Fault Diagnosis In Chemical Processes Research Articles

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.

Fault diagnosis of chemical process based on SE-ResNet-BiGRU neural network

In order to enhance the accuracy and reliability of fault diagnosis in chemical processes, this paper proposes a methodology for chemical process fault diagnosis based on an improved SE-ResNet-BiGRU neural network. Initially, the ResNet model is enhanced by incorporating the SENet mechanism, enabling the extraction of features from input data and selectively enhancing them, thereby strengthening the model’s ability to capture crucial features. Subsequently, the BiGRU model is employed to perform temporal modeling on the extracted features, allowing for better capture of dynamic changes in fault signals. In order to validate the effectiveness of this approach, experiments are conducted using the TE chemical process dataset. The results are analyzed using methods such as ROC-AUC, confusion matrix, and t-SNE visualization. The improved SE-ResNet-BiGRU model achieves a testing accuracy of 97.78% and an average fault diagnosis rate of 97.24%. Compared to other deep learning methods, this methodology exhibits significant improvements in fault diagnosis rate and reliability. It holds promising potential as an essential tool for fault diagnosis in chemical processes, contributing to enhanced production safety, efficiency, and reduced risk of accidents.

Fault diagnosis with high accuracy and timeliness for semi‐batch crystallization process based on deep learning with multiple pattern representation

AbstractThe research on chemical process fault diagnosis has made significant progress, but there is still a big gap in its application to complex practical industrial processes. As for the fault diagnosis of batch crystallization processes, the recently‐proposed dynamic time warping–convolutional neural network (DTW‐CNN) model has achieved a great improvement in the fault diagnosis. However, its fault diagnosis rate (FDR) and timeliness of fault diagnosis are still low, and thus, it needs to improve further before being applied to the practical application. In this paper, a multiple pattern representation–convolutional neural network (MPR‐CNN) model is proposed and applied for the fault diagnosis of a semi‐batch crystallization process. The MPR‐CNN model enables the manual extraction of features with four pattern representation algorithms in the data pre‐processing stage, and generates a three‐dimensional matrix which is used as the training sample and input to the CNN for the formal feature extraction and weight learning. An excellent classification performance, with an average FDR of 97.5%, is achieved. This model is also applied for the fault diagnosis of process data within a shorter period of time after the occurrence of faults. The results indicate that the model could make timely fault diagnosis with a highly stable and accurate performance after the occurrence of a fault.

Domain generalization of chemical process fault diagnosis by maximizing domain feature distribution alignment

Fault detection and diagnosis (FDD) is crucial for ensuring safety and quality control in chemical industry. But data-driven FDD methods are usually trained with samples under certain operating conditions or data domains. This leads to their performance degradation caused by domain shifts in practice. To address this, a new FDD network, termed the DFDA model, has been designed to achieve domain generalization. Distribution of features extracted from fault samples is progressively aligned by supervised contrastive learning, learning vector quantization and gradient reversal adversarial learning in the DFDA model. So that FDD using such aligned features can potentially offer enhanced domain generalization capabilities. The performance of the DFDA model is benchmarked against other state-of-the-art FDD frameworks using data from various domains/modes of the Tennessee Eastman Process and an actual industrial process. The experimental outcomes indicate that the DFDA model consistently outperforms others in five distinct tasks, achieving an average Fault Detection Rate (FDR) that is 23% higher than that of ViT and 16% higher than CausalViT.

Causal temporal graph attention network for fault diagnosis of chemical processes

Fault detection and diagnosis (FDD) plays a significant role in ensuring the safety and stability of chemical processes. With the development of artificial intelligence (AI) and big data technologies, data-driven approaches with excellent performance are widely used for FDD in chemical processes. However, the improved predictive accuracy has often been achieved through increased model complexity, which turns models into black-box methods and causes the uncertainty regarding their decisions. In this study, a causal temporal graph attention network (CTGAN) is proposed for fault diagnosis of chemical processes. A chemical causal graph is built by causal inference to represent the propagation path of faults. Attention mechanism and chemical causal graph were combined to help us notice the key variables relating to fault fluctuations. Experiments in the Tennessee Eastman (TE) process and the Green Ammonia (GA) process showed that CTGAN achieved high performance and good explainability.

Swarm intelligence based deep learning model via improved whale optimization algorithm and Bi-directional long short-term memory for fault diagnosis of chemical processes

The chemical production process typically possesses complexity and high risks. Effective fault diagnosis is a key technology for ensuring the reliability and safety of chemical production processes. In this study, a comprehensive fault diagnosis method based on time-varying filtering empirical mode decomposition (TVF-EMD), kernel principal component analysis (KPCA), and an improved whale optimization algorithm (WOA) to optimize bi-directional long short-term memory (BiLSTM) is proposed. This research utilizes TVF-EMD and KPCA to analyze and preprocess the raw data, eliminating noise and and reducing the dimensions of the fault data. Subsequently, BiLSTM is employed for fault data classification. To address the hyperparameters within BiLSTM, the enhanced WOA is used for optimization. Finally, the efficacy and superiority of this approach are validated through two fault diagnosis examples.

Physical Graph-Based Spatiotemporal Fusion Approach for Process Fault Diagnosis.

The rapid development of big data technology and machine learning has increasingly focused attention on fault diagnosis in complex chemical processes. However, data-driven approaches often overlook the inherent physical correlations within the system and lack a robust mechanism for providing trusted explanations for fault diagnosis. To address this challenge, a graph-based fault diagnosis model framework is proposed along with a dependable fault node diagnosis analysis method. In order to enhance the extraction of chemical process features from a spatial perspective, a graph convolution network (GCN)-based node spatial encoding module is integrated. The construction of the adjacency matrix involves combining a priori knowledge of chemical processes with Pearson correlation, thereby incorporating the physical correlations between nodes. Simultaneously, to capture temporal dependencies in fault data, a spatiotemporal feature fusion module based on the long short-term memory network (LSTM) is employed. In terms of model training, a dual-supervision strategy is adopted to ensure stable convergence of the multiclass fault diagnosis model. For model inference, a multi-model voting strategy is designed to mitigate accuracy degradation resulting from model prediction bias. To tackle the interpretability challenge, a fault diagnosis analysis method based on node masking is designed, effectively identifying critical nodes contributing to system faults. Experimental validation on the Tennessee Eastman process demonstrates the effectiveness of the proposed model, achieving high accuracy in fault diagnosis. The average fault diagnosis rate for all fault types reaches 0.9844, showcasing state-of-the-art performance in fault diagnosis.

An Order-Invariant and Interpretable Dilated Convolution Neural Network for Chemical Process Fault Detection and Diagnosis

An Order-Invariant and Interpretable Dilated Convolution Neural Network for Chemical Process Fault Detection and Diagnosis

Fault detection and diagnosis for chemical processes using dynamic global–local preserving projections

AbstractMost traditional multivariate statistical monitoring methods require an assumption that the observation values at a certain moment and a past moment are statistically independent. However, in actual chemical and biological processes, the sample at a certain moment is often affected by the previous moment. Therefore, given the problem of more false alarms and poor detection ability based on the traditional principal component analysis, this article proposes a dynamic global–local preserving projections (DGLPP) algorithm. Unlike dynamic local preserving projections (DLPP) and dynamic principal component analysis (DPCA), DGLPP controls the global and local information retained in the dimensionality reduction data by introducing weight coefficients, which makes the algorithm applicable to more types of industrial processes. Moreover, new parameter determination methods are also proposed for improved detection and diagnosis. Through the improved contribution graph method, we can see the influence degree of each variable on the fault, to monitor and isolate the fault. Finally, by verifying the operation of the multivariable process and two practical cases, the results show that compared with DPCA, DLPP, and global local retained projection (GLPP) methods, the performance under this method has been significantly improved.

Chemical Process Fault Diagnosis Method Based on Deep Learning

To address the problem that the traditional fault diagnosis method for chemical processes under big data relies too much on expert experience and fault features are difficult to distinguish, a deep learning-based fault diagnosis method is proposed, which combines convolutional neural network (CNN), long and short-term memory (LSTM) and attention mechanism (AM). In this method, the spatial sequence features of the input signal are extracted by the CNN adaptively, while the LSTM extracts the time-series features of the signal. Finally, the model performance is enhanced by introducing the attention mechanism and using the SoftMax layer as a classifier for fault diagnosis, so that the model can notice the important features of the faults with the interference of noise. Simulation validation of the method in this paper is performed using the TE chemical process data set, and it is demonstrated that the method can be used for chemical process fault diagnosis studies. Finally, compared with other fault diagnosis methods, the method is more accurate and has certain superiority.

Compensation of accuracy by increased data “thickness” for high timeliness in fault diagnosis of chemical process

BackgroundFault diagnosis rate (FDR) and fault diagnosis timeliness (FDT) are critical to a fault diagnosis model applied to industrial processes. Generally, high FDR requires process data of longer time, which means a low timeliness; while, the time-length reduction of data will result in a decreased FDR as learning features from less data is more difficult. MethodsTo address this problem, an idea is proposed that the time-length reduction of process data could be compensated by enhancing the data “thickness”, i.e., the shortage of feature information of data could be compensated by investigating more patterns from multiple different aspects of the data. Based on this, the multiple pattern representation-convolutional neural network (MPR-CNN) is proposed, in which multiple pattern representation algorithms are used first to pre-extract features from data. The resulting three-dimensional matrix contains richer features, which could be learned more efficiently by the CNN. Significant FindingsThe diagnosis results from a continuous and a batch chemical process show that MPR-CNN significantly improves the fault diagnosis performance, which achieved 96.1% of FDR in the Tennessee Eastman process and 98.1% of FDR in a semi-batch crystallization process, and outperforms other CNN structure-based models.

SensorSCAN: Self-supervised learning and deep clustering for fault diagnosis in chemical processes

Modern industrial facilities generate large volumes of raw sensor data during the production process. This data is used to monitor and control the processes and can be analyzed to detect and predict process abnormalities. Typically, the data has to be annotated by experts in order to be used in predictive modeling. However, manual annotation of large amounts of data can be difficult in industrial settings.In this paper, we propose SensorSCAN, a novel method for unsupervised fault detection and diagnosis, designed for industrial chemical process monitoring. We demonstrate our model's performance on two publicly available datasets of the Tennessee Eastman Process with various faults. The results show that our method significantly outperforms existing approaches (+0.2-0.3 TPR for a fixed FPR) and effectively detects most of the process faults without expert annotation. Moreover, we show that the model fine-tuned on a small fraction of labeled data nearly reaches the performance of a SOTA model trained on the full dataset. We also demonstrate that our method is suitable for real-world applications where the number of faults is not known in advance. The code is available at https://github.com/AIRI-Institute/sensorscan.

Imbalanced Chemical Process Fault Diagnosis Using Balancing GAN With Active Sample Selection

Current deep-learning-based fault diagnosis methods, though proven to be successful with sufficient fault data, cannot well address the challenges of sample availability in real-world industrial scenarios. To address this challenge, this paper proposes an effective approach by exploiting data generation and sample selection techniques. Specifically, we first develop a balancing generative adversarial network (BAGAN) based data generation technique to generate more discriminative fault samples by utilizing not only the fault samples but also the normal samples. Second, a strategy is devised to select the samples generated by BAGAN, and on this basis, active learning is utilized to select the most informative samples. The stacked auto-encoder (SAE) based deep neural network (DNN) is used to classify the faults. The proposed method is evaluated by conducting computational experiments on the Tennessee Eastman (TE) dataset. The evaluation results demonstrate that the proposed BAGAN-based method with an active sample selection strategy achieves improved performance in imbalanced chemical fault diagnosis tasks.

CausalViT: Domain generalization for chemical engineering process fault detection and diagnosis

Fault detection and diagnosis (FDD) is a promising technology for safe operation, quality control, and profitability improvement in chemical process systems. In practice, chemical process systems usually switch among operating conditions in response to market demands, fatigue, malfunctions of components and environments. However, FDD models often deteriorate when transferred from one operating condition to another. Current widely used FDD models are trained and tested in identical domains (or operating conditions) without emphasis on the capability of domain generalization. Furthermore, they ignore the causality that remains invariant among domains but excessively rely on statistical correlation that varies from domain to domain. This paper presents an FDD model named CausalViT. Causality and a gradient reversal operation are designed to enhance domain generalization. In CausalViT, a Causal Layer is constructed to discover the causal mechanism in features to purify superfluous domain-specific statistical correlation. Then, a domain classifier cooperating with gradient reversal layers is developed to further enhance the domain invariance. To evaluate the CausalViT, comparisons have been made between it and other advanced FDD models in six domains/modes in Tennessee Eastman Process. CausalViT shows superiority from the perspectives of both fault detection ratio and domain generalization, obtaining an average 16% improvement on domain generalization. Dimension reduction visualization shows the significance of domain generalization and gives a deeper insight into CausalViT. Furthermore, the convergence of the model and the effectiveness of components in the model have been analyzed and demonstrated.

Fault diagnosis of complex chemical process based on multi‐scale ADCRC feature learning

AbstractThe time series and multi‐scale characteristics of complex industrial process data are always important factors affecting the performance of fault diagnosis. In this study, a new fault diagnosis model based on multi‐scale attention dilated causal residual convolution (ADCRC) is proposed. Aiming at the temporal nature of industrial data, the ADCRC module is developed to extract time series features, in which the ADCRC module is composed of dilated causal convolution (DCC), attention mechanism (AM), and residual connection, DCC is used to extract time series features, AM adjusts the weight of features according to attention distribution to obtain more important feature information, and residual connection is used to enhance the training accuracy of model. For the multi‐scale characteristics of original data, MS‐ADCRC model based on ADCRC module is developed for multi‐scale feature extraction, in which multiple ADCRC modules extract multi‐scale features of data in parallel. Finally, the proposed MS‐ADCRC model is tested on the Tennessee‐Eastman data set. Compared with other existing models, the results show that the proposed MS‐ADCRC model has more advantages in fault diagnosis feature learning.

ProTopormer: Toward Understandable Fault Diagnosis Combining Process Topology for Chemical Processes

In modern chemical processes, fault detection and diagnosis (FDD) is a key part of abnormal situation management (ASM). As researchers continue to improve the fault diagnosis performance of different models, the emphasis on model interpretability and explainability studies has increased in recent years. In this paper, a novel model, ProTopormer, was proposed for fault diagnosis of chemical processes. Self-attention mechanism and process topology knowledge were fully combined to achieve high model performance and good interpretability. Experiments on Tennessee Eastman process showed that the model achieved a high diagnosis rate and a low false alarm rate. Attention weights were visualized and quantitatively analyzed to identify key variables for fault diagnosis, which showed strong explanations for the root causes of different faults.

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.

Improving convolutional neural networks for fault diagnosis in chemical processes by incorporating global correlations

Fault diagnosis (FD) has received attention because of its importance in maintaining safe operations of industrial processes. Recently, modern data-driven FD approaches such as deep learning have shown encouraging performance. Particularly, convolutional neural networks (CNNs) offer an alluring capacity to deal with multivariate time-series data converted into images. Nonetheless, existing CNN techniques focus on capturing local correlations. However, global spatiotemporal correlations often prevail in multivariate time-series data from industrial processes. Hence, extracting global correlations using CNNs from such data requires deep architectures that incur many trainable parameters. This paper proposes a novel local–global scale CNN (LGS-CNN) that directly accounts for local and global correlations. Specifically, the proposed network incorporates local correlations through traditional square kernels and global correlations are collected utilizing spatially separated one-dimensional kernels in a unique arrangement. FD performance on the benchmark Tennessee Eastman process dataset validates the proposed LGS-CNN against CNNs, and other state-of-the-art data-driven FD approaches.

An enhanced temporal algorithm- coupled optimized adaptive sparse principal component analysis methodology for fault diagnosis of chemical processes

Principal component analysis (PCA) is a classic fault diagnosis method widely used in chemical process data modeling. However, the limitation of PCA to handle dynamic and time-variant data has been progressively exposed with the ever-increasing development of artificial intelligence technologies and applications of automation systems in the chemical industry. In this paper, an enhanced temporal algorithm-coupled optimized adaptive sparse PCA (ETA-OASPCA) methodology is proposed with the aim to improve the conventional PCA method by introducing temporal state computation and dynamic adaptive sparsity. The ETA is constructed to extract the state transitions of the time-variant process data by introducing the “Transition” and “Hold”, acquire the region of interest by employing a Maximum Fuzzy c-means (MFCM) method, and then use the region time prediction automaton to label the possible abnormal states. The introduction of dynamic adaptive sparsity in PCA, achieved by presenting an iterative interior point optimization method to update the dynamic sparse penalty term, can impose sparsity constraints on dynamic data calculations, thus enhancing the interpretability of the PCA model. The effectiveness of the proposed method was validated through its application in the Eastman Tennessee process, showing that an average fault detection rate, false alarm rate, and latency of 99.4%, 0.37%, and 4.11 mins can be achieved, respectively.

Attention-based long short-term memory fully convolutional network for chemical process fault diagnosis

A correct and timely fault diagnosis is important for improving the safety and reliability of chemical processes. With the advancement of big data technology, data-driven fault diagnosis methods are being extensively used and still have considerable potential. In recent years, methods based on deep neural networks have made significant breakthroughs, and fault diagnosis methods for industrial processes based on deep learning have attracted considerable research attention. Therefore, we propose a fusion deep-learning algorithm based on a fully convolutional neural network (FCN) to extract features and build models to correctly diagnose all types of faults. We use long short-term memory (LSTM) units to expand our proposed FCN so that our proposed deep learning model can better extract the time-domain features of chemical process data. We also introduce the attention mechanism into the model, aimed at highlighting the importance of features, which is significant for the fault diagnosis of chemical processes with many features. When applied to the benchmark Tennessee Eastman process, our proposed model exhibits impressive performance, demonstrating the effectiveness of the attention-based LSTM FCN in chemical process fault diagnosis.