Process Fault Detection Research Articles

Dynamic Process Fault Detection and Diagnosis Method Based on Factor Analysis: Application on the Three‐Tank System Process

ABSTRACTTo address the issue of underreporting faults in the detection of tiny faults by dynamic factor analysis (DFA), a novel fault detection and diagnosis method based on DFA‐sliding window combined with mean square error (DFA‐SWMSE) is proposed. Firstly, the data matrix is augmented by introducing time lag shifts. Secondly, factor analysis (FA) is applied to the augmented data matrix, achieving dimensionality reduction and feature extraction while retaining most of the original data's information. Then, the sliding window technique is applied to calculate the mean square error of the dimensionally reduced data, allowing for the monitoring of the system's current state and the detection of tiny faults. Finally, effective fault diagnosis is achieved through the analysis of fault factors and variable contributions. The proposed method is validated using a complex dynamic numerical example and a three‐tank system process named Sim3Tanks. This system has gained widespread application in the field of process fault detection due to its ability to simulate and generate various types of faults. The proposed method is compared with principal component analysis (PCA), dynamic principal component analysis (DPCA), PCA similarity factor (SPCA), FA, and DFA. The experimental results thoroughly validate the effectiveness of the proposed method in detecting and diagnosing tiny faults in dynamic processes.

Unsupervised fault detection using frequency-wise angular filtering in contaminated vibration signals

Manufacturing processes involve multiple machines within a production line. Unexpected faults in machines reduce productivity and increase maintenance costs. Engineers face difficulties in managing numerous machines individually and controlling them immediately. For automatic condition monitoring, several studies have focused on multivariate statistical process control and fault detection based on artificial intelligence. These methods require labeled data or assume that the training data contains only normal patterns. However, obtaining labeled data in the industry is challenging because engineers must manually label the data. Contaminated signals containing fault patterns in unlabeled training data significantly degrade the performance of fault detection in the model. This study proposes Unsupervised fault detection with Frequency-wise Angular Filtering (UFAF) to improve the performance of fault detection in contaminated vibration signals. The UFAF extracts angular features to estimate the normal samples for use only during model training. This filtering strategy is repeated at every epoch and is eventually optimised to use only high-quality normal samples during model training. An experiment using SpectraQuest gearbox datasets confirms the excellent performance for contaminated signals, as angular features are effective in identifying normal and fault signals. The UFAF is practical and applicable in industries wherein it is difficult to collect labeled data.

Bearing Fault Detection in Conveyor Belt Drums Using Machine Learning

In recent years, the application of machine learning techniques in condition monitoring has significantly advanced the precision and efficiency of fault detection processes. In particular, detecting bearing faults in conveyor belt drums is critical in the mining industry for maintaining operational reliability and productivity. This paper presents a case study using vibration signals and diagnostic reports provided by the company Dynamox. After meticulous data cleaning, preprocessing, and feature extraction employing advanced signal processing techniques and statistical features, several machine learning models were trained, optimized and evaluated, with the best models providing very promising results.

A Novel Approach for Fault Detection and Diagnosis in Multi-mode Processes Based on PCA, Random Forest, and K-means Clustering

A Novel Approach for Fault Detection and Diagnosis in Multi-mode Processes Based on PCA, Random Forest, and K-means Clustering

AI-Augmented Fault Detection and Diagnosis in VLSI Circuits: A Step toward Intelligent Chip Design

Fault detection and diagnosis are critical challenges in Very Large Scale Integration (VLSI) circuits, where even minor defects can cause significant performance degradation or system failure. Traditional fault detection methods often struggle with scalability and real-time analysis as circuits grow increasingly complex. This paper proposes an AI-augmented framework that leverages machine learning algorithms and neural networks to enhance the fault detection process in VLSI circuits. The proposed model not only identifies and classifies faults with high accuracy but also provides root-cause diagnostics, significantly reducing testing time and maintenance costs. Simulation results demonstrate the effectiveness of the AI-based approach in comparison with conventional techniques, showcasing improved fault coverage, faster detection, and scalability for modern chip designs. This study serves as a step toward the integration of intelligent diagnostics into chip manufacturing, paving the way for more robust and self-healing electronic systems.

CBS-YOLOv5: fault detection algorithm of electrolyzer plate with low-resolution infrared images based on improved YOLOv5

Abstract In the process of copper electrorefining, accurate detection of electrode plate faults is extremely challenging due to the low resolution of captured infrared images, significant noise interference, and dense electrode plate arrangements. To address these challenges, this paper proposes an improved YOLOv5-based electrode plate fault detection algorithm called CBS-YOLOv5. This algorithm introduces several innovations over the original YOLOv5, including: the incorporation of coordinate attention to enhance the ability of the feature extraction network to separate target information from noise; the construction of a small object detection module to improve the detection of dense small objects by increasing the resolution of the feature map; the replacement of the traditional path aggregation network with a Bi-directional Feature Pyramid Network (BiFPN) for more flexible multi-scale feature fusion; and the integration of the swin transformer to optimize the cross-stage partial bottleneck structure, significantly enhancing the model’s ability to detect densely packed small objects. Experimental results show that the proposed CBS-YOLOv5 model achieves an accuracy of 88.1%, which is an improvement of 5.7% over the base model. Furthermore, this algorithm demonstrates exceptional detection capabilities for dense small objects in low-resolution infrared images while maintaining real-time detection speed, making it suitable for various complex industrial scenarios, including fault detection in non-ferrous metal electrolysis processes. CBS-YOLOv5 not only improves detection accuracy and robustness but also has broad application prospects, offering a new solution for intelligent manufacturing and industrial inspection.

Industrial Process Fault Detection Based on IGA‐Combinatorial Model Decision Mechanism

ABSTRACTTo address the challenges of extracting features from complex industrial process data, the reliance of numerous fault detection methodologies on presupposed data distribution types, and the limited generalization capacity of fault detection, this manuscript introduces a sophisticated algorithm for industrial process fault detection. This algorithm harnesses the information gain adaptive (IGA) technique for feature selection and a synergistic model decision mechanism. Initially, the process involves the computation of information gain via decision trees, coupled with the determination of the value through cross‐validation. This strategy enables the adaptive selection of features, thereby facilitating data dimensionality reduction and effective feature extraction. The subsequent phase introduces a ternary statistical measure monitoring group for the detection of linear faults, while autoencoders and one‐class SVM methodologies are applied for the monitoring of nonlinear faults. The culmination of this approach is the development of an innovative weighted decision mechanism, designed to amalgamate the findings from both linear and nonlinear detection avenues, yielding more dependable detection results. The validation of this algorithm employs datasets from the water chillers process and Tennessee Eastman (TE) process, demonstrating the IGA‐combined model's superior performance over isolated linear or nonlinear detection algorithms in terms of detection accuracy and robustness. Notably, the efficacy of this method is not contingent upon specific assumptions regarding data distribution, rendering it a versatile and efficacious tool for the fault detection in industrial processes.

Information enhanced slow feature analysis integrated with prior fault data for sensitive monitoring of chemical processes

As an effective feature extraction method, slow feature analysis (SFA) has been successfully applied in the domain of chemical process monitoring. However, as an unsupervised method, the basic SFA ignores the key information carried by prior fault data so that many complicated faults cannot be sensitively detected. To address this issue, an enhanced SFA method, termed Information Enhanced SFA (IE-SFA), is proposed by integrating available fault discriminant information to achieve more sensitive detection of complicated chemical process faults. The proposed method builds a primary-auxiliary SFA modeling framework. On the one hand, the normal training data are analyzed to establish the primary SFA model. On the other, prior fault data are integrated to develop an auxiliary monitoring model for providing extra fault-sensitive information. In the auxiliary model, a sample-variable weighted Fisher discriminant analysis method is performed on normal data, fault data, and their respective slow feature components, thereby extracting fault-sensitive features for enhancing the fault detection capability. For full-view fault monitoring, the Bayesian fusion strategy is used to fuse the outcomes from both primary and auxiliary models. The applications on a benchmark Tennessee Eastman chemical process and a three-phase flow process demonstrate that the proposed IE-SFA method provides superior fault detection performance compared to the basic SFA method.

Novel deep‐learning model for chemical process fault detection based on DCW transformer

AbstractIn this study, a double‐channel convolutional neural network and weighted (DCW) transformer model is proposed to address the problem of insufficient extraction of local information and no attention to channel‐step information in the traditional transformer model. First, a double‐channel information extraction method is proposed, so both the channel‐step and time‐step information achieve attention; second, the local information in the time and channel dimension of the data is extracted from deep and multiple scales, improving the feature extraction capability for local information; third, the long distance dependency relationship of the data is preserved by the attention mechanism, hence, the global correlation of the data is extracted effectively; finally, using the Gumbel‐SoftMax function, the weights of the time‐step and channel‐step feature information are assigned, so the extracted feature information has been optimized. The proposed method was applied for the penicillin fermentation process to verify its efficacy. Experimental results show that the proposed method achieved a better fault detection accuracy, outperforming the existing models. Further ablation experiments were conducted to demonstrate the effectiveness of each component of the proposed model.

Advancements in Bearing Defect Diagnosis: Deep Learning-based Signal Processing and Real-time Fault Detection

Advancements in Bearing Defect Diagnosis: Deep Learning-based Signal Processing and Real-time Fault Detection

BibMon: An open source Python package for process monitoring, soft sensing, and fault diagnosis

This paper introduces BibMon, a Python package that provides predictive models for data-driven fault detection and diagnosis, soft sensing, and process condition monitoring. Key features include regression and reconstruction models, preprocessing pipelines, alarms, and visualization through control charts and diagnostic maps. BibMon also includes real and simulated datasets for benchmarking, comparative performance analysis of different models, and hyperparameter tuning. The package is designed to be highly extensible, allowing for easy integration of new models and methodologies through its object-oriented implementation. Currently, BibMon is in production at Petrobras, a major player in the energy industry, monitoring numerous industrial assets and enabling real-time detection and diagnosis of equipment and process faults. The software is open source and available at: https://github.com/petrobras/bibmon.

Enhanced Fault Diagnosis in IoT: Uniting Data Fusion with Deep Multi-Scale Fusion Neural Network

One of the biggest challenges is figuring out when industrial IoT devices break. Notwithstanding these difficulties, one of the many advantages of using real-time sensor data from the Industrial Internet of Things (IIoT) is the ability to monitor events and respond promptly. The IIoT's sensor numbers may be analysed, combined with data from other sources, and applied quickly to make decisions. This manuscript proposes Enhanced Fault Diagnosis in IoT, Uniting Data Fusion with Deep Multi-Scale Fusion Neural Network (FD-IoT-DMSFNN). Initially, input sensor data are taken from the CWRU Dataset. Then, sensor data is normalised using Multivariate Fast Iterative Filtering. Then, the normalised sensor data are extensively dispersed and diverse; hence, the data outlier detection is performed using the Deep isolation forest (DIF) approach. Therefore, this manuscript proposes a Mexican Axolotl Optimization (MAO) for tuning the weight parameter of DMSFNN, which exhibits an enhanced fault detection process. Python is used to implement the recommended strategy. The proposed method's performance yields a lower completion time of 6.5%, 1.8%, and 4.13%; higher prediction accuracy of 2.26%, 6.71%, and 8.32% compared with existing approaches such as towards deep domain adaptability training methods for industrial IoT edge device soft real-time fault diagnosis (FD-IoT-LSTM), Intelligent Fault Quantitative Identification for the Industrial Internet of Things (IIoT) utilising a particular deep dual reinforcement learning model with insufficient samples (FD-IoT-DRL) and IIoT- based fault identification model for industrial use (FD-IoT-ML-LSTM) respectively.

Improved diffusion mapping combined with procrustes analysis for capturing local-global data structures in industrial process monitoring

BackgroundProcess monitoring, by providing early warnings of abnormal operating states resulting from process faults, facilitates the maintenance of normal production and ensures process safety. In the domain of industrial process monitoring, capturing the local-global structural features of data and acquiring an explicit mapping relationship for dimensionality reduction projection holds significant importance for online fault detection in industrial processes. MethodsThis study introduces an Improved Diffusion Mapping and Procrustes analysis (IDM-P) method for this purpose. Initially, considering the multiscale and correlation among industrial data features, the Mahalanobis distance is incorporated to improve the diffusion mapping algorithm. Utilizing this method allows for the concurrent capture of both local and global data structures, leading to a more efficient extraction of data-representative features, which enhances the accuracy of fault detection. Procrustes analysis is then used to obtain an explicit mapping matrix between high-dimensional data and low-dimensional manifolds, improving the efficiency of the key feature extraction of the new samples. Finally, this matrix is utilized to construct process monitoring statistics for fault detection. Significant FindingsThe method's effectiveness was validated through experiments on the TEP dataset and actual industrial data, demonstrating that IDM-P maintains higher accuracy and achieves optimal fault detection compared to other methods.

Intelligent prediction of incipient fault in vinyl chloride production process based on deep learning

With the development of industrial information technology, deep learning (DL) has been successfully applied in chemical process fault detection. However, the features of incipient faults could not be more evident at the initial stage, which makes it difficult for deep learning to fully mine the feature probability information, resulting in poor performance of incipient fault monitoring. This paper proposes an intelligent predictive model based on mechanistic modeling to predict incipient faults and determine optimal response times for vinyl chloride production (VCP) processes. Firstly, a dynamic simulation of the VCP production process is performed to obtain datasets of early faults. Secondly, data dimensionality reduction is performed by cleverly combining spearman ranking correlation coefficient (SRCC) and slow feature analysis (SFA). Then, a long short-term memory network (LSTM) with attention mechanism (AM) is built to predict the future trends of key variables. Finally, the optimal response time for different types of incipient faults is determined by comparing the effectiveness of various control schemes. Compared with traditional methods, the R2 of the proposed prediction model corresponding to different step sizes can reach 99.7%, 99.5%, 99.2%, and 98.7%. In addition, the response times for single and parallel faults are 2 and 2.5 h, which helps to control and eliminate potential incidents in advance.

An approach to automatic fault detection in four-point system for knitted fabric with our benchmark dataset Isl-Knit

Knit fabric is one of the dominating fabric types for wearable textile across the whole world and during the production of knit fabric, faults created by different reasons cause difficulties in the subsequent process. The existing fault detection process in the knitting industry is done manually by the human naked eye. To detect faults automatically, deep learning-based models are very efficient and can reduce the workload for fabric inspection. However, implementing a deep learning-based model for fabric fault detection needs a large number of images with different types of knit fabric faults from real-world scenarios to train and for better performance. Most of the existing fabric fault datasets are based on woven fabric, which cannot be used for training because the faults of woven fabric and knit fabrics are completely different. In this research, we propose a new dataset named ISL-Knit. It is a benchmark dataset for knit fabric faults collected from different knit dyeing industries in Bangladesh. Our dataset contains high-resolution images of grey fabric and dyed fabric annotated with 7 types of faults. To provide correct annotation, all images of faults are annotated and verified with the proper reference which can be a great tool for boosting up deep learning-based models for automatic knit fabric fault detection in the textile field. A comparison of the accuracy of the deep learning-based model by training with our proposed dataset and existing knit fabric fault dataset is also done. To develop an automatic fault detection system, we trained the YOLOv5 models with our dataset, considering different image sizes. For real-world implementation, the best models are selected considering mAP and inference time. YOLOv5 models are implemented in Raspberry Pi with a Raspberry Pi camera, a laptop with no dedicated GPU, and a laptop with a dedicated GPU with a web camera to observe the performance. Finally, an automated four-point inspection system is developed by calibrating the camera to generate the score representing the quality of fabrics. The implementation represents the feasibility of a deep learning-based model in knit fabric fault detection, considering the cost and accuracy.

Fault Detection in Industrial Wastewater Treatment Processes Using Manifold Learning and Support Vector Data Description

Fault Detection in Industrial Wastewater Treatment Processes Using Manifold Learning and Support Vector Data Description

A novel fault detection and identification method for complex chemical processes based on OSCAE and CNN

The safety and reliability of chemical processes are of paramount importance. However, due to the complicated heat, mass, and momentum transfer processes coupled with chemical reactions, and the interrelated effects among various equipment units, it is difficult to accurately detect and identify faults occurring in the chemical system. This study proposes an innovative fault diagnosis framework specifically designed for chemical systems. A method based on orthogonal self-attention convolutional autoencoder (OSCAE) is constructed for fault detection. The convolutional autoencoder is combined with an orthogonal attention mechanism to extract time-series characteristic information of the operational state, thereby achieving precise detection of fault conditions. Additionally, a convolutional neural network (CNN) model uses both raw signals and data reconstructed by OSCAE to identify different faults, which enhances the features of the input and enables highly accurate fault identification. The effectiveness and robustness of the propose method is validated with simulation data of our self-established chemical distillation system and the publicly available Tennessee Eastman process system. The diagnosis efficacy in fault detection and identification is validated by applying fault detection metrics, high-dimensional feature visualizations, and confusion matrix analyses across two case studies. This paper not only provides an effective diagnostic framework for chemical systems but also offers a usable benchmark dataset for chemical distillation systems to the academic community, with the hope that the research content can enhance the stability and reliability of chemical processes.

F-Norm-Based Soft LDA Algorithm for Fault Detection in Chemical Production Processes.

In chemical production processes, outliers are inevitable. Many existing feature extraction algorithms are overly sensitive to outliers and excessively focus on secondary features while ignoring the key features in the data. To address this problem, the Frobenius norm based soft linear discriminant analysis algorithm (FBSLA) is proposed in this paper. Specifically, FBSLA uses the Frobenius norm instead of its square as a metric to enhance the robustness of the algorithm. Furthermore, a nonreduced dimensionality projection matrix is introduced to make the training data features more obvious. Additionally, soft constraint is adopted instead of the traditional hard constraint to reduce the sensitivity caused by outliers. To validate the effectiveness of FBSLA, in this paper, experiments are conducted on the Tennessee Eastman Process and the Penicillin Fermentation Process data sets. According to experimental results, FBSLA significantly outperforms other state-of-the-art algorithms in terms of fault detection accuracy.

Unsupervised Hybrid Models Integrating Deep Autoencoders and Process Controllers’ Models for Enhanced Process Monitoring and Fault Detection

Unsupervised Hybrid Models Integrating Deep Autoencoders and Process Controllers’ Models for Enhanced Process Monitoring and Fault Detection

Industrial process fault detection based on dynamic kernel principal component analysis combined with weighted structural difference

AbstractThe practical application of traditional data‐driven techniques for process monitoring encounters significant challenges due to the inherent nonlinear and dynamic nature of most industrial processes. Aiming at the problem of nonlinear dynamic process monitoring, a novel fault detection method based on dynamic kernel principal component analysis combined with weighted structural difference (DKPCA‐WSD) is proposed in this paper. Initially, the proposed method leverages a sophisticated nonlinear transformation to project the augmented matrix of the original input data into a high‐dimensional feature space, thereby facilitating the establishment of a DKPCA model. Subsequently, the WSD statistic is computed, utilizing a widely known sliding window technique, to quantify the mean and standard deviation differences across data structures. Ultimately, the WSD statistic is utilized for fault detection, completing the process monitoring task. By integrating the capability of DKPCA to capture nonlinear dynamic characteristics with the effectiveness of the WSD statistic in mitigating the impact of non‐Gaussian data distributions, DKPCA‐WSD significantly enhances the monitoring performance of traditional DKPCA in nonlinear dynamic processes. The proposed method is evaluated through a numerical case exhibiting nonlinear dynamic behaviors and a simulation model of a continuous stirred tank reactor. A comparative analysis with conventional methods, including principal component analysis (PCA), dynamic principal component analysis, KPCA, PCA similarity factor (SPCA), DKPCA, and moving window KPCA (MWKPCA), demonstrates that DKPCA‐WSD outperforms traditional fault detection techniques in nonlinear dynamic processes, offering a substantial improvement in monitoring performance.