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Processes
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Fault Diagnosis of Equipment in the Process Industry
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Fault Diagnosis of Equipment in the Process Industry

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Manufacturing Processes and Systems".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 3232

Special Issue Editors

Dr. Heng Zhang

E-Mail Website
Guest Editor
College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Interests: prognostic and health management; battery management system; fault diagnosis and prognosis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chuanjiang Li

E-Mail Website
Guest Editor
State Key Laboratory of Public Big Data, Gui Zhou University, Guizhou, China
Interests: intelligent PHM; few-shot fault diagnosis; UAV data analysis; meta-learning
Special Issues, Collections and Topics in MDPI journals
Dr. Yuhang Xu

E-Mail Website
Guest Editor Assistant
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Interests: fault-tolerant control; game control; differential game; multi-agent systems

Special Issue Information

Dear Colleagues,

This Special Issue on the "Fault Diagnosis of Equipment in the Process Industry" seeks to present the latest innovations and trends in fault detection, diagnosis, and predictive maintenance techniques for industrial processes. With the growing complexity and automation of modern industrial systems, effective fault diagnosis has become essential for ensuring operational safety, reliability, and efficiency. This Special Issue will focus on novel approaches and methodologies aimed at improving fault detection accuracy, reducing downtime, and enhancing the overall reliability of process equipment.

We welcome submissions that address both theoretical advancements and practical applications, particularly in areas involving intelligent algorithms, real-time monitoring, and the integration of fault diagnosis technologies within industrial systems. Researchers and practitioners from academia and industry are encouraged to contribute their latest work on fault diagnosis systems, predictive maintenance, and machine learning applications. With a focus on the fault diagnosis of equipment in the process industry, this Special Issue will encompass a wide range of topics, including, but not limited to, the following:

  • Data-driven and model-based fault diagnosis techniques.
  • Intelligent algorithms and machine learning for condition monitoring.
  • AI and big data analytics in fault diagnosis for process equipment.
  • Anomaly detection and location in the process industry.
  • Remaining useful life prediction for key equipment.
  • Small data challenges in PHM.
  • Predictive maintenance and health management in the process industry.
  • Fault diagnosis and prognosis systems and applications in the process industry.
  • Reliability and safety assessment in complex systems.

Dr. Heng Zhang
Prof. Dr. Chuanjiang Li
Guest Editors

Dr. Yuhang Xu
Guest Editor Assistant

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. Processes is an international peer-reviewed open access monthly 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 2400 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

  • fault diagnosis
  • process industry
  • anomaly detection
  • prognosis and health management
  • AI technology

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

Published Papers (5 papers)

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Research

23 pages, 8118 KiB  
Article
Open-Circuit Fault Diagnosis in 3ϕ V/F-Controlled VSIs Under Variable Load Conditions at Different Frequencies Using Park’s Vector Normalization and Extreme Gradient Boosting
by Priyanka Tupe-Waghmare, Neha Ganvir, R. B. Dhumale and Aziz Nanthaamornphong
Processes 2025, 13(5), 1313; https://doi.org/10.3390/pr13051313 - 25 Apr 2025
Viewed by 81
Abstract
The open-circuit fault diagnosis of switching devices in 3ϕ V/F-controlled voltage source inverters is critical, since diagnostic parameters change with varying load conditions and frequency. Park’s vector transform-based approaches depend on threshold values for fault diagnosis, demanding continuous modifications based on load variations, [...] Read more.
The open-circuit fault diagnosis of switching devices in 3ϕ V/F-controlled voltage source inverters is critical, since diagnostic parameters change with varying load conditions and frequency. Park’s vector transform-based approaches depend on threshold values for fault diagnosis, demanding continuous modifications based on load variations, making them prone to improper diagnosis. Artificial intelligence-based methods give good accuracy, but they require extensive data collection under varying load conditions, creating implementation efforts that are considerably high. This paper focuses on optimizing threshold-independent methods and reducing data requirements for the artificial intelligence-based open-circuit fault diagnosis of 3ϕ V/F-controlled VSIs. To mitigate the problem of fault misclassification under variable load conditions at different frequencies, the stator current is normalized using Park’s vector transform. Normalized currents ensure that the extracted features remain the same under all load conditions while providing distinctive features for faulty and healthy conditions. Feature extraction is implemented using the wavelet transform, and feature selection is carried out using a ReliefF algorithm, which enhances classification by selecting key features. The selected features are then used to diagnose faults using an extreme gradient boosting algorithm. In XGBoost, a random search is preferred over a grid search to find the best hyperparameters for optimal performance, as it speeds up tuning, explores more options, and efficiently balances accuracy. The proposed system outperforms current open-switch fault diagnosis approaches by providing high effectiveness, strong resistivity, and a fast detection time. The results are presented for different combinations of single and multiple open-switch faults under variable load conditions at different frequencies. Full article
(This article belongs to the Special Issue Fault Diagnosis of Equipment in the Process Industry)
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22 pages, 1406 KiB  
Article
Comparative Analysis of Kalman-Based Approaches for Fault Detection in a Clean-In-Place System Model
by Ayman E. O. Hassan and Askin Demirkol
Processes 2025, 13(4), 936; https://doi.org/10.3390/pr13040936 - 21 Mar 2025
Viewed by 310
Abstract
The most appropriate operating conditions are necessary in industrial manufacturing to maintain product quality and consistency. In this respect, Clean-In-Place (CIP) is a widely adopted method in the food, beverage, pharmaceutical, and chemical industries, which ensures equipment cleanliness without dismantling. A detailed analysis [...] Read more.
The most appropriate operating conditions are necessary in industrial manufacturing to maintain product quality and consistency. In this respect, Clean-In-Place (CIP) is a widely adopted method in the food, beverage, pharmaceutical, and chemical industries, which ensures equipment cleanliness without dismantling. A detailed analysis and simulation for the assessment of accuracy, computational efficiency, and adaptability in fault detection, such as valve malfunction, pump failure, and sensor error, are necessary for the CIP system. Advanced fault detection methods within a five-tank CIP model are investigated in this paper, comparing the extended Kalman filter (EKF) with the unscented Kalman filter (UKF). Both techniques have their merits for fault detection in complex systems. The results indicate that the UKF mostly performs better than the EKF in treating the nonlinearities of the given CIP system with the chosen system characteristics and fault type. This approach helps improve the reliability and efficiency of the CIP process, thus providing insights into enhancing fault detection strategies in industrial applications. Full article
(This article belongs to the Special Issue Fault Diagnosis of Equipment in the Process Industry)
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22 pages, 6777 KiB  
Article
Automated Tomato Defect Detection Using CNN Feature Fusion for Enhanced Classification
by Musaad Alzahrani
Processes 2025, 13(1), 115; https://doi.org/10.3390/pr13010115 - 4 Jan 2025
Viewed by 902
Abstract
Tomatoes are among the most widely cultivated and consumed vegetable crops worldwide. They are usually harvested in large quantities that need to be promptly and accurately classified into healthy and defective categories. Traditional methods for tomato classification are labor-intensive and prone to human [...] Read more.
Tomatoes are among the most widely cultivated and consumed vegetable crops worldwide. They are usually harvested in large quantities that need to be promptly and accurately classified into healthy and defective categories. Traditional methods for tomato classification are labor-intensive and prone to human error. Therefore, this study proposes an approach that leverages feature fusion from two pre-trained convolutional neural networks (CNNs), VGG16 and ResNet-50, to enhance classification performance. A comprehensive evaluation of multiple individual and hybrid classifiers was conducted on a dataset of 43,843 tomato images, which is heavily imbalanced toward the healthy class. The results showed that the best-performing classifier on fused features achieved an average precision (AP) and accuracy of 0.92 and 0.97, respectively, on the test set. In addition, the experimental evaluation revealed that fused features improved classification performance across multiple metrics, including accuracy, AP, recall, and F1-score, compared to individual features of VGG16 and ResNet-50. Furthermore, the proposed approach was benchmarked against three standalone CNN models, namely MobileNetV2, EfficientNetB0, and DenseNet121, and demonstrated superior performance in all evaluated metrics. These findings highlight the efficacy of deep feature fusion in addressing class imbalance and improving automated tomato defect detection. Full article
(This article belongs to the Special Issue Fault Diagnosis of Equipment in the Process Industry)
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25 pages, 6265 KiB  
Article
Optimum Cable Bonding with Pareto Optimal and Hybrid Neural Methods to Prevent High-Voltage Cable Insulation Faults in Distributed Generation Systems
by Bahadır Akbal
Processes 2024, 12(12), 2909; https://doi.org/10.3390/pr12122909 - 19 Dec 2024
Cited by 1 | Viewed by 641
Abstract
The high voltage, current and harmonic distortion in high-voltage cable metal sheaths cause cable insulation faults. The SSBLR (Sectional Solid Bonding with Inductance (L) and Resistance) method was designed as a new cable grounding method to prevent insulation faults. SSBLR was optimized using [...] Read more.
The high voltage, current and harmonic distortion in high-voltage cable metal sheaths cause cable insulation faults. The SSBLR (Sectional Solid Bonding with Inductance (L) and Resistance) method was designed as a new cable grounding method to prevent insulation faults. SSBLR was optimized using multi-objective optimization (MOP) with the prediction method (PM) to minimize these factors. The Pareto optimal method was used for MOP. The artificial neural network, hybrid artificial neural network and regression methods were used as the PM. When the artificial neural network–genetic algorithm hybrid method was used as the PM, and the genetic algorithm was used as the optimization method, the voltage and current were significantly reduced in the metal sheath of the cable. Full article
(This article belongs to the Special Issue Fault Diagnosis of Equipment in the Process Industry)
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16 pages, 2432 KiB  
Article
Probabilistic Time Series Forecasting Based on Similar Segment Importance in the Process Industry
by Xingyou Yan, Heng Zhang, Zhigang Wang and Qiang Miao
Processes 2024, 12(12), 2700; https://doi.org/10.3390/pr12122700 - 29 Nov 2024
Cited by 1 | Viewed by 1040
Abstract
Probabilistic time series forecasting is crucial in various fields, including reducing stockout risks in retail, balancing road network loads, and optimizing power distribution systems. Building forecasting models for large-scale time series is challenging due to distribution differences, amplitude fluctuations, and complex patterns across [...] Read more.
Probabilistic time series forecasting is crucial in various fields, including reducing stockout risks in retail, balancing road network loads, and optimizing power distribution systems. Building forecasting models for large-scale time series is challenging due to distribution differences, amplitude fluctuations, and complex patterns across various series. To address these challenges, a probabilistic forecasting method with two different implementations that focus on historical segment importance is proposed in this paper. First, a patch squeeze and excitation (PSE) module is designed to preprocess historical data, capture segment importance, and distill information. Next, an LSTM-based network is used to generate maximum likelihood estimations of distribution parameters or different quantiles for multi-step forecasting. Experimental results demonstrate that the proposed PSE module significantly enhances the base model’s prediction performance, and direct multi-step forecasting offers more detailed information for high-frequency data than recursive forecasting. Full article
(This article belongs to the Special Issue Fault Diagnosis of Equipment in the Process Industry)
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