Research on Fault Diagnosis of Tennessee Eastman Process Based on KPCA and SVM

Zhang, Ke; Qian, Kun; Chai, Yi; Li, Yi; Liu, Jianhuan

doi:10.1109/iscid.2014.234

Cited by 8 publications

(6 citation statements)

References 10 publications

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“…In the TE simulation process, 21 different process faults can be generated. Faults IDV1–8 are related to step changes in related variables . Faults IDV 9–12 are related to random variation of variables.…”

Section: Application and Benchmarkingmentioning

confidence: 99%

Autonomous Fault Diagnosis and Root Cause Analysis for the Processing System Using One-Class SVM and NN Permutation Algorithm

Arunthavanathan

Khan

Ahmed

et al. 2022

Ind. Eng. Chem. Res.

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In this era of Industry 4.0, there are continuing efforts to develop fault detection and diagnosis methods that are fully autonomous; these methods are self-learning, with little or no human intervention. This paper proposes a methodology for the autonomous diagnosis of the root cause of a detected fault in a complex processing system. The methodology comprises steps to detect and classify any newly encountered fault, classify the known faults, and find the root cause of the detected fault condition. The one-class support vector machine (SVM) model is used in the framework to detect the unlabeled fault, and the neural network is used for fault classification and root cause analysis. The developed methodology is capable of self-updating the fault database by detecting and diagnosing any new fault condition. A permutation algorithm is applied in the neural network framework to extract the variable’s contribution to the classified fault condition. Also, Spearman’s rank correlation approach is used to investigate and justify the data correlation and causation. The proposed framework is tested using a continuous stirred tank heater and the benchmark Tennessee Eastman process.

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Section: Application and Benchmarkingmentioning

confidence: 99%

Autonomous Fault Diagnosis and Root Cause Analysis for the Processing System Using One-Class SVM and NN Permutation Algorithm

Arunthavanathan

Khan

Ahmed

et al. 2022

Ind. Eng. Chem. Res.

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show abstract

“…These methods eliminate the difficulties of using and developing detailed models and can enable appropriate process monitoring and fault detection tasks, especially for large chemical process systems. Therefore, data‐driven technologies include neural network methods, fuzzy theory, signed directed graph (SDG), support vector machines (SVM), and other machine learning methods have been widely used in process monitoring and fault diagnosis research. Hsu and Chen proposed a method for the fault diagnosis of the Tennessee Eastman (TE) process based on the SVM and verified the effectiveness of the method.…”

Section: Introductionmentioning

confidence: 99%

“…Hsu and Chen proposed a method for the fault diagnosis of the Tennessee Eastman (TE) process based on the SVM and verified the effectiveness of the method. Subsequently, Zhang et al used the genetic algorithm (GA) to optimize the penalty parameter c in the SVM to improve the fault diagnosis accuracy of the SVM. D'Angelo et al proposed a fault diagnosis method based on fuzzy set theory, which combines the ClonALG algorithm with the Kohonen neural network.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis in industrial chemical processes using optimal probabilistic neural network

Xie

Yang

et al. 2019

Can J Chem Eng

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For fault detection and diagnosis in large‐scale industrial systems, traditional methods have a low classification accuracy, which is an issue. This paper proposes a fault diagnosis method based on the modified cuckoo search algorithm (MCS) to optimize the probabilistic neural network (PNN). The random forest treebagger (RFtb) is used to reduce the data feature and the PNN is trained for fault diagnosis and classification. However, in order to address the problem that the parameters of PNN easily fall into the local optimal value, the MCS algorithm is introduced to globally optimize the hidden layer element smoothing factor (σ) in the PNN. The MCS algorithm uses a parameters update and a better optimization mechanism to achieve excellent global convergence and to effectively improve the fault diagnosis capability of the model. During the testing process using the Tennessee Eastman (TE) process dataset, the performance of the proposed model is assessed by comparing the accuracy and the F1‐score of different methods. Graphs are presented that depict fault classification and diagnostic results for the different models. The results show that the MCS algorithm has a better optimization ability than the traditional optimization algorithm and the proposed combination method can significantly improve the accuracy of the TE process fault diagnosis.

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“…In order to effectively separate faults, machine learning methods such as the support vector machine (SVM) [12,13], K-nearest neighbor (KNN) [14,15], Bayes [16,17], and decision tree (DT) [18,19] are used for fault diagnosis. Zhang used KPCA to reduce the data dimension and then classified fault data by using the SVM [20]. Random forest (RF) is composed of multiple decision trees.…”

Section: Introductionmentioning

confidence: 99%

A Multichannel LSTM‐CNN Method for Fault Diagnosis of Chemical Process

Shao

Bian

et al. 2019

Mathematical Problems in Engineering

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The identification and classification of faults in chemical processes can provide decision basis for equipment maintenance personnel to ensure the safe operation of the production process. In this paper, we combine long short-term memory neural network (LSTM) with convolutional neural network (CNN) and propose a new fault diagnosis method based on multichannel LSTM-CNN (MCLSTM-CNN). The primary methodology here includes three aspects. In the initial state, the fault data are input into the LSTM to obtain the output of the hidden layer, which stores the relevant temporal and spatial domain information. Due to the diversity of data features, convolutional kernels with different sizes are utilized to form multiple channels to extract the output characteristics of the hidden layer simultaneously. Finally, the fault data are classified by fully connected layers. The Tennessee Eastman (TE) chemical process is used for experimental analysis, and the MCLSTM-CNN model is compared with the LSTM-CNN, LSTM, CNN, RF and KPCA + SVM models. The experimental results show that the MCLSTM-CNN model has higher diagnostic accuracy, and the fault classification results are superior to other models.

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Research on Fault Diagnosis of Tennessee Eastman Process Based on KPCA and SVM

Cited by 8 publications

References 10 publications

Autonomous Fault Diagnosis and Root Cause Analysis for the Processing System Using One-Class SVM and NN Permutation Algorithm

Autonomous Fault Diagnosis and Root Cause Analysis for the Processing System Using One-Class SVM and NN Permutation Algorithm

Fault diagnosis in industrial chemical processes using optimal probabilistic neural network

A Multichannel LSTM‐CNN Method for Fault Diagnosis of Chemical Process

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