Industrial Process Fault Research Articles

Data Augmentation and Fault Diagnosis for Imbalanced Industrial Process Data Based on Residual Wasserstein Generative Adversarial Network With Gradient Penalty

ABSTRACTIn practical industrial applications, equipment usually operates normally and failures are relatively rare, resulting in serious imbalances in the collected data. This imbalance leads to issues such as overfitting, instability, and poor robustness, significantly reducing the accuracy and stability of fault diagnosis system. To address these challenges, this research proposes a method for imbalanced data augmentation and industrial process fault diagnosis based on improved Generative Adversarial Network (GAN). The method adopts Wasserstein distance with gradient penalty and integrates residual connections into the architecture of the generator. This innovation not only helps improve gradient transfer in the generator, but also significantly enhances the data generation capabilities of the generative model through improving the stability of training. Limited industrial process data is used by a generative model to produce synthetic samples with high similarity and diversity. These high‐quality samples improve fault diagnosis by enriching the imbalanced dataset. Experimental results on two industrial datasets confirm the method's effectiveness in enhancing fault diagnosis performance with limited data.

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.

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.

Fault detection of industrial processes using attention-based gated recurrent unit autoencoder with skip connection

Abstract With the continuous evolution of modern industrial technology, industrial production has grown progressively complex, necessitating the use of various sensors to measure multiple process variables. However, intricate temporal dependencies and nonlinear relationships between data presented by multivariate sequences pose significant challenges to process fault detection. In response to these challenges, this paper proposes an attention-based gated recurrent unit autoencoder with skip connection (SAGRU-AE) model for monitoring large-scale, nonlinear, and multivariate industrial process faults. SAGRU-AE combines gated recurrent units, multi-head self-attention, and autoencoder to extract features from multivariable time series data efficiently. Concurrently, feature reuse is achieved through the skip connection structure, which improves the accuracy of data reconstruction. Based on the implementation of process data feature extraction and input reconstruction in SAGRU-AE, two statistics have been developed, namely the H2 statistic and square prediction error (SPE) statistic, for fault detection tasks. Ultimately, the feasibility and effectiveness of the proposed algorithms are validated through experimentation on the TE process.

Industrial process fault diagnosis based on video recognition and multi-source information fusion

With the development of technology, industrial monitoring systems can collect not only process data but also video data. Considering the temporal characteristics of video data as well as the correlation and inconsistency between video and process data, and in order to further improve the accuracy of industrial process fault diagnosis, a fault diagnosis method based on video recognition and multi-source heterogeneous information fusion is proposed. In this method, a Visual Geometry Group (VGG) using Adaptive Parameter Rectified Linear Unit (APReLU) embedded Video Vision Transformer (ViViT) is designed firstly to extract the information change of video. It uses the VGG to extract the local information, the Spatial Transformer Encoder to extract global information, and the Temporal Transformer Encoder to extract the temporal features of the sequential video frames. Then one-dimensional Residual Network (ResNet) is adopted to handle the correlated process variables. Since video data and process data reflect different information of industrial processes, Highway Network is used to process the concatenated fusion features of the video data and process data. The Highway Network can assign larger weights to important components, and smaller weights to unimportant components. Experiments show that the accuracy of industrial process fault diagnosis based on video recognition and multi-source data is greater than that based on single-source data.

A deep learning methodology based on adaptive multiscale CNN and enhanced highway LSTM for industrial process fault diagnosis

Intelligent fault diagnostic techniques are crucial for ensuring the long-term reliability of manufacturing. The process variables collected by sensors in real industrial systems typically exhibit diverse time scales and data features. To overcome the above issues, we propose a comprehensive model known as adaptive multiscale CNN and enhanced highway LSTM (ACEL). To begin with, multiscale features under variable operating conditions are automatically extracted by constructing the parallel convolutional module. Then, to selectively focus on the feature representation within the channels, efficient channel attention is introduced to downplay the channels with less important information. In addition, the bidirectional LSTM is designed to generate more fine-grained hybrid features based on contextual information and local features learned by the CNN. An enhanced highway configuration is designed to be used to bolster the global temporal dependencies. Finally, ACEL is applied to the Tennessee Eastman benchmark and CSTR simulation, and multiple statistical metrics show that the proposed model outperforms other advanced fault diagnosis methods.

Industrial process fault diagnosis based on domain adaptive broad echo network

BackgroundIn response to the challenge that traditional fault diagnosis models are difficult to maintain satisfactory accuracy when data distribution changes due to changes in process conditions, a fault diagnosis model of industrial process based on domain adaptive broad echo network (DABEN) is proposed. MethodsThe DABEN model first constructs feature nodes through random feature mapping to extract shallow features of process data, and then inputs feature nodes into cascade reservoirs to extract dynamic features of different levels. On this basis, the objective function of DABEN is constructed, which starts from the four goals of minimizing the prediction error, maximum mean discrepancy distribution alignment, manifold regularization and minimizing cross-domain error to ensure that the features are as similar as possible between the source domain and the target domain. Significant FindingsFinally, two simulation cases show that DABEN can achieve good transfer fault diagnosis performance under different data distributions

Industrial process fault diagnosis based on feature enhanced meta-learning toward domain generalization scenarios

With the rise of the Internet of Things, computing science, and artificial intelligence, data-driven fault diagnosis methods for industrial processes have attracted considerable attention. However, these methods typically assume that the distribution of the test data (target domains) is similar to that of the training data (source domains), making it difficult to deploy them in actual scenarios because the distribution of the collected data varies with operating conditions. Faced with this challenge, a novel fault diagnosis method based on feature enhanced meta-learning (FEML) is proposed to learn domain generalization knowledge effectively. It aims to jointly extract domain-invariant information that can be transferred across domains, and domain-specific information related to classification labels. First, feature extractors based on gated recurrent units (GRUs) are designed to capture domain-invariant and domain-specific representations with time-sequence characteristics from industrial process data. Second, a meta-learning strategy is introduced to enhance the domain-specific representation, thus improving the generalization ability of the model without accessing the target domains. Finally, the Tennessee Eastman process (TEP), Fed-Batch Fermentation Penicillin process (FBFP), and Three-Phase Flow process (TPFP) are used to verify the effectiveness of FEML. The results illustrate that the FEML is robust and outperforms other related methods. The diagnostic accuracies for TEP, FBFP, and TPFP are 87.11%, 95.94%, and 83.07%, respectively.

A network structure for industrial process fault diagnosis based on hyper feature extraction and stacked LSTM

Traditional feature extraction methods are highly dependent on manual work, and manual extraction will inevitably ignore some potentially useful features. For complex systems, there is no simple one-to-one correspondence between faults and symptoms, and fault characteristics often have complex characteristics such as hierarchy, propagation correlation, and delay. In the existing research results, the convolutional neural network (CNN) has been proven to be an effective network structure. But the existing network structure is more like a classifier, and only some features of industrial data are considered in the feature extraction stage, which will cause some feature information to be lost during the training process. To effectively solve the above problems, this paper proposes an industrial process-oriented hyper feature extraction network structure. This method first extracts multi-level CNN fault features, aggregates the hierarchical feature maps and compresses them in a unified feature space to form hyper features. Then the hyper features formed by feature fusion are input to the stacked Long Short Term Memory (LSTM) network for further feature extraction. In this way, the information in the time series can be effectively transmitted, and at the same time, the problem of gradient disappearance caused by long-term training can be solved. We conducted experiments on the Tennessee-Eastman (TE) benchmark process and industrial coking oven datasets. The experimental results show that the proposed method can effectively improve the fault diagnosis accuracy of the industrial production process system.

Time-slice dynamic prediction and multiway serial PCA for batch industrial process monitoring

This paper introduces an innovative method for batch industrial process monitoring and fault detection. By combining time-slice dynamic prediction with an enhanced adaptive multiway principal component analysis (PCA) technique, it aims to balance the trade-off between high fault detection rates and low false alarm rates in monitoring batch industrial data. The method extracts dynamic features from 3D batch data time slices and utilizes a serial PCA framework with variable control limits for real-time monitoring. Comparative evaluations against traditional adaptive multiway PCA and dynamic time-slice canonical correlation analysis approaches demonstrate its superior performance. A case study using a novel cold rolling mill simulation model underscores its effectiveness for batch industrial process monitoring and fault detection.

Dynamic Feature Extraction-Based Quadratic Discriminant Analysis for Industrial Process Fault Classification and Diagnosis.

This paper introduces a novel method for enhancing fault classification and diagnosis in dynamic nonlinear processes. The method focuses on dynamic feature extraction within multivariate time series data and utilizes dynamic reconstruction errors to augment the feature set. A fault classification procedure is then developed, using the weighted maximum scatter difference (WMSD) dimensionality reduction criterion and quadratic discriminant analysis (QDA) classifier. This method addresses the challenge of high-dimensional, sample-limited fault classification, offering early diagnosis capabilities for online samples with smaller amplitudes than the training set. Validation is conducted using a cold rolling mill simulation model, with performance compared to classical methods like linear discriminant analysis (LDA) and kernel Fisher discriminant analysis (KFD). The results demonstrate the superiority of the proposed method for reliable industrial process monitoring and fault diagnosis.

Reconstruction-based stacked sparse auto-encoder for nonlinear industrial process fault diagnosis

The reconstruction-based (RB) approach can effectively suppress the misdiagnosis problem due to the smearing effect in fault isolation. However, the current exploration of the RB approach for large-scale nonlinear systems is still limited. Therefore, this paper proposes a reliable and effective fault diagnosis method based on a reconstruction-based stacked sparse autoencoder (RBSSAE) for high-dimensional industrial systems. In RBSSAE, a reconstruction-based index achieved by the Steffensen iterative method is developed to check whether the given variable(s) are responsible for the faults efficiently. However, the number of possible faulty variable combinations grows exponentially with the system dimension or actual abnormal variables, causing an unbearable computational burden. Hence, the proposed RBSSAE utilizes a sequential floating forward selection approach to rapidly isolate the most decisive variable combination, meeting a requirement of online fault diagnosis. Finally, the effectiveness of the RBSSAE is verified on a numerical example and a real industrial case.

An industrial process fault diagnosis method based on independent slow feature analysis and stacked sparse autoencoder network

Deep learning, with its powerful multilayer nonlinear representation of deep neural networks, enables models trained based on deep learning to describe the true distribution of data more accurately and thus have better generalization capabilities. However, the training data are usually assumed to obey independent identical distribution. Industrial process data nowadays usually have complex characteristics such as non-Gaussian, nonlinear, dynamic, strong correlation, and high-dimensional, which make it difficult to meet the assumptions of deep learning. Using industrial process data directly would result in low model accuracy and bias in the model output. This paper proposes a fault diagnosis method based on independent slow feature analysis (ISFA) and stacked sparse autoencoder (SSAE) network. The statistical properties possessed by ISFA are used to perform preliminary feature extraction of industrial process data, and then the extracted preliminary features are input to the SSAE network for fine-grained feature extraction. The proposed two-step feature extraction method not only makes the data input to the SSAE network as close as possible to obey independent identical distribution and provides convincing fault diagnosis results, but also makes full use of the powerful feature extraction capability of the SSAE network, which makes the proposed method also significantly improve the detection accuracy of incipient faults. The feasibility and superiority of the proposed method is verified through the TE process.

A Novel Distributed Fault Diagnosis Scheme Toward Open-Set Scenarios Based on Extreme Value Theory

Under closed-set scenarios (CSS), distributed modeling performs well in fault diagnosis of plant-wide industrial processes due to its flexibility and robustness. However, a more realistic scenario is often open, where unseen situations may arise unexpectedly, rendering existing methods infeasible. The advent of open-set recognition algorithms that can effectively distinguish known samples and reject unknown ones bridges this gap. Nevertheless, the poor scalability of these algorithms prevents them from being elegantly embedded in popular distributed modeling schemes, which hinders the implementation of plant-wide industrial process fault diagnosis toward open-set scenarios (OSS). In this work, we formulate a novel distributed fault diagnosis scheme toward OSS to solve this problem. Firstly, a mutual information-based local module decomposition and expansion strategy is proposed to minimize the loss of inter-module relevant information. Secondly, a novel generalized basic probability assignments generation technique based on extreme value theory is developed for modeling unknown information. It enables any classifier capable of probabilistic prediction to be applied to OSS and easily embedded in distributed modeling schemes. Finally, a conflict management scheme combining supervised and unsupervised is devised to address the vulnerability of the modified generalized combination rule to counter-intuitive results from fusing conflicting evidence. Experimental results on two plant-wide industrial process datasets demonstrate the proposed approach's feasibility and superiority.

Fusing logic rule-based hybrid variable graph neural network approaches to fault diagnosis of industrial processes

It is practical that industrial processes are regarded as hybrid systems involving both continuous and discrete variables. Particularly, process discrete variables are usually rather crucial to process normal operations. However, existing data-driven fault diagnosis methods have been rarely applied for industrial processes concerning hybrid system characteristics. In response to this issue, this paper proposes a novel industrial process fault diagnosis approach based on fusing logic rule-based hybrid variable graph neural networks (GNNs) able to learn hybrid variable spatial structures. Therein, continuous variables samples are regarded as learning objects, while logic rule information is utilized to guide the learning process. Specifically, a mixed 0–1 integer linear programming algorithm is used to extract logic rules with fault labels from discrete variable data. Subsequently, a rule classification method is used to classify the logic rules with fault labels into independent and interdependent rule classes which are filed in a rule class library using as the basis for topological connections among data samples. In addition, an edge pruning-based graph optimization is employed to reduce redundant connections of the nodes. It is found that the established hybrid variable GNNs is able to learn the spatial topological structure of hybrid industrial process variables, providing rich heuristic knowledge for process fault classifications so as to effectively improve the fault diagnose performance. Simulation experiments are carried out in an industrial coal gasification process platform and a storage tank systems simulation platform, achieving satisfactory results.

Dynamic causal modeling for nonstationary industrial process performance degradation analysis and fault prognosis

Performance degradation analysis and fault prognosis of industrial processes refer to distinguish different degradation stage and raise warnings before a failure occurs, which are crucial to ensure the operation efficiency and safety. However, processes usually have nonstationarity caused by operating condition switching, which may be confused with the underlying temporal variations in the degradation process. Considering that the degradation process is essentially the changes in short-term temporal information transmission between variables, we propose a dynamic causal graph-driven performance degradation analysis and fault prognosis framework (DCG-PDA), which can reveal the intrinsic connection between causality and degradation mechanism. First, a dynamic causal graph framework is constructed, in which the nonstationarity is removed by transfer entropy-guided symbolization, to extract significant short-term causal information transmission. Second, changes in causality are effectively captured and quantified, and the causal graphs corresponding to some time points with larger changes in causality are selected to characterize the representative causal patterns in the degradation process. Finally, a node anomaly score is designed to consider the accumulation of causal structural changes at anomaly time points and measure variable-wise degradation contributions, so as to realize accurate locations for key variables in the degradation process. The validity of the proposed method is illustrated through a real compressor fouling process.

A zero-sample industrial process fault diagnosis model based on joint explicit and implicit attribute transfer

For fault diagnosis of industrial processes, due to the complexity of working conditions and the uncertainty of abnormality, it is difficult to obtain all types of fault samples of target working conditions. To address this issue, a zero-sample industrial process fault diagnosis model based on joint explicit and implicit attribute transfer (ZS-JEIAT) is proposed, which using partial known fault samples to identify otherwise unknown faults. Firstly, we define the fault attribute space based on fault knowledge, and establish the causal relationship between attributes and faults. Next, considering some problems that the model will encounter in complex industrial environments, we divide the attribute space into explicit and implicit attribute spaces by feasibility of attribute transfer. We judge these two spaces and according to the causal relationship between faults and attributes to realize the zero-sample fault diagnosis. Finally, the effectiveness of the proposed method is verified using an actual industrial process.

A Simple Framework to Generalized Zero-Shot Learning for Fault Diagnosis of Industrial Processes

Dear Editor, This letter provides a simple framework to generalized zero-shot learning for fault diagnosis. For industrial process monitoring, supervised learning and zero-shot learning (ZSL) can only deal with seen and unseen faults, respectively. However, in the online monitoring stage of the actual industrial process, both seen and unseen faults may occur. This makes supervised learning and zero-shot learning impractical in industrial process monitoring. Generalized zero-shot learning (GZSL) can handle this problem, but its implementation process is too complicated. This letter introduces GZSL into industrial process fault diagnosis, and a simple end-to-end framework is provided to implement GZSL-based fault diagnosis. In this framework, GZSL-based fault diagnosis can be realized by using only a binary classification algorithm. Experimental results show that the proposed framework can accomplish this challenging task of GZSL for fault diagnosis.

Gated recurrent unit-enhanced deep convolutional neural network for real-time industrial process fault diagnosis

When deep learning-based models are employed for the fault diagnosis of chemical processes, problems of poor calculation accuracy and efficiency often occur in the scenarios of high-dimensional, nonlinear, and time-varying data, which affects the overall robustness of the fault diagnosis model. This study proposes a novel Gated Recurrent Unit (GRU) - enhanced deep convolutional neural network (EDCNN) model for the improved fault detection and diagnosis of chemical processes. In the GRU-EDCNN model, a maximum smooth function (MSF) is developed and presented to replace the classical activation function for better matching the input data format, which can significantly improve the calculation accuracy and the overall efficiency of DCNN models. Moreover, the convolutional layers in DCNN are optimized by a decentralized convolutional structure, thereby reducing the problem of parameter redundancy. The issue of gradient disappearance is tackled by the embedment of GRU, whose ability to control time series features and gateway information can ensure that no overfitting occurs in the GRU-EDCNN training processes. In two case studies, the GRU-EDCNN model was applied to the benchmark Tennessee Eastman (TE) process and an acid gas absorption process, and the corresponding data of fault diagnosis rate (FDR), false positive rate (FPR), and fault diagnosis time (FDT) of the GRU-EDCNN model were analyzed and compared with those of the other deep learning-based models, verifying the effectiveness of the GRU-EDCNN model for fault diagnosis in both simulated and real-time industrial processes.

Farthest-Nearest Distance Neighborhood and Locality Projections Integrated With Bootstrap for Industrial Process Fault Diagnosis

It has become a big challenge and a hot topic of research to capture the most relevant features from high-dimensional process data for enhancing fault diagnosis. To effectively extract discriminative features from high-dimensional data, a novel dimensionality reduction (DR) approach named neighborhood and locality projections with the farthest and nearest distance (FNDNLP) is first proposed for industrial process fault feature acquisition and diagnosis. By constructing intra-class weights and inter-class weights, FNDNLP takes both the intra-class distance and the inter-class distance into consideration in its objective function, improving the diagnostic ability of extracted features through maximizing the inter-class distance and minimizing the intra-class distance. In addition, bootstrap based FNDNLP (BFNDNLP) is further proposed to handle the matrix decomposition problem in FNDNLP. To find the proper order through DR, the Akaike information criterion (AIC) is adopted. Finally, the Naïve Bayes (NB) based classifier is utilized to achieve acceptable fault diagnosis. The simulation results from two complex industrial cases indicate that the proposed methodology can achieve higher diagnosis accuracy than other related methods. What's more, the DR features are further analyzed to show the effectiveness and benefits of the proposed BFNDNLP extraction approach. The main source code of this paper can be obtained on GitHub soon.