Improved nonlinear process monitoring based on ensemble KPCA with local structure analysis

Cui, Ping; Zhan, Chengjun; Yang, Yupu

doi:10.1016/j.cherd.2018.12.028

Cited by 29 publications

(32 citation statements)

References 43 publications

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“…Meanwhile, Li et al [80,267] also used multi-level hierarchical models involving both linear PCA and kernel PCA. More recently, the ensemble kernel PCA was fused with local structure analysis by Cui et al [273] for manifold learning (see Section 4.9).…”

Section: Advanced Methods: Ensembles and Deep Learningmentioning

confidence: 99%

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A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring

et al. 2019

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Kernel methods are a class of learning machines for the fast recognition of nonlinear patterns in any data set. In this paper, the applications of kernel methods for feature extraction in industrial process monitoring are systematically reviewed. First, we describe the reasons for using kernel methods and contextualize them among other machine learning tools. Second, by reviewing a total of 230 papers, this work has identified 12 major issues surrounding the use of kernel methods for nonlinear feature extraction. Each issue was discussed as to why they are important and how they were addressed through the years by many researchers. We also present a breakdown of the commonly used kernel functions, parameter selection routes, and case studies. Lastly, this review provides an outlook into the future of kernel-based process monitoring, which can hopefully instigate more advanced yet practical solutions in the process industries.

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Section: Advanced Methods: Ensembles and Deep Learningmentioning

confidence: 99%

“…Meanwhile, generalized LPP and discriminative LPP (and its kernel version) were proposed by Shao et al [110] and Rong et al [151], respectively. Other works that adopted variants of LPP can be found in [218,234,252,258,266,273,290]. The heat kernel (HK) is commonly used as a weighting function in LPP.…”

Section: Manifold Learning and Local Structure Analysismentioning

confidence: 99%

A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring

et al. 2019

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“…So, the marginal likelihood is not conditioned on the clusters and computes as It defines the Bayesian decision boundaries for the model. The class parameters optimize by maximizing the likelihood of the data as (7) max ( | 1: ) = max ∏ ( | , 1: ) =1 where is the number of vectors ≡ { 1 , 2 , … , }.…”

Section: Bayesian Clusteringmentioning

confidence: 99%

Comparison of Different Spike Sorting Subtechniques Based on Rat Brain Basolateral Amygdala Neuronal Activity

Hojjatinia

Lagoa

2019

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Developing electrophysiological recordings of brain neuronal activity and their analysis provide a basis for exploring the structure of brain function and nervous system investigation. The recorded signals are typically a combination of spikes and noise. High amounts of background noise and possibility of electric signaling recording from several neurons adjacent to the recording site have led scientists to develop neuronal signal processing tools such as spike sorting to facilitate brain data analysis. Spike sorting plays a pivotal role in understanding the electrophysiological activity of neuronal networks. This process prepares recorded data for interpretations of neurons interactions and understanding the overall structure of brain functions. Spike sorting consists of three steps: spike detection, feature extraction, and spike clustering. There are several methods to implement each of spike sorting steps. This paper provides a systematic comparison of various spike sorting sub-techniques applied to real extracellularly recorded data from a rat brain basolateral amygdala. An efficient sorted data resulted from careful choice of spike sorting sub-methods leads to better interpretation of the brain structures connectivity under different conditions, which is a very sensitive concept in diagnosis and treatment of neurological disorders. Here, spike detection is performed by appropriate choice of threshold level via three different approaches. Feature extraction is done through PCA and Kernel PCA methods, which Kernel PCA outperforms. We have applied four different algorithms for spike clustering including K-means, Fuzzy Cmeans, Bayesian and Fuzzy maximum likelihood estimation. As one requirement of most clustering algorithms, optimal number of clusters is achieved through validity indices for each method. Finally, the sorting results are evaluated using inter-spike interval histograms.

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“…With the increasing scale and complexity of modern industrial processes, timely fault diagnosis technology is gaining its importance because of the high demands for plant safety and process continuity. Due to the application of the advanced data acquisition and computer control systems, huge volumes of data are collected so that data-driven fault diagnosis methods have been one of the most popular process monitoring technologies in recent years [1][2][3]. Intrinsically, data-driven fault detection can be viewed as one anomaly detection task.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Chemical Process Fault Diagnosis Using Ensemble Deep Support Vector Data Description

Deng¹,

Zhang²

2020

Sensors

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As one classical anomaly detection technology, support vector data description (SVDD) has been successfully applied to nonlinear chemical process monitoring. However, the basic SVDD model cannot achieve a satisfactory fault detection performance in the complicated cases because of its intrinsic shallow learning structure. Motivated by the deep learning theory, one improved SVDD method, called ensemble deep SVDD (EDeSVDD), is proposed in order to monitor the process faults more effectively. In the proposed method, a deep support vector data description (DeSVDD) framework is firstly constructed by introducing the deep feature extraction procedure. Different to the traditional SVDD with only one feature extraction layer, DeSVDD is designed with multi-layer feature extraction structure and optimized by minimizing the data-enclosing hypersphere with the regularization of the deep network weights. Further considering the problem that DeSVDD monitoring performance is easily affected by the model structure and the initial weight parameters, an ensemble DeSVDD (EDeSVDD) is presented by applying the ensemble learning strategy based on Bayesian inference. A series of DeSVDD sub-models are generated at the parameter level and the structure level, respectively. These two levels of sub-models are integrated for a holistic monitoring model. To identify the cause variables for the detected faults, a fault isolation scheme is designed by applying the distance correlation coefficients to measure the nonlinear dependency between the original variables and the holistic monitoring index. The applications to the Tennessee Eastman process demonstrate that the proposed EDeSVDD model outperforms the traditional SVDD model and the DeSVDD model in terms of fault detection performance and can identify the fault cause variables effectively.

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Improved nonlinear process monitoring based on ensemble KPCA with local structure analysis

Cited by 29 publications

References 43 publications

A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring

A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring

Comparison of Different Spike Sorting Subtechniques Based on Rat Brain Basolateral Amygdala Neuronal Activity

Nonlinear Chemical Process Fault Diagnosis Using Ensemble Deep Support Vector Data Description

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