Special Issues
Table of Content

Failure Detection Algorithms, Methods and Models for Industrial Environments

Submission Deadline: 30 June 2023 (closed) View: 38

Guest Editors

Prof. Robertas Damasevicius, Silesian University of Technology, Poland
Prof. Rytis Maskeliunas, Kaunas University of Technology, Lithuania
Dr. Mohammed A. A. Al-qaness, Wuhan University, China


This special issue focuses on the failure, anomaly detection, or even novelty detection within the industrial manufacturing environments through a myriad of multimodal senseless and sensor-based signal processing methods, models and algorithms. Such technique has recently received further push in installment due to the development of the smart Internet of Things (IoT) solutions, following explosive growth of big data and to rapid improvement of machine learning techniques, especially deep learning, in the last decade. Anomaly detection is recognized as one of the essential techniques in an application for preventive maintenance of the industrial machine as well as for predictive maintenance of useful life (or time to failure) prediction and quality control. This special issue aims to explore standalone approaches (not being an integral part of the manufacturing equipment itself) following the principle of sustained development of Industry 4.0 digitizing the process while keeping integral parts of the old equipment.


Potential topics include but are not limited to the following: 

l  Sensor-based and senseless anomaly detection, localization, and tracking

l  Machine learning for failure sensing in real-life industrial applications

l  Sensor enhancement for real-life manufacturing environment applications

l  Manufacturing environment classification through fault detection

l  Time-variant analysis of fault detection in manufacturing environments

l  Emerging fault detection applications in industrial environments

l  Fault processing in smart manufacturing

l  Signal decomposition methods for anomaly and failure detection


Anomaly detection; failure detection; fault localization and tracking; condition monitoring; smart manufacturing; predictive maintenance

Published Papers

  • Open Access


    A Railway Fastener Inspection Method Based on Abnormal Sample Generation

    Shubin Zheng, Yue Wang, Liming Li, Xieqi Chen, Lele Peng, Zhanhao Shang
    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 565-592, 2024, DOI:10.32604/cmes.2023.043832
    (This article belongs to the Special Issue: Failure Detection Algorithms, Methods and Models for Industrial Environments)
    Abstract Regular fastener detection is necessary to ensure the safety of railways. However, the number of abnormal fasteners is significantly lower than the number of normal fasteners in real railways. Existing supervised inspection methods have insufficient detection ability in cases of imbalanced samples. To solve this problem, we propose an approach based on deep convolutional neural networks (DCNNs), which consists of three stages: fastener localization, abnormal fastener sample generation based on saliency detection, and fastener state inspection. First, a lightweight YOLOv5s is designed to achieve fast and precise localization of fastener regions. Then, the foreground clip… More >

  • Open Access


    Fault Identification for Shear-Type Structures Using Low-Frequency Vibration Modes

    Cuihong Li, Qiuwei Yang, Xi Peng
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2769-2791, 2024, DOI:10.32604/cmes.2023.030908
    (This article belongs to the Special Issue: Failure Detection Algorithms, Methods and Models for Industrial Environments)
    Abstract Shear-type structures are common structural forms in industrial and civil buildings, such as concrete and steel frame structures. Fault diagnosis of shear-type structures is an important topic to ensure the normal use of structures. The main drawback of existing damage assessment methods is that they require accurate structural finite element models for damage assessment. However, for many shear-type structures, it is difficult to obtain accurate FEM. In order to avoid finite element modeling, a model-free method for diagnosing shear structure defects is developed in this paper. This method only needs to measure a few low-order… More >

  • Open Access


    Gradient Optimizer Algorithm with Hybrid Deep Learning Based Failure Detection and Classification in the Industrial Environment

    Mohamed Zarouan, Ibrahim M. Mehedi, Shaikh Abdul Latif, Md. Masud Rana
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1341-1364, 2024, DOI:10.32604/cmes.2023.030037
    (This article belongs to the Special Issue: Failure Detection Algorithms, Methods and Models for Industrial Environments)
    Abstract Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamless operation of the system. Current industrial processes are getting smarter with the emergence of Industry 4.0. Specifically, various modernized industrial processes have been equipped with quite a few sensors to collect process-based data to find faults arising or prevailing in processes along with monitoring the status of processes. Fault diagnosis of rotating machines serves a main role in the engineering field and industrial production. Due to the disadvantages of existing fault, diagnosis approaches, which greatly depend on professional… More >

  • Open Access


    Fault Detection and Identification Using Deep Learning Algorithms in Induction Motors

    Majid Hussain, Tayab Din Memon, Imtiaz Hussain, Zubair Ahmed Memon, Dileep Kumar
    CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.2, pp. 435-470, 2022, DOI:10.32604/cmes.2022.020583
    (This article belongs to the Special Issue: Failure Detection Algorithms, Methods and Models for Industrial Environments)
    Abstract Owing to the 4.0 industrial revolution condition monitoring maintenance is widely accepted as a useful approach to avoiding plant disturbances and shutdown. Recently, Motor Current Signature Analysis (MCSA) is widely reported as a condition monitoring technique in the detection and identification of individual and multiple Induction Motor (IM) faults. However, checking the fault detection and classification with deep learning models and its comparison among themselves or conventional approaches is rarely reported in the literature. Therefore, in this work, we present the detection and identification of induction motor faults with MCSA and three Deep Learning (DL)… More >

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