A novel multi‐mode data processing method and its application in industrial process monitoring

Wang

2019

Self Cite

Recently, local modelling methods have drawn increasing attention for fault detection in industrial processes. However, current local modelling adopts the basic multivariate statistical process monitoring method without deep mining the internal statistical information of the process data. Locality preserving projection (LPP) is a typical local fault detection method. However, industrial processes typically have complex multimodal characteristics. In these situations, the traditional LPP method performs poorly due to its assumption that the process data are Gaussian and unimodal. To improve fault detection performance of LPP in multimodal industrial processes and mine the internal system information, this paper proposes a new fault detection method based on improved local entropy LPPs (ILELPPs). ILELPP first eliminates the multimodal and non‐Gaussian characteristics of the original data using the improved local entropy method. Then, LPP is applied to the preprocessed data, neglecting the influences of multimodal and non‐Gaussian characteristics. A multimodal numerical example and an industrial application in a semiconductor manufacturing process are used to verify the effectiveness of the proposed method. The simulation results demonstrate that the proposed method has better fault detection performance than the LPP, global entropy LPP, and local entropy LPP methods.

Section: Introductionmentioning

confidence: 99%

Fault detection based on improved local entropy locality preserving projections in multimodal processes

Wang

2019

Self Cite

“…With the gradually increased demand of product quality and production safety in modern industries, multivariate statistical process control based on data driven is widely applied . It uses the statistical regularity of process fluctuations to analyze and evaluate production processes …”

Section: Introductionmentioning

confidence: 99%

Fault detection method based on principal component difference associated with DPCA

Cheng

2018

Self Cite

Aiming at the fault detection in a dynamic process with nonlinear or multimodal features, a new fault detection strategy based on principal component difference associated with dynamic PCA (Diff-DPCA) is proposed in this paper.First, augment the training data set using the time lag, and then obtain an augmented training data set. Second, find the K-nearest neighbor set of each sample in the augmented training set and calculate the mean of the K nearest neighbor set. Third, calculate the loading and score matrices of the augmented training data set using PCA, and then calculate the scores of the proposed mean above using the loading matrix. Next, calculate the difference between the scores of a sample and its corresponding mean. Finally, 2 new statistics are built in difference subspace and residual subspace respectively. Principal component difference associated with dynamic PCA, which inherit the ability of DPCA monitoring a dynamic process, can improve the fault detection rate in the process with multimodal or nonlinear characteristics; meanwhile, the new statistics present low autocorrelation levels. The proposed method in this paper and other traditional methods are applied to detect faults in 2 cases and the Tennessee Eastman process, such as PCA, DPCA, and FD-KNN.The experimental results indicate that the proposed method outperforms the conventional PCA, DPCA, and FD-KNN.

“…Owing to the complexity and the multimodal characteristics of industrial processes, the collected data usually follow different distributions. Wang et al proposed a novel multimodal data processing method called weighted k ‐neighborhood standardization to address the multimodal data problem. Most multivariate statistical process monitoring methods, such as PCA, have a fundamental assumption that the operating data follow unimodal distribution.…”

Section: Introductionmentioning

confidence: 99%

kNN based on probability density for fault detection in multimodal processes

Wang

2018

Self Cite

Recently, k‐nearest neighbor rules (kNN) have drawn increasing attention for fault detection of multimodal industrial processes. However, the traditional kNN method performs poorly for weak faults in a dense mode when the dispersion degree of each mode is quite different. The reason is that the kNN statistics of weak faults are usually submerged by those of normal data in a mode with a high dispersion degree. To improve the fault detection performance of kNN in this case, this paper proposes a new multimodal fault detection method of kNN based on probability density. The proposed method does not need to consider the different degrees of dispersion between modes and avoids the problem of weak faults in a mode that has a low dispersion degree that is submerged by normal data in a mode with a high dispersion degree. A multimodal numerical example with different dispersion degrees of each mode and an industrial application in a semiconductor manufacturing process are used to verify the effectiveness of the proposed method. The simulation results demonstrate that the proposed method shows better fault detection performance than the kNN, local outlier factor, and weighted difference principal component analysis methods.