the defect of the ferromagnetic material. The force-magnetic relationship optimization model is used to calculate the best detection magnetic field strength. The force-magnetic coupling simulation of Q235 steel material is carried out by ANSYS 2019 R1 software based on the improved J-A force-magnetic model. The results show that the effect of the stress on the pipe on the magnetic induction increases first and then decreases with the increase in the excitation magnetic field strength, and the magnetic signal has the maximum proportion of the stress signal during the excitation process; the magnetic induction at the pipe defect increases linearly with the increase in the stress trend. Through the strong and weak magnetic scanning detection of cracked pipeline materials, the correctness of the theoretical analysis and the validity of the engineering application of the strong and weak magnetic detection method are verified. Full article

(This article belongs to the Special Issue Artificial Intelligence and Sensor-Enhanced Fault Diagnosis for Industrial Application)

► Show Figures

Figure 1

18 pages, 12186 KiB

Open AccessArticle

Cloud-Edge Collaborative Defect Detection Based on Efficient Yolo Networks and Incremental Learning

by Zhenwu Lei, Yue Zhang, Jing Wang and Meng Zhou

Sensors 2024, 24(18), 5921; https://doi.org/10.3390/s24185921 - 12 Sep 2024

Cited by 3 | Viewed by 1928

Abstract

Defect detection constitutes one of the most crucial processes in industrial production. With a continuous increase in the number of defect categories and samples, the defect detection model underpinned by deep learning finds it challenging to expand to new categories, and the accuracy and real-time performance of product defect detection are also confronted with severe challenges. This paper addresses the problem of insufficient detection accuracy of existing lightweight models on resource-constrained edge devices by presenting a new lightweight YoloV5 model, which integrates four modules, SCDown, GhostConv, RepNCSPELAN4, and ScalSeq. Here, this paper abbreviates it as SGRS-YoloV5n. Through the incorporation of these modules, the model notably enhances feature extraction and computational efficiency while reducing the model size and computational load, making it more conducive for deployment on edge devices. Furthermore, a cloud-edge collaborative defect detection system is constructed to improve detection accuracy and efficiency through initial detection by edge devices, followed by additional inspection by cloud servers. An incremental learning mechanism is also introduced, enabling the model to adapt promptly to new defect categories and update its parameters accordingly. Experimental results reveal that the SGRS-YoloV5n model exhibits superior detection accuracy and real-time performance, validating its value and stability for deployment in resource-constrained environments. This system presents a novel solution for achieving efficient and accurate real-time defect detection. Full article

(This article belongs to the Special Issue Artificial Intelligence and Sensor-Enhanced Fault Diagnosis for Industrial Application)

► Show Figures

Figure 1

31 pages, 4601 KiB

Open AccessArticle

Evaluating the Role of Data Enrichment Approaches towards Rare Event Analysis in Manufacturing

by Chathurangi Shyalika, Ruwan Wickramarachchi, Fadi El Kalach, Ramy Harik and Amit Sheth

Sensors 2024, 24(15), 5009; https://doi.org/10.3390/s24155009 - 2 Aug 2024

Cited by 2 | Viewed by 2162

Abstract

Rare events are occurrences that take place with a significantly lower frequency than more common, regular events. These events can be categorized into distinct categories, from frequently rare to extremely rare, based on factors like the distribution of data and significant differences in rarity levels. In manufacturing domains, predicting such events is particularly important, as they lead to unplanned downtime, a shortening of equipment lifespans, and high energy consumption. Usually, the rarity of events is inversely correlated with the maturity of a manufacturing industry. Typically, the rarity of events affects the multivariate data generated within a manufacturing process to be highly imbalanced, which leads to bias in predictive models. This paper evaluates the role of data enrichment techniques combined with supervised machine learning techniques for rare event detection and prediction. We use time series data augmentation and sampling to address the data scarcity, maintaining its patterns, and imputation techniques to handle null values. Evaluating 15 learning models, we find that data enrichment improves the F1 measure by up to 48% in rare event detection and prediction. Our empirical and ablation experiments provide novel insights, and we also investigate model interpretability. Full article

(This article belongs to the Special Issue Artificial Intelligence and Sensor-Enhanced Fault Diagnosis for Industrial Application)

► Show Figures

Figure 1

17 pages, 1779 KiB

Open AccessArticle

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

Cited by 1 | Viewed by 1221

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 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)

► Show Figures

Figure 1

18 pages, 13202 KiB

Open AccessArticle

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

Cited by 1 | Viewed by 1191

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 (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)

► Show Figures

Journal Menu

Journal Browser

Artificial Intelligence and Sensor-Enhanced Fault Diagnosis for Industrial Application

Share This Special Issue

Special Issue Editors

Special Issue Information

Benefits of Publishing in a Special Issue

Published Papers (6 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI