Submit to Processes Review for Processes Propose a Special Issue

Journal Menu

Journal Browser

Process Monitoring and Fault Diagnosis

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

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 25794

Share This Special Issue

Special Issue Editor

Prof. Dr. Wei Sun

E-Mail Website
Guest Editor

College of Chemical Engineering, Beijing University of Chemical Technology, Beijing 100029, China
Interests: process modeling and analysis; process monitoring and fault diagnosis; industrial data mining; process system engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The topic of “Process Monitoring and Fault Diagnosis” has emerged and become a hot research spot since the end of last century. With the development of data-acquiring technology, the amount of data collected from process industry has increased dramatically, essentially containing almost all information regarding both process operation and equipment condition, but the actual industrial situation has created a great challenge to both academia and industry due to frequent adjustments of operation and gradual changes of equipment condition, which leave us with an embarrassing dichotomy of rich data and poor information.

Recent development in all fields related to this topic, including but not limited to chemical engineering, data acquisition, data analysis, pattern recognition, information theory, and machine learning, has provided a good opportunity to tackle this problem.

The aim of this issue is to present methodological, theoretical, and practical developments related to “Process Monitoring and Fault Diagnosis”. Potential topics include but are not limited to the following:

Early detection of process abnormality in industrial practice;
Feature extraction of normality under multi-steady and non-steady states;
Dynamics feature extraction under different control strategies;
Root cause identification based on both process knowledge and data analytics;
Integration of first principle model and data information;
Application of machine learning methods;
Soft sensor development;
Data processing for feature extraction;
Bottleneck analysis based on multiscale process data analysis;
New method for batch process monitoring;
Modeling and analysis of non-stationary signals;
Metrics for fault detection.

Prof. Dr. Wei Sun
Guest Editor

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.

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 polices can be found here.

Published Papers (11 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Editorial

Jump to: Research, Review

4 pages, 166 KiB

Open AccessEditorial

Special Issue on “Process Monitoring and Fault Diagnosis”

by Cheng Ji and Wei Sun

Processes 2024, 12(7), 1432; https://doi.org/10.3390/pr12071432 - 9 Jul 2024

Viewed by 562

Abstract

The following Special Issue entitled “Process Monitoring and Fault Diagnosis” aims to explore the latest progress and perspectives on the application of data analytic techniques to enhance stable operation and safety in chemical processes and other related process industries [...] Full article

(This article belongs to the Special Issue Process Monitoring and Fault Diagnosis)

Research

Jump to: Editorial, Review

16 pages, 1131 KiB

Open AccessArticle

Advancing Fault Prediction: A Comparative Study between LSTM and Spiking Neural Networks

by Rute Souza de Abreu, Ivanovitch Silva, Yuri Thomas Nunes, Renan C. Moioli and Luiz Affonso Guedes

Processes 2023, 11(9), 2772; https://doi.org/10.3390/pr11092772 - 16 Sep 2023

Cited by 6 | Viewed by 1427

Abstract

Predicting system faults is critical to improving productivity, reducing costs, and enforcing safety in industrial processes. Yet, traditional methodologies frequently falter due to the intricate nature of the task. This research presents a novel use of spiking neural networks (SNNs) in anticipating faults in syntactical time series, utilizing the generalized stochastic Petri net (GSPN) model. The inherent ability of SNNs to process both time and space aspects of data positions them as a prime instrument for this endeavor. A comparative evaluation with long short-term memory (LSTM) networks suggests that SNNs offer comparable robustness and performance. Full article

(This article belongs to the Special Issue Process Monitoring and Fault Diagnosis)

► Show Figures

Figure 1

19 pages, 2926 KiB

Open AccessArticle

Fault Diagnosis of Bearings Using Wavelet Packet Energy Spectrum and SSA-DBN

by Jinglei Qu, Xueli Cheng, Ping Liang, Lulu Zheng and Xiaojie Ma

Processes 2023, 11(7), 1875; https://doi.org/10.3390/pr11071875 - 22 Jun 2023

Cited by 5 | Viewed by 1392

Abstract

To enhance fault characteristics and improve fault detection accuracy in bearing vibration signals, this paper proposes a fault diagnosis method using a wavelet packet energy spectrum and an improved deep confidence network. Firstly, a wavelet packet transform decomposes the original vibration signal into different frequency bands, fully preserving the original signal’s frequency information, and constructs feature vectors by extracting the energy of sub-frequency bands via the energy spectrum to extract and enhance fault feature information. Secondly, to minimize the time-consuming manual parameter adjustment procedure and increase the diagnostic accuracy, the sparrow search algorithm–deep belief network method is proposed, which utilizes the sparrow search algorithm to optimize the hyperparameters of the deep belief networks and reduce the classification error rate. Finally, to verify the effectiveness of the method, the rolling bearing data from Casey Reserve University were selected for verification, and compared to other commonly used algorithms, the proposed method achieved 100% and 99.34% accuracy in two sets of comparative experiments. The experimental results demonstrate that this method has a high diagnostic rate and stability. Full article

(This article belongs to the Special Issue Process Monitoring and Fault Diagnosis)

► Show Figures

Figure 1

18 pages, 5674 KiB

Open AccessArticle

A Novel Prediction Method Based on Bi-Channel Hierarchical Vision Transformer for Rolling Bearings’ Remaining Useful Life

by Wei Hao, Zhixuan Li, Guohao Qin, Kun Ding, Xuwei Lai and Kai Zhang

Processes 2023, 11(4), 1153; https://doi.org/10.3390/pr11041153 - 9 Apr 2023

Cited by 11 | Viewed by 1720

Abstract

Accurate prediction of the remaining useful life (RUL) of rolling bearings can effectively ensure the safety of complicated machinery and equipment in service. However, the diversity of rolling bearing degradation processes makes it difficult for deep learning-based RUL prediction methods to improve prediction accuracy further and provide generalizability for engineering applications. This study proposed a novelty RUL prediction model for rolling bearings based on a bi-channel hierarchical vision transformer to reduce the impact of the above problems on prediction accuracy improvement. Firstly, hierarchical vision transformer network structures based on different-sized patches were employed to extract depth features containing more degradation processes information from input samples. Second, the dual channel fusion method is implemented into classic RUL prediction networks based on a multi-layer fully connected network to improve prediction accuracy. With two distinct validation experimental arrangements utilizing the datasets from PHM 2012, the prediction accuracy of the proposed approach can be increased by up to 9.43% and 43.10%, respectively, compared with the current standard method. The results demonstrate that the proposed method is more suitable for rolling bearing RUL prediction. Full article

(This article belongs to the Special Issue Process Monitoring and Fault Diagnosis)

► Show Figures

Figure 1

25 pages, 1863 KiB

Open AccessArticle

Goal-Oriented Tuning of Particle Filters for the Fault Diagnostics of Process Systems

by Éva Kenyeres and János Abonyi

Processes 2023, 11(3), 823; https://doi.org/10.3390/pr11030823 - 9 Mar 2023

Cited by 2 | Viewed by 1544

Abstract

This study introduces particle filtering (PF) for the tracking and fault diagnostics of complex process systems. In process systems, model equations are often nonlinear and environmental noise is non-Gaussian. We propose a method for state estimation and fault detection in a wastewater treatment system. The contributions of the paper are the following: (1) A method is suggested for sensor placement based on the state estimation performance; (2) based on the sensitivity analysis of the particle filter parameters, a tuning method is proposed; (3) a case study is presented to compare the performances of the classical PF and intelligent particle filtering (IPF) algorithms; (4) for fault diagnostics purposes, bias and impact sensor faults were examined; moreover, the efficiency of fault detection was evaluated. The results verify that particle filtering is applicable and highly efficient for tracking and fault diagnostics tasks in process systems. Full article

(This article belongs to the Special Issue Process Monitoring and Fault Diagnosis)

► Show Figures

Figure 1

12 pages, 1411 KiB

Open AccessArticle

Application of Improved PNN in Transformer Fault Diagnosis

by Xunyou Zhang and Zuo Sun

Processes 2023, 11(2), 474; https://doi.org/10.3390/pr11020474 - 4 Feb 2023

Cited by 9 | Viewed by 2081

Abstract

A transformer is an important part of the power system. Existing transformer fault diagnosis methods are still limited by the accuracy and efficiency of the solution and excessively rely on manpower. In this paper, a novel neural network is designed to overcome this issue. Based on the traditional method of judging the ratio of dissolved gas in transformer internal insulation oil, a fast fault diagnosis model of a transformer was built with an improved probabilistic neural network (PNN). The particle swarm optimization (PSO) algorithm was used to find the global optimal smoothing factor and improve the fault diagnosis accuracy of PNN. The transformer fault diagnosis model based on improved PNN not only eliminates the influence of human subjective factors but also significantly improves the diagnosis speed and accuracy, meeting the requirements for real-time application in practical projects. The feasibility and effectiveness of the method proposed in this paper are illustrated by a case study of actual data. Through analysis and comparison, the diagnostic accuracy of the proposed method is 10% higher than that of the general BPNN and 5% higher than that of the traditional PNN on the premise of ensuring the efficiency of the solution. Full article

(This article belongs to the Special Issue Process Monitoring and Fault Diagnosis)

► Show Figures

Figure 1

14 pages, 7641 KiB

Open AccessArticle

Gas Pipeline Leakage Detection Method Based on IUPLCD and GS-TBSVM

by Haiou Shan and Yongqiang Zhu

Processes 2023, 11(1), 278; https://doi.org/10.3390/pr11010278 - 15 Jan 2023

Cited by 2 | Viewed by 1632

Abstract

To improve the identification accuracy of gas pipeline leakage and reduce the false alarm rate, a pipeline leakage detection method based on improved uniform-phase local characteristic-scale decomposition (IUPLCD) and grid search algorithm-optimized twin-bounded support vector machine (GS-TBSVM) was proposed. First, the signal was decomposed into several intrinsic scale components (ISC) by the UPLCD algorithm. Then, the signal reconstruction process of UPLCD was optimized and improved according to the energy and standard deviation of the amplitude of each ISC, the ISC components dominated by the signal were selected for signal reconstruction, and the denoised signal was obtained. Finally, the TBSVM was optimized using a grid search algorithm, and a GS-TBSVM model for pipeline leakage identification was constructed. The input of the GS-TBSVM model was the data processed by the IUPLCD algorithm, and the output was the real-time working conditions of the gas pipeline. The experimental results show that IUPLCD can effectively filter the noise in the signal and GS-TBSVM can accurately judge the working conditions of the gas pipeline, with a maximum identification accuracy of 98.4%. Full article

(This article belongs to the Special Issue Process Monitoring and Fault Diagnosis)

► Show Figures

Figure 1

24 pages, 9410 KiB

Open AccessArticle

Distributed Robust Dictionary Pair Learning and Its Application to Aluminum Electrolysis Industrial Process

by Jingkun Wang, Xiaofang Chen, Ziqing Deng, Hongliang Zhang and Jing Zeng

Processes 2022, 10(9), 1850; https://doi.org/10.3390/pr10091850 - 14 Sep 2022

Cited by 4 | Viewed by 1643

Abstract

In modern industrial systems, high-dimensional process data provide rich information for process monitoring. To make full use of local information of industrial process, a distributed robust dictionary pair learning (DRDPL) is proposed for refined process monitoring. Firstly, the global system is divided into several sub-blocks based on the reliable prior knowledge of industrial processes, which achieves dimensionality reduction and reduces process complexity. Secondly, a robust dictionary pair learning (RDPL) method is developed to build a local monitoring model for each sub-block. The sparse constraint with

l_{2, 1}

norm is added to the analytical dictionary, and a low rank constraint is applied to the synthetical dictionary, so as to obtain robust dictionary pairs. Then, Bayesian inference method is introduced to fuse local monitoring information to global anomaly detection, and the block contribution index and variable contribution index are used to realize anomaly isolation. Finally, the effectiveness of the proposed method is verified by a numerical simulation experiment and Tennessee Eastman benchmark tests, and the proposed method is then successfully applied to a real-world aluminum electrolysis process. Full article

(This article belongs to the Special Issue Process Monitoring and Fault Diagnosis)

► Show Figures

Figure 1

17 pages, 5933 KiB

Open AccessArticle

R-CNN-Based Large-Scale Object-Defect Inspection System for Laser Cutting in the Automotive Industry

by Donggyun Im and Jongpil Jeong

Processes 2021, 9(11), 2043; https://doi.org/10.3390/pr9112043 - 15 Nov 2021

Cited by 4 | Viewed by 2457

Abstract

A car side-outer is an iron mold that is applied in the design and safety of the side of a vehicle, and is subjected to a complicated and detailed molding process. The side-outer has three features that make its quality inspection difficult to automate: (1) it is large; (2) there are many objects to inspect; and (3) it must fulfil high-quality requirements. Given these characteristics, the industrial vision system for the side-outer is nearly impossible to apply, and indeed there is no reference for an automated defect-inspection system for the side-outer. Manual inspection of the side-outer worsens the quality and cost competitiveness of the metal-cutting companies. To address these problems, we propose a large-scale Object-Defect Inspection System based on Regional Convolutional Neural Network (R-CNN; RODIS) using Artificial Intelligence (AI) technology. In this paper, we introduce the framework, including the hardware composition and the inspection method of RODIS. We mainly focus on creating the proper dataset on-site, which should be prepared for data analysis and model development. Additionally, we share the trial-and-error experiences gained from the actual installation of RODIS on-site. We explored and compared various R-CNN backbone networks for object detection using actual data provided by a laser-cutting company. The Mask R-CNN models using Res-net-50-FPN show Average Precision (AP) of 71.63 (Object Detection) and 86.21 (Object Seg-mentation), which indicates a better performance than that of other models. Full article

(This article belongs to the Special Issue Process Monitoring and Fault Diagnosis)

► Show Figures

Figure 1

21 pages, 960 KiB

Open AccessEditor’s ChoiceArticle

Robust Detection of Minute Faults in Uncertain Systems Using Energy Activity

by Manarshhjot Singh, Anne-Lise Gehin and Belkacem Ould-Boaumama

Processes 2021, 9(10), 1801; https://doi.org/10.3390/pr9101801 - 11 Oct 2021

Cited by 3 | Viewed by 1863

Abstract

Fault detection is one of the key steps in Fault Detection and Isolation (FDI) and, therefore, critical for subsequent prognosis or implementation of Fault Tolerant Control (FTC). It is, therefore, advisable to utilize detection algorithms which are quick and can detect the smallest faults. Model-based detection methods satisfy both these criteria and should be preferred. However, a big limitation for model-based methods is that they require the accurate value of the component parameters, which is difficult to obtain in real situations. This limits the accuracy of model-based methods. This paper proposes a new method for fault detection using Energy Activity (EA) which can detect minute levels of fault in systems with high component uncertainty. Different forms of EA are developed for use as an FDI metric. The proposed forms are simulated using a two-tank system under various types of faults. The results are compared with each other and with the traditional model-based FDI method using Analytical Redundancy Relations (ARRs). The simulations are performed considering model uncertainties to check the inherent performance of the methods. From initial simulations, it is established that the integral form of EA is most suited for fault detection. The integral for if EA is then tested using a real two-tank system considering both the model and measurement uncertainties. Full article

(This article belongs to the Special Issue Process Monitoring and Fault Diagnosis)

► Show Figures

Figure 1

Review

Jump to: Editorial, Research

36 pages, 1102 KiB

Open AccessFeature PaperReview

A Review on Data-Driven Process Monitoring Methods: Characterization and Mining of Industrial Data

by Cheng Ji and Wei Sun

Processes 2022, 10(2), 335; https://doi.org/10.3390/pr10020335 - 10 Feb 2022

Cited by 59 | Viewed by 7694

Abstract

Safe and stable operation plays an important role in the chemical industry. Fault detection and diagnosis (FDD) make it possible to identify abnormal process deviations early and assist operators in taking proper action against fault propagation. After decades of development, data-driven process monitoring technologies have gradually attracted attention from process industries. Although many promising FDD methods have been proposed from both academia and industry, challenges remain due to the complex characteristics of industrial data. In this work, classical and recent research on data-driven process monitoring methods is reviewed from the perspective of characterizing and mining industrial data. The implementation framework of data-driven process monitoring methods is first introduced. State of art of process monitoring methods corresponding to common industrial data characteristics are then reviewed. Finally, the challenges and possible solutions for actual industrial applications are discussed. Full article

(This article belongs to the Special Issue Process Monitoring and Fault Diagnosis)

► Show Figures

Journal Menu

Journal Browser

Process Monitoring and Fault Diagnosis

Share This Special Issue

Special Issue Editor

Special Issue Information

Benefits of Publishing in a Special Issue

Published Papers (11 papers)

Editorial

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI