Process Fault Detection Research Articles

Intermittent fault detection in nonstationary processes via a Wald‐based control chart

SummaryIn this article, the problem of intermittent fault (IF) detection is investigated for nonstationary processes in the multivariate statistics framework. By combining the moving window technique with maximum likelihood estimation (MLE), the moving window Wald‐based control chart is proposed to realize the detection of IFs in nonstationary processes. The computational efficiency and the convergence properties are discussed for the designed iterative algorithm of MLE. Then, necessary and sufficient conditions are presented to guarantee the detectability of IFs with the consideration of window lengths. Moreover, the alarm delays are analyzed for the appearance and disappearance of IFs. In virtue of the above analysis, the optimal window length is derived by minimizing the supremum of alarm delays. In order to estimate the time of IFs' appearance and disappearance, an algorithm is designed with the inspiration of simulated annealing strategy. Finally, a simulation on rotary steerable drilling tool system is provided to verify the effectiveness of the proposed method.

Analysis of connection fault and service maintenance strategy for subsea horizontal clamp connector

The deep-sea environment restricts the collection of reliable data for subsea connectors, leading to a lack of available failure data to develop connection fault detection processes and maintenance strategies. Therefore, this paper proposes an FMECA-BN model. The model developed an algorithm for mapping Risk Priority Number (RPN) to Bayesian network (BN) parameters and enhanced the details for mapping BN structures to match the diversity of FMEA. This enables the establishment of a BN based on a given Failure Mode, Effects, and Criticality Analysis (FMECA). The model was applied to the development of the Subsea Horizontal Clamp Connector (SHCC) to quantify and evaluate failure modes during the connection stage, creating an effective detection process. Then, the system's reliability variations caused by component degradation were analyzed using transformed dynamic BN, offering information for maintenance strategies based on availability. The reliability analysis results of the FMECA-BN model demonstrate a statistical error of 4.5% compared to actual data samples, proving the model's efficacy in addressing issues of information asymmetry, incomplete reliability data, and uncertainty. The analysis revealed that the developed SHCC requires a maintenance cycle of 14,892 h to maintain a 0.99 reliability level, with a cumulative failure probability of 4.6% after 20 years.

Gaussian-kernel weighted neighborhood preserving embedding algorithm and its application in fault detection

Fault detection in industrial processes is essential for enhancing production safety. Despite the application of the neighborhood preserving embedding (NPE) algorithm in fault detection as a manifold learning technique, a notable limitation exists-NPE overlooks local geometric structure, leading to suboptimal fault detection and occasional false alarms. This paper introduces the Gaussian kernel weighted NPE (KW-NPE) algorithm to address this challenge. Specifically designed for precise weight assignment in local structures, KW-NPE strategically employs the Gaussian kernel method to project the spatial neighborhood set and capture comprehensive local structural characteristics. The weight assignment, dependent on feature values, enhances the retention of intrinsic structure during dimensionality reduction. A novel objective function further augments this process.To assess performance, a comprehensive composite index is introduced in a case study, amalgamating the false alarm rate and fault detection rate. The effectiveness of the KW-NPE algorithm is demonstrated through extensive simulations and its application to the Tennessee Eastman process dataset, highlighting its superiority over conventional approaches.

A Deep Learning-Based Fault Diagnosis Approach for Power System Equipment via Infrared Image Sensing

Automatic fault diagnosis for power system equipment has always been an essential concern in this industry. Conventionally, such works are conducted by manual patrol inspection, which consumes much human labor and expert knowledge. Fortunately, infrared images can present diagnosis areas inside the equipment via the thermal sensing function. In such context, this work utilizes deep neural network to construct a specific infrared image processing framework that can realize automatic fault diagnosis. Thus in this paper, a deep learning-based fault diagnosis approach for power system equipment via infrared image sensing is proposed. First, a pulse-coupled neural network structure is employed to enhance feature representation for infrared images of the equipment. Next, a fuzzy [Formula: see text]-means (FCM)-based segmentation method is developed to filter diagnosis areas from the infrared images. Finally, a convolution operation-based fault diagnosis operator is adopted to identify the diagnosis types. After that, some simulation experiments are conducted on real-world infrared images on the power system equipment, in order to make the performance evaluation of the proposed approach. The proposal realizes the end-to-end process of feature extraction and fault detection and identification, and avoids the problems of single feature. It is due to manual extraction of fault features, and the inability to detect and identify faults effectively in specific situations and scenarios.

Multirate Nonlinear Process Fault Detection Based on Multiscale Hierarchical Variational Autoencoder

Multirate Nonlinear Process Fault Detection Based on Multiscale Hierarchical Variational Autoencoder

Dimensionality reducing Gaussian mixture‐based reconstruction for fault detection in multimode processes

AbstractModern industrial processes increasingly prioritize demands for safety and reliability, spurring substantial research on process monitoring models. Among existing research subjects, concurrent multimode operating conditions are vital for effective process monitoring. This work proposes an efficient dimensionality‐reducing Gaussian mixture‐based reconstruction approach for multimode industrial process monitoring. The t‐SNE method is first employed to transform high‐dimensional data into a lower‐dimensional space that retains critical operational information. Using these reduced dimensions, a robust Gaussian mixture model is established to partition the operation data into different modes. Furthermore, the original data are assigned to the corresponding operating modes, and local variational autoencoder (VAE) reconstruction models are established, respectively. For each VAE model, two statistics are designed, termed and , to detect abnormalities. The proposed method is applied to a three‐phase flow facility, and the superiority over the comparison methods is proved.

Fault detection and isolation for dynamic non-stationary processes with stationary subspace-based canonical variate analysis

As modern science and technology advance, industrial processes have grown more complex, characterized by dynamic and non-stationary features. Existing methods often focus on single features, necessitating the development of approaches capable of addressing multiple characteristics. This study introduces a novel approach based on stationary subspace canonical variate analysis for fault detection and isolation in dynamic non-stationary processes. The proposed model combines the strengths of stationary subspace analysis and canonical variate analysis (CVA) by introducing new detection indices and their corresponding contributions. These new indices, derived from CVA indices, are further transformed into quadratic form to facilitate easy calculation of contributions, which are based on reconstruction. A numerical example and simulation of the continuous stirred tank reactor process are carried out to demonstrate the superior sensitivity and accuracy of the proposed approach.

Dynamic fault detection and diagnosis of industrial alkaline water electrolyzer process with variational Bayesian dictionary learning

Alkaline Water Electrolysis (AWE) is one of the simplest green hydrogen production method using renewable energy. AWE system typically yields process variables that are serially correlated and contaminated by measurement uncertainty. A novel robust dynamic variational Bayesian dictionary learning (RDVDL) monitoring approach is proposed to improve the reliability and safety of AWE operation. RDVDL employs a sparse Bayesian dictionary learning to preserve the dynamic mechanism information of AWE process which allows the easy interpretation of fault detection results. To improve the robustness to measurement uncertainty, a low-rank vector autoregressive (VAR) method is derived to reliably extract the serial correlation from process variables. The effectiveness of the proposed approach is demonstrated with an industrial hydrogen production process, and RDVDL can efficiently detect and diagnose critical AWE faults.

Integrated Fault Detection System for Underground Lines Using ESP32 and LORA Communication Using IOT

Abstract: This paper introduces an advanced fault detection system tailored for underground line infrastructure, featuring the integration of ESP32 microcontroller technology and LORA communication protocols. The primary objective of this system is to enhance the reliability and efficiency of fault detection processes, crucial for maintaining the integrity of underground lines. At the core of this system lies the ESP32 microcontroller, serving as the central processing unit responsible for coordinating various functionalities. The detection mechanism incorporates six toggle switches strategically positioned along the underground lines, each assigned to monitor a specific location segment. In the event of a fault occurrence, the corresponding toggle switch is triggered, promptly initiating the fault detection process. Upon fault detection, the system seamlessly orchestrates the transmission of GPS location data to a LORA communication device. This data transmission mechanism ensures rapid dissemination of critical information regarding the fault's precise location. Additionally, an LED indicator is employed to provide immediate visual feedback, enabling quick identification of fault occurrences. Moreover, a LCD display unit offers realtime updates on the system's operational status, facilitating efficient monitoring and management. On the receiving end, the LORA communication device securely receives the transmitted data and forwards it to the cloud infrastructure using IOT technology. This cloud integration enables seamless data aggregation, storage, and analysis, empowering stakeholders with valuable insights into underground line health and performance.

CNC machine gearbox fault detection with convolutional neural network

This study focuses on the development of a deep learning-based approach of gearbox monitoring and fault detection. The project aims to create a solution for early detection of defects in dynamic equipment based on data from vibration sensor by building a binary classifier with convolutional neural network implemented. The gearboxes condition of which is being assessed is stored in three similar computer numerically controlled (CNC) milling machines. Data is collected during 15 milling operations of different duration and with different tool’s speed and feed. Vibration is measured by an accelerometer stored on the body of each gearbox. Convolutional neural network takes vibration spectra as inputs and whether fault is detected makes a prediction of a gearbox condition. To make the whole solution autonomous and be able to embed it into manufacture the project is integrated into a server with an edge-to-cloud architecture. As an end product deep learning fault classifier stored on a server is to detect possible gearbox faults, draw conclusions on condition of dynamic equipment and automate the process of fault detection.

Robust Fault Detection in Monitoring Chemical Processes Using Multi-Scale PCA with KD Approach

Effective fault detection in chemical processes is of utmost importance to ensure operational safety, minimize environmental impact, and optimize production efficiency. To enhance the monitoring of chemical processes under noisy conditions, an innovative statistical approach has been introduced in this study. The proposed approach, called Multiscale Principal Component Analysis (PCA), combines the dimensionality reduction capabilities of PCA with the noise reduction capabilities of wavelet-based filtering. The integrated approach focuses on extracting features from the multiscale representation, balancing the need to retain important process information while minimizing the impact of noise. For fault detection, the Kantorovich distance (KD)-driven monitoring scheme is employed based on features extracted from Multiscale PCA to efficiently detect anomalies in multivariate data. Moreover, a nonparametric decision threshold is employed through kernel density estimation to enhance the flexibility of the proposed approach. The detection performance of the proposed approach is investigated using data collected from distillation columns and continuously stirred tank reactors (CSTRs) under various noisy conditions. Different types of faults, including bias, intermittent, and drift faults, are considered. The results reveal the superior performance of the proposed multiscale PCA-KD based approach compared to conventional PCA and multiscale PCA-based monitoring methods.

Optimized data driven fault detection and diagnosis in chemical processes

Fault detection and diagnosis (FDD) is crucial for ensuring process safety and product quality in the chemical industry. Despite the large amounts of process data recorded and stored in chemical plants, most of them are not well-labeled, and their conditions are not adequately specified. In this study, an optimized data-driven FDD model was developed for a chemical process based on automatic clustering algorithms. Due to data preprocessing importance, feature selection was performed by a non-dominated sorting genetic algorithm (NSGAII) based on k-means clustering. The optimal subset of features is selected by comparing clustering results for each subset. The performance of the proposed feature selection method was compared with the Fisher discriminant ratio (FDR), and XGBoost methods. The t-distributed stochastic neighbor embedding (t-SNE), Isomap, and KPCA dimension reduction methods were also employed for feature extraction. Finally, automatic clustering was performed based on metaheuristic algorithms for fault detection and diagnosis. Results were compared with non-automatic clustering methods. The performance of the proposed method was evaluated by examining the Tennessee Eastman and four water tank processes as case studies. The results showed that the proposed method is reliable and capable of online and offline chemical process fault detection and diagnosis. As a result, the findings of this study can be used to stabilize the operation of chemical processes.

Test suite optimization under multi-objective constraints for software fault detection and localization: Hybrid optimization based model

Testing and debugging have been the most significant steps of software development since it is tricky for engineers to create error-free software. Software testing takes place after coding with the goal of finding flaws. If errors are found, debugging would be done to identify the source of the errors so that they may be fixed. Detecting as well as locating defects are thus two essential stages in the creation of software. We have created a unique approach with the following two working phases to generate a minimized test suite that is capable of both detecting and localizing faults. In the initial test suite minimization process, the cases were generated and minimized based on the objectives such as D-score and coverage by the utilization of the proposed Blue Monkey Customized Black Widow (BMCBW) algorithm. After this test suite minimization, the fault validation is done which includes the process of fault detection and localization. For this fault validation, we have utilized an improved Long Short-Term Memory (LSTM). At 90% of the learning rate the accuracy of the presented work is 0.97%, 2.20%, 2.52%, 0.97% and 2.81% is better than the other extant models like AOA, COOT, BES, BMO and BWO methods. The results obtained proved that our Blue Monkey Customized Black Widow Optimization-based fault detection and localization approach can provide superior outcomes.

Application of time‐space neighborhood standardization technology to complex multi‐stage process fault detection

AbstractTo solve the problem that the multi‐stage process with dynamicity and nonlinear is hard to monitor effectively, the time‐space neighborhood standardization (TSNS) method is proposed, which is further applied to partial least squares (PLS) to propose TSNS and PLS (TSNS‐PLS) method for process fault detection. TSNS can transform multi‐stage data into single‐stage data that approximately obeys a standard normal distribution, remove temporal correlation between samples at previous and subsequent moments in the process data, and separate online fault samples. TSNS makes the transformed process data satisfy the requirements of the PLS method for process data and can significantly improve the fault detection rate of the PLS method. Finally, the performance of TSNS‐PLS was examined by a numerical simulation process and the penicillin fermentation process design fault detection experiment.

An integrated approach combining randomized kernel PCA, Gaussian mixture modeling and ICA for fault detection in non-linear processes

Principal component analysis (PCA) and independent component analysis (ICA), as well as their kernel extensions, have been widely applied in the past for industrial fault detection with Gaussian or non-Gaussian process data with linear or non-linear characteristics. Kernel-based techniques lead to computational complexity due to the high dimensionality of the dataset in the feature space. In this work, a randomization approach is used to obtain a low-rank approximation of the high-dimensional kernel matrix. A hybrid machine learning technique is proposed that integrates randomized kernel PCA (RKPCA) with ICA and Gaussian mixture modeling (GMM). The proposed approach, ICA-RKPCA-GMM, addresses the Gaussian and non-Gaussian characteristics of non-linear process data. Another hybrid algorithm combining three basic techniques of ICA, PCA and GMM is also developed (ICA-PCA-GMM). The fault detection performances of the proposed techniques (ICA-RKPCA-GMM and ICA-PCA-GMM) are compared with PCA, ICA, KPCA and combined ICA-PCA techniques by applying the techniques to two benchmark systems. Monitoring performances were evaluated by determining the false alarm rate and fault detection rate for different types of process and sensor faults. The simulation results show that the proposed ICA-RKPCA-GMM approach yields better results than individual ICA, PCA and KPCA techniques, the combined ICA-PCA and the proposed ICA-PCA-GMM technique.

Fault detection of complicated processes based on an enhanced transformer network with graph attention mechanism

Recently, deep learning becomes increasingly popular in the industrial data analysis field due to its distinguished feature representation capability. As an emerging deep learning technology, transformer network has attracted extensive attention, but its application in industrial fault detection is still not sufficiently exploited. Furthermore, the traditional transformer network mainly focuses on the time series information of the data, while ignoring the spatial characteristic of the data. For this problem, an improved transformer network model with graph attention mechanism (GA-Tran) is proposed and used for complicated process fault detection. Considering the strong coupling property of process variables, the presented method performs joint spatial-temporal learning by mining the data information in views of both time-dimension and variable-dimension. On the one hand, a transformer encoder module equipped with a self-attention mechanism is utilized to capture long-term temporal dependencies, while multi-scale convolution is integrated to abstract short-term time dependencies. On the other hand, a graph attention network is introduced into the transformer network to meticulously analyze the spatial relationships among variables. Considering the topology of the industrial process variables, we use spectral clustering to infer prior topology information, and remove intelligently irrelevant spatial information. Different from the existing transformer models with the anomaly scores as the monitoring indicator, two monitoring statistics based on encoder features and reconstruction errors are constructed for process status monitoring. Studies on the Tennessee Eastman chemical process demonstrate the effectiveness of the proposed GA-Tran method.

Multirate Mixture Probability Principal Component Analysis for Process Monitoring in Multimode Processes

In the multirate sampling processes, the process data are usually collected from various operating conditions and display multimodal characteristics. To monitor these multirate multimode processes, a multirate mixture probability principal component analysis model is proposed for process modeling and fault detection. In this model, the local multirate models are built first for each mode and all of them are subsequently fused with the mixture modeling approach. Such model is able to deal with multirate data with various amount of sampling rates, contributing to a remarkable fault detection and mode identification performance by utilizing all the available measurements even if some variables are unobserved. Then the expectation <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$-$</tex-math> </inline-formula> maximum algorithm is utilized to estimate all the model parameters in the probabilistic framework and the corresponding monitoring method is also developed based on the constructed models. Finally, the effectiveness of the proposed method is demonstrated through a PRONTO benchmark and a real multimode ammonia synthesis process. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Motivated by the practical problem of ununiform sampling intervals in multimode processes, this paper proposes a novel multirate mixture probability principle component analysis model for processes modeling and monitoring. In this model, all the available observations with different sampling rates can be incorporated, which contributes greatly to capturing the multimodal characteristics within the industrial processes. Such ability is the key to realize multimode process monitoring, evaluation, fault diagnosis, and process optimization. In addition, although this paper only focuses on the continuous multirate data in industry, it is equally applicable to other forms of multirate data, such as images and videos.

Development of a mathematical model and algorithm for the sequence of fault detection of the ship's hull

The labor intensity of the hull fault detection process, which generally consists of work performed directly on the vessel and the preparation of the final report, can be reduced through the use of modern methods of computer data analysis. The sequence of measurements of defects on the vessel (residual thicknesses, deformations, cement and other sealings, cracks, holes) can be justified by solving a modified traveling salesman problem, reflecting the optimization of the path between points in space, with the goal function in the form of the spent finite time, which is the sum of the time for performing the measurements themselves, moving between the points of such measurements, various technical works, for example, installing a ladder for access to the area located above human growth and its movement to the right sector of work. The article develops a mathematical model of human motion in the space around the vessel, an algorithm of movement. The adaptation of the genetic algorithm for the problem under consideration is given, including the features of solution coding, crossing and mutation of solutions, and choosing the best option. The results of the test debugging simulation for the bow of the river pusher hull showed the operability of the developed algorithm and the possibility of its application for research and practical tasks.

A Novel Distributed Process Monitoring Framework of VAE-Enhanced with Deep Neural Network

Intelligent manufacturing process needs to adopt distributed monitoring scenario due to its massive, high-dimensional and complex data. Distributed process monitoring has been introduced into global monitoring and local monitoring to analyze the characteristic relationship between process data. However, the existing framework methods ignore or suppress the fault information and thus cannot effectively identify the local faults and the time sequence characteristics between units in the batch production system. This paper proposes a novel distributed process monitoring framework based on Girvan-Newman algorithm modular subunit partitioning and probabilistic learning model with deep neural networks. First, Girvan-Newman algorithm is used to divide the complex manufacturing system modularized to reduce the latitude of data processing. Second, variational autoencoder (VAE) is adopted to ensure the stability of local analysis, and long short-term memory is adopted to improve the VAE model to detect global multi-time scale anomalies. Finally, distributed process fault detection is carried out for each subunit in a separate and integrated manner, and the performance of the framework in distributed process monitoring is analyzed through two fault detection indicators T2 and SPE statistics. A case study of the Tennessee Eastman Process is used to demonstrate the performance and applicability of the proposed framework. Results show that the proposed VAE enhancement framework based on the DNN could accurately identify faults in distributed process monitoring and locate the specific sub-units where the fault occurs. Compared with VAE-DNN method and traditional process monitoring methods, the framework proposed in this paper has higher fault detection rate and lower false alarm rate, and the detection rate of some faults can reach 100%.

An ASCON AOP-SystemC Environment for Security Fault Analysis

Cryptographic devices’ complexity necessitates fast security simulation environments against fault attacks. SystemC, a promising candidate in Electronic System Levels (ESLs), can achieve higher simulation speeds while maintaining accuracy and reliability, and its modular and hierarchical design allows for efficient modeling of complex cryptographic algorithms and protocols. However, code modification is required for fault injection and detection. Aspect-Oriented Programming (AOP) can test cryptographic models’ robustness without modifications, potentially replacing real cryptanalysis schemes and reducing the time and effort required for fault injection and detection. Through the utilization of a fault injection/detection environment, this paper presents a novel approach to simulating the security fault attacks of ASCON cryptographic systems at the ESL. The purpose of this methodology is to evaluate the resistance of ASCON SystemC models against fault attacks. The proposed methodology leverages the advantages of AOP to enhance the fault injection and detection process. By applying AOP techniques, we inject faults into the SystemC models without making any changes to the main codebase. This approach not only improves the efficiency of testing cryptographic systems but also ensures that the main functionality remains intact during the fault injection process. The methodology was validated using three scenarios and SystemC ASCON as a case study. The first simulation involved evaluating fault detection capabilities, the second focused on the impact of AOP on executable file size and simulation time, and the third focused on the ESL impact on the ASCON design process. Simulation results show that this methodology can perfectly evaluate the robustness of the ASCON design against fault injection attacks with no significant impact on simulation time and file executable size. Additionally, the simulation results prove that the ASCON development life cycle at the ESL reduces the amount of time devoted to the design procedure by 83.34%, and the ASCON security attack simulations at the ESL decrease the simulation time by 40% compared to the register transfer level (RTL).