Fault Identification Using Fast k-Nearest Neighbor Reconstruction

Abstract

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.

Temporal-Spatial Neighborhood Enhanced Sparse Autoencoder for Nonlinear Dynamic Process Monitoring

“…Considering the dynamic characteristics of the process, the current sample can only use historical samples and samples of future moments cannot be obtained. Therefore, the time window L is defined as a time delay window, L = 2k is generally selected, and k is the number of spatial neighbors selected [23]. The TS-SSAE algorithm is composed of TS-SSAE-1 and TS-SSAE-2, and their neighborhood information is different.…”

A Novel Fault Detection Scheme Based on Mutual k-Nearest Neighbor Method: Application on the Industrial Processes with Outliers

“…For example, the detection threshold of Hotelling-T 2 and squared prediction error (SPE) are calculated based on the premise that process variables satisfy a normal or Gaussian distribution. Due to the nonlinearity, non-Gaussianity, and multimodality in industrial processes, it is not easy to meet this assumption in practice [7][8][9][10][11]. Therefore, the traditional PCA-based process monitoring method has poor monitoring performance when facing the above problems [12][13][14][15][16].…”

Fault Detection and Isolation of Non-Gaussian and Nonlinear Processes Based on Statistics Pattern Analysis and the k-Nearest Neighbor Method

“…The fault detection method using the kNN rule (FD-kNN) only uses the distance between neighbors to perform fault detection; there is no restriction on the data distribution. 27 , 28 The qualitative and quantitative analysis for the non-Gaussianity and nonlinearity of statistics are also presented. Meanwhile, the effect of window width on the non-Gaussianity of statistics is investigated.…”

“…In this paper, a new process monitoring method, which combines the superiority of SPA in extracting HOS information with kNN in processing the non-Gaussian and nonlinearity of data samples, is proposed to deal with the defects of those methods above. The fault detection method using the kNN rule (FD-kNN) only uses the distance between neighbors to perform fault detection; there is no restriction on the data distribution. , The qualitative and quantitative analysis for the non-Gaussianity and nonlinearity of statistics are also presented. Meanwhile, the effect of window width on the non-Gaussianity of statistics is investigated.…”