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

Zhang, Ning; Xu, Yuan; Zhu, Qunxiong; He, Yan‐Lin

doi:10.1109/tii.2022.3182774

Cited by 25 publications

(3 citation statements)

References 26 publications

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“…Generally, the selected 10 fault data have large overlap and are difficult to classify. 14 , 35 The 10 fault types and descriptions are shown in Table 1 . The fault type in case 1 is affected by feed and flow, and case 2 is affected by temperature.…”

Section: Experimental Verificationmentioning

confidence: 99%

“…Referring to the literature, we select 10 representative faults and divide them into two cases with the aim of verifying the generalization ability and robustness of the proposed method. Generally, the selected 10 fault data have large overlap and are difficult to classify. , The 10 fault types and descriptions are shown in Table . The fault type in case 1 is affected by feed and flow, and case 2 is affected by temperature.…”

Section: Experimental Verificationmentioning

confidence: 99%

“…The improved LPP based on the heat-kernel and cosine weights can effectively extract the internal structure feature of data, so high fault diagnosis accuracy can be achieved in two chemical processes. Zhang et al 14 constructed a new farthest–nearest distance neighborhood and locality projections method and used it to reduce the dimension of high-dimensional process data to extract discriminant features. NB was adopted as a classifier for process fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

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Fault Diagnosis of the Dynamic Chemical Process Based on the Optimized CNN-LSTM Network

et al. 2022

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Deep learning provides new ideas for chemical process fault diagnosis, reducing potential risks and ensuring safe process operation in recent years. To address the problem that existing methods have difficulty extracting the dynamic fault features of a chemical process, a fusion model (CS-IMLSTM) based on a convolutional neural network (CNN), squeeze-and-excitation (SE) attention mechanism, and improved long short-term memory network (IMLSTM) is developed for chemical process fault diagnosis in this paper. First, an extended sliding window is utilized to transform data into augmented dynamic data to enhance the dynamic features. Second, the SE is utilized to optimize the key fault features of augmented dynamic data extracted by CNN. Then, IMLSTM is used to balance fault information and further mine the dynamic features of time series data. Finally, the feasibility of the proposed method is verified in the Tennessee-Eastman process (TEP). The average accuracies of this method in two subdata sets of TEP are 98.29% and 97.74%, respectively. Compared with the traditional CNN-LSTM model, the proposed method improves the average accuracies by 5.18% and 2.10%, respectively. Experimental results confirm that the method developed in this paper is suitable for chemical process fault diagnosis.

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Section: Experimental Verificationmentioning

confidence: 99%