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Advanced Sensing and Fault Diagnosis for Complex Manufacturing Processes

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

Deadline for manuscript submissions: 10 April 2025 | Viewed by 2877

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Special Issue Editors

Dr. Kai Zhang

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Guest Editor

Department of Control Science and Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: fault diagnosis; process monitoring; data-driven performance monitoring and management
Special Issues, Collections and Topics in MDPI journals

Dr. Zhiwen Chen

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Guest Editor

School of Automation, Central South University, Changsha, 410083, China
Interests: machine learning; data mining and analytic; PHM and fault diagnosis
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Yuri A. W. Shardt

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Guest Editor

Department of Automation Engineering, Technical University of Ilmenau, 99084 Ilmenau, Germany
Interests: soft sensors; modelling and system identification design of experiments; control, smart Factory/Industry 4.0; fault detection and isolation

Special Issue Information

Dear Colleagues,

Due to the development of advanced sensing techniques, vast quantities of data are produced daily in complex manufacturing processes. To make the most and the best use of the available data, data-driven techniques have been the subject of extensive research in recent years. Compared with traditional model-based techniques, data-driven methods can not only save in costly modelling processes, but also obtain valuable information from the available process data for real-time process maintenance. Then, abnormal events including different types of faults can be diagnosed in a timely manner. Due to the ever-increasing complexity that exists in manufacturing processes, there are many new challenging problems to be solved in this field, such as fault root-cause analysis for large-scale, plant-wide processes; advanced sensing, such as image and voiceprint-based process monitoring; and fault diagnosis in the distributed framework, among others.

This Special Issue aims to provide a platform for the presentation of recent findings and emerging research developments in advanced sensing and data-driven fault diagnosis for complex manufacturing processes, especially process monitoring, fault detection, fault diagnosis, and deep learning-relevant fault diagnosis techniques and their application in complex manufacturing processes.

Potential topics to be covered:

(1) Advanced sensing techniques

(2) Data-driven fault diagnosis methods

(3) Deep learning-based fault diagnosis methods

(4) Data-driven fault identification and root-cause analysis

(5) Data-driven fault degrade evaluation methods

(6)Image and voiceprint-based fault diagnosis

(7) Fault diagnosis methods with application to different sectors

Dr. Kai Zhang
Dr. Zhiwen Chen
Prof. Dr. Yuri A. W. Shardt
Guest Editors

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Published Papers (3 papers)

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Research

23 pages, 9270 KiB

Open AccessArticle

Unsupervised Transfer Learning Method via Cycle-Flow Adversarial Networks for Transient Fault Detection under Various Operation Conditions

by Xiaoyue Yang, Long Chen, Qidong Feng, Yucheng Yang and Sen Xie

Sensors 2024, 24(15), 4839; https://doi.org/10.3390/s24154839 - 25 Jul 2024

Viewed by 729

Abstract

The efficient fault detection (FD) of traction control systems (TCSs) is crucial for ensuring the safe operation of high-speed trains. Transient faults (TFs) can arise due to prolonged operation and harsh environmental conditions, often being masked by background noise, particularly during dynamic operating conditions. Moreover, acquiring a sufficient number of samples across the entire scenario presents a challenging task, resulting in imbalanced data for FD. To address these limitations, an unsupervised transfer learning (TL) method via federated Cycle-Flow adversarial networks (CFANs) is proposed to effectively detect TFs under various operating conditions. Firstly, a CFAN is specifically designed for extracting latent features and reconstructing data in the source domain. Subsequently, a transfer learning framework employing federated CFANs collectively adjusts the modified knowledge resulting from domain alterations. Finally, the designed federated CFANs execute transient FD by constructing residuals in the target domain. The efficacy of the proposed methodology is demonstrated through comparative experiments. Full article

(This article belongs to the Special Issue Advanced Sensing and Fault Diagnosis for Complex Manufacturing Processes)

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31 pages, 24430 KiB

Open AccessArticle

A Common Knowledge-Driven Generic Vision Inspection Framework for Adaptation to Multiple Scenarios, Tasks, and Objects

by Delong Zhao, Feifei Kong, Nengbin Lv, Zhangmao Xu and Fuzhou Du

Sensors 2024, 24(13), 4120; https://doi.org/10.3390/s24134120 - 25 Jun 2024

Viewed by 764

Abstract

The industrial manufacturing model is undergoing a transformation from a product-centric model to a customer-centric one. Driven by customized requirements, the complexity of products and the requirements for quality have increased, which pose a challenge to the applicability of traditional machine vision technology. Extensive research demonstrates the effectiveness of AI-based learning and image processing on specific objects or tasks, but few publications focus on the composite task of the integrated product, the traceability and improvability of methods, as well as the extraction and communication of knowledge between different scenarios or tasks. To address this problem, this paper proposes a common, knowledge-driven, generic vision inspection framework, targeted for standardizing product inspection into a process of information decoupling and adaptive metrics. Task-related object perception is planned into a multi-granularity and multi-pattern progressive alignment based on industry knowledge and structured tasks. Inspection is abstracted as a reconfigurable process of multi-sub-pattern space combination mapping and difference metric under appropriate high-level strategies and experiences. Finally, strategies for knowledge improvement and accumulation based on historical data are presented. The experiment demonstrates the process of generating a detection pipeline for complex products and continuously improving it through failure tracing and knowledge improvement. Compared to the (

{1.767}^{°}

, 69.802 mm) and 0.883 obtained by state-of-the-art deep learning methods, the generated pipeline achieves a pose estimation ranging from (

{2.771}^{°}

, 153.584 mm) to (

{1.034}^{°}

, 52.308 mm) and a detection rate ranging from 0.462 to 0.927. Through verification of other imaging methods and industrial tasks, we prove that the key to adaptability lies in the mining of inherent commonalities of knowledge, multi-dimensional accumulation, and reapplication. Full article

(This article belongs to the Special Issue Advanced Sensing and Fault Diagnosis for Complex Manufacturing Processes)

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19 pages, 6684 KiB

Open AccessArticle

Real-Time Multi-Sensor Joint Fault Diagnosis Method for Permanent Magnet Traction Drive Systems Based on Structural Analysis

by Weiwei Gan, Xueming Li, Dong Wei, Rongjun Ding, Kan Liu and Zhiwen Chen

Sensors 2024, 24(9), 2878; https://doi.org/10.3390/s24092878 - 30 Apr 2024

Viewed by 779

Abstract

Sensor faults are one of the most common faults that cause performance degradation or functional loss in permanent magnet traction drive systems (PMTDSs). To quickly diagnose faulty sensors, this paper proposes a real-time joint diagnosis method for multi-sensor faults based on structural analysis. Firstly, based on limited monitoring signals on board, a structured model of the system was established using the structural analysis method. The isolation and detectability of faulty sensors were analyzed using the Dulmage–Mendelsohn decomposition method. Secondly, the minimum collision set method was used to calculate the minimum overdetermined equation set, transforming the higher-order system model into multiple related subsystem models, thereby reducing modeling complexity and facilitating system implementation. Next, residual vectors were constructed based on multiple subsystem models, and fault detection and isolation strategies were designed using the correlation between each subsystem model and the relevant sensors. The validation results of the physical testing platform based on online fault data recordings showed that the proposed method could achieve rapid fault detection and the localization of multi-sensor faults in PMTDS and had a good application value. Full article

(This article belongs to the Special Issue Advanced Sensing and Fault Diagnosis for Complex Manufacturing Processes)

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