Two-Step Localized Kernel Principal Component Analysis Based Incipient Fault Diagnosis for Nonlinear Industrial Processes

Deng, Xiaogang; Cai, Peipei; Cao, Yanlong; Wang, Ping

doi:10.1021/acs.iecr.9b06826

Cited by 36 publications

(16 citation statements)

References 41 publications

(83 reference statements)

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“…For the EUELM, the kernel function is chosen as the Gaussian kernel [35][36][37]. The kernel parameter  and the output space dimension o n are respectively set up as 250 and 50 according to the grid search algorithm [47][48] by seeking the optimal fautl detection result of the training dataset.…”

Section: ) Comparative Methods and Parameter Settingmentioning

confidence: 99%

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An Enhanced Unsupervised Extreme Learning Machine Based Method for the Nonlinear Fault Detection

Shao

Kang

et al. 2021

IEEE Access

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Although the unsupervised extreme learning machine (UELM) based methods have been widely used to diagnosis the nonlinear process faults recently, the UELM algorithm is only designed to preserve the local adjacency similarity of the input dataset instead of mining the intra-class variations. Besides, the determination of the optimal UELM hidden nodes number is a tough issue. In order to deal with these two problems, a novel enhanced UELM (EUELM) based scheme is developed to effectively detect the nonlinear process faults in our work. In the proposed EUELM approach, the UELM algorithm is first improved by naturally incorporating the diversity analysis technique into the original UELM objective function to preserve both the intra-class variation and the local adjacency similarity of the input dataset. Then, to settle the difficult issue of selecting the optimal number of hidden nodes, kernel trick is further employed in the EUELM approach to mine the data strong nonlinearity. Based on the extracted diversity and local similarity low dimensional feature information, the k-nearest neighbor (KNN) principle is applied to derive a monitoring statistic for fault detection. At last, the experiments and comparisons on the monitoring effectiveness of the suggested EUELM based approach are made on a numerical nonlinear system and the benchmark Tennessee Eastman (TE) process. The obtained monitoring results illustrate that the significant improvements can be achieved by the proposed EUELM based fault detection approach compared with other popular and related approaches.

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Section: ) Comparative Methods and Parameter Settingmentioning

confidence: 99%

“…To figure out the issue of explicitly setting up the optimal modified UELM's hidden nodes number, kernel trick [35,36] is employed. By using the kernel function ( , ) ( ), ( )…”

Section: B Employ the Kernel Trick To The Modified Uelm Modelmentioning

confidence: 99%

An Enhanced Unsupervised Extreme Learning Machine Based Method for the Nonlinear Fault Detection

Shao

Kang

et al. 2021

IEEE Access

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“…When PCA performs a nonlinear data process, it is assumed that the data conform to ideal data distribution. Otherwise, PCA can only perform linear transformations [ 31 ]. In this article, the input data is processed into an image by the Wavelet Transform.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Multilocation and Multiscale Learning Framework with Skip Connection for Fault Diagnosis of Bearing under Complex Working Conditions

Ban

Wang

et al. 2021

Sensors

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Considering various fault states under severe working conditions, the comprehensive feature extraction from the raw vibration signal is still a challenge for the diagnosis task of rolling bearing. To deal with strong coupling and high nonlinearity of the vibration signal, this article proposes a novel multilocation and multikernel scale learning network based on deep convolution encoder (DCE) and bidirectional short-term memory network (BiLSTM). The former multifeature learning network of this article proposed combines the skip connection and the DCE network. The network can automatically extract and fuse global and local features from different network depth and time scales of the raw vibration signal. Then, the former network as the input of the latter network is fed into the feature protection layer for further mining sensitive and complementary features. Consequently, the proposed network scheme can perform well in generalization capability. The performance of the proposed method is verified on the two kinds of bearing datasets. The diagnostic results demonstrate that the proposed method can diagnose multiple fault types more accurately. Also, the method performs better in load and speed adaptation compared with other intelligent fault classification methods.

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“…However, as mentioned above, batch processes generally have dynamic characteristics in two directions: time‐wise dynamics and batch‐wise dynamics. [ 11,12 ] To be specific, the time‐wise dynamics are characterized by the inherently time‐varying dynamic behaviour during each batch run because of the slowly varying underlying driving forces. The batch‐wise dynamics are associated with different operating modes that result from dynamic variations and deviations among different batches.…”

Section: Introductionmentioning

confidence: 99%

Dynamic nonlinear batch process fault detection and identification based on two‐directional dynamic kernel slow feature analysis

et al. 2020

Self Cite

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The batch process generally covers high nonlinearity and two-directional dynamics: time-wise dynamics, which correspond to inherently time-varying dynamics resulting from the slowly varying underlying driving forces within each batch duration; and batch-wise dynamics, which are associated with different operating modes among different batches. However, most existing dynamic nonlinear monitoring methods cannot extract the slowly varying underlying driving forces of the nonlinear batch process and rarely tackle the batch-wise dynamic characteristics among batch runs. In order to address these issues, a new monitoring scheme based on two-directional dynamic kernel slow feature analysis (TDKSFA) is developed by combining kernel SFA with a global modelling strategy. In the TDKSFA method, kernel SFA is integrated with the ARMAX time series model to mine the nonlinear and timewise dynamic properties within a batch run due to its capability of extracting the slowly varying underlying driving forces. Furthermore, the global modelling strategy is presented to handle the batch-wise dynamics among batches by calculating the total average kernel matrix of all training batches. After the slow features are extracted, Hotelling's T 2 and SPE statistics are built to detect faults. To solve the issue of fault variable nonlinear identification, a novel nonlinear contribution plot inspired by the pseudo-sample variable projection trajectories in the TDKSFA model is further proposed to identify fault variables. Finally, the feasibility and effectiveness of the TDKSFA-based fault diagnosis strategy is demonstrated through a numerical system and the penicillin fermentation process. K E Y W O R D S batch process, fault diagnosis, slow feature analysis, two-directional dynamics 1 | INTRODUCTION Batch processes are becoming increasingly important in the pharmaceutical, semiconductor, polymer, and biochemical industries to manufacture high value-added products. However, it is difficult to establish a batch process monitoring model due to the high nonlinearity in time-wise and the time-varying dynamics both time-wise

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Two-Step Localized Kernel Principal Component Analysis Based Incipient Fault Diagnosis for Nonlinear Industrial Processes

Cited by 36 publications

References 41 publications

An Enhanced Unsupervised Extreme Learning Machine Based Method for the Nonlinear Fault Detection

An Enhanced Unsupervised Extreme Learning Machine Based Method for the Nonlinear Fault Detection

Multilocation and Multiscale Learning Framework with Skip Connection for Fault Diagnosis of Bearing under Complex Working Conditions

Dynamic nonlinear batch process fault detection and identification based on two‐directional dynamic kernel slow feature analysis

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