Fault diagnosis using kNN reconstruction on MRI variables

Wang, Guozhu; Liu, Jianchang; Li, Yuan

doi:10.1002/cem.2719

Cited by 26 publications

(15 citation statements)

References 47 publications

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“…Although it can reduce the dimensionality of high-dimensional data and perform well in fault detection, the separation effect on faults is poor. 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].…”

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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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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“…Wang et al [28] developed an algorithm for fault variables identification based on kNN reconstruction (FVI-kNN). In FVI-kNN, each variable is sequentially estimated by the rest variables using kNN regression; then m (i.e., the number of the variables) reconstructed samples can be obtained by replacing the original variables with their estimation, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…Without prior fault information, however, the identification procedure of either FVI-kNN [28] or IFVI-kNN [29] is extremely time-consuming. For an industrial process with m monitoring variables, when any fault affects q of the m variables, the times of sample reconstruction required by these two methods [28,29] is m! (m−q)!×q!…”

Section: Introductionmentioning

confidence: 99%

Fault Identification Using Fast k-Nearest Neighbor Reconstruction

Zhou¹,

Li²,

Cai³

2019

Processes

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Data with characteristics like nonlinear and non-Gaussian are common in industrial processes. As a non-parametric method, k-nearest neighbor (kNN) rule has shown its superiority in handling the data set with these complex characteristics. Once a fault is detected, to further identify the faulty variables is useful for finding the root cause and important for the process recovery. Without prior fault information, due to the increasing number of process variables, the existing kNN reconstruction-based identification methods need to exhaust all the combinations of variables, which is extremely time-consuming. Our previous work finds that the variable contribution by kNN (VCkNN), which defined in original variable space, can significantly reduce the ratio of false diagnosis. This reliable ranking of the variable contribution can be used to guide the variable selection in the identification procedure. In this paper, we propose a fast kNN reconstruction method by virtue of the ranking of VCkNN for multiple faulty variables identification. The proposed method significantly reduces the computation complexity of identification procedure while improves the missing reconstruction ratio. Experiments on a numerical case and Tennessee Eastman problem are used to demonstrate the performance of the proposed method.

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“…As modern industrial systems become increasingly large and complex, timely process monitoring and fault diagnosis technologies serve more and more important roles to ensure process safety and improve product quality . As computer control systems are extensively applied in modern industries, abundant data are gathered and stored in the process database.…”

Section: Introductionmentioning

confidence: 99%

Multi‐block principal component analysis based on variable weight information and its application to multivariate process monitoring

Wang

Deng

2017

Can J Chem Eng

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The traditional principal component analysis (PCA)‐based process monitoring method builds a global statistical model and omits the mining of the local variable behaviours, which may degrade the fault detection performance. Considering this problem, this paper proposes a variable weight information‐based multi‐block PCA (VWI‐MBPCA) method. Firstly, a sequence hierarchical clustering algorithm is proposed to divide the full PCA component space into several sub‐blocks, where the components sharing similar variable weight information are gathered together and then the sub‐block T2 statistic is constructed for monitoring sub‐block components. Further, the variables with small weight information on each component sub‐block are extracted to build an additional sub‐PCA model, where the trueTˆ2 statistic is developed to compensate the sub‐block T2 statistic. In order to integrate the monitoring results of each sub‐block, Bayesian inference is applied to construct an overall T2 statistic. To identify the faulty variables, a multi‐block PCA contribution plot is designed by choosing some specific blocks to highlight fault information. Finally, simulations on a numerical example and the benchmark Tennessee Eastman (TE) process are used to demonstrate the strengths of the proposed method.

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Fault diagnosis using kNN reconstruction on MRI variables

Cited by 26 publications

References 47 publications

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

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

Fault Identification Using Fast k-Nearest Neighbor Reconstruction

Multi‐block principal component analysis based on variable weight information and its application to multivariate process monitoring

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