Mixture control chart patterns recognition using independent component analysis and support vector machine

Lu, Chi-Jie; Shao, Yuehjen E.; Li, Po-Hsun

doi:10.1016/j.neucom.2010.06.036

Cited by 66 publications

(38 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, because different mixture disturbances are typically associated with specific root causes that adversely affect the process, the effective identification of MCCPs for an SPC-EPC process is crucial [7]. Therefore, the issue of identification of MCCPs for industrial processes is an important research topic.…”

Section: Complexitymentioning

confidence: 99%

Applying Two-Stage Neural Network Based Classifiers to the Identification of Mixture Control Chart Patterns for an SPC-EPC Process

Shao

Chang

2017

Complexity

Self Cite

View full text Add to dashboard Cite

The effective controlling and monitoring of an industrial process through the integration of statistical process control (SPC) and engineering process control (EPC) has been widely addressed in recent years. However, because the mixture types of disturbances are often embedded in underlying processes, mixture control chart patterns (MCCPs) are very difficult for an SPC-EPC process to identify. This can result in problems when attempting to determine the underlying root causes of process faults. Additionally, a large number of categories of disturbances may be present in a process, but typical single-stage classifiers have difficulty in identifying large numbers of categories of disturbances in an SPC-EPC process. Therefore, we propose a two-stage neural network (NN) based scheme to enhance the accurate identification rate (AIR) for MCCPs by performing dimension reduction on disturbance categories. The two-stage scheme includes a combination of a NN, support vector machine (SVM), and multivariate adaptive regression splines (MARS). Experimental results reveal that the proposed scheme achieves a satisfactory AIR for identifying MCCPs in an SPC-EPC system.

show abstract

Section: Complexitymentioning

confidence: 99%

Applying Two-Stage Neural Network Based Classifiers to the Identification of Mixture Control Chart Patterns for an SPC-EPC Process

Shao

Chang

2017

Complexity

Self Cite

View full text Add to dashboard Cite

show abstract

“…This leaves the challenging task to identify multiple faults effectively. There are a few studies on multiple faults patterns recognition [6][7][8]. Chen et al integrate extremepoint symmetric mode decomposition with extreme learning machine to identify typical multiple patterns recognition [6].…”

Section: Introductionmentioning

confidence: 99%

“…Lu et al propose a hybrid system that uses independent component analysis (ICA) and support vector machine (SVM) for recognizing mixture patterns. That method initially applies the ICA to get the independent components (ICs), and then the ICs are used as the inputs for the SVM classifier [8].…”

Section: Introductionmentioning

confidence: 99%

Multiple‐Fault Diagnosis Method Based on Multiscale Feature Extraction and MSVM_PPA

Zhang

Cai

Cheng

2018

Shock and Vibration

View full text Add to dashboard Cite

Identification of rolling bearing fault patterns, especially for the compound faults, has attracted notable attention and is still a challenge in fault diagnosis. In this paper, a novel method called multiscale feature extraction (MFE) and multiclass support vector machine (MSVM) with particle parameter adaptive (PPA) is proposed. MFE is used to preprocess the process signals, which decomposes the data into intrinsic mode function by empirical mode decomposition method, and instantaneous frequency of decomposed components was obtained by Hilbert transformation. Then, statistical features and principal component analysis are utilized to extract significant information from the features, to get effective data from multiple faults. MSVM method with PPA parameters optimization will classify the fault patterns. The results of a case study of the rolling bearings faults data from Case Western Reserve University show that (1) the proposed intelligent method (MFE PPA MSVM) improves the classification recognition rate; (2) the accuracy will decline when the number of fault patterns increases; (3) prediction accuracy can be the best when the training set size is increased to 70% of the total sample set. It verifies the method is feasible and efficient for fault diagnosis.

show abstract

“…Lu [11] proposed an ICA-based monitoring scheme to identify shift pattern in an autocorrelated process. Lu et al [12] combined ICA and SVM for diagnosing mixture CCPs which are mixed by the normal and other abnormal basic patterns. However, their work did not consider the issue of recognition of concurrent CCPs.…”

Section: Introductionmentioning

confidence: 99%

“…The SVM has attracted the interest of researchers and has been applied many applications such as diseases classification and process monitoring [14,15,16,17,18]. However, relatively few studies have been conducted using SVM for CCP recognition [12].…”

Section: Introductionmentioning

confidence: 99%

Recognition of Concurrent Control Chart Patterns by Integrating ICA and SVM

Lu¹,

Shao²,

Li³

2014

Appl. Math. Inf. Sci.

Self Cite

View full text Add to dashboard Cite

Control chart patterns (CCPs) recognition is an important issue in statistical process control, since abnormal CCPs exhibited in control charts can be associated with certain assignable causes and then help to quickly eliminate assignable causes of process variation. Most of the existing studies assume that the observed process data needs to be recognized are basic types of abnormal CCPs. However, in practical situations, the observed process data may be concurrent CCPs which are mixed together by two basic CCPs. In this study, an integrated scheme using independent component analysis (ICA) and support vector machine (SVM) is proposed for recognizing concurrent CCPs. The proposed ICA-SVM scheme initially uses ICA with concurrent patterns for generating independent components (ICs). The hidden basic patterns of the concurrent patterns can be discovered in these ICs. The ICs are then used as input variables for the SVM for building a CCP recognition model. Experimental results reveal that the proposed ICA-SVM scheme can produce accurate and stable recognition results. It outperforms the three comparison models and is able to effectively recognize concurrent control chart patterns.

show abstract

Mixture control chart patterns recognition using independent component analysis and support vector machine

Cited by 66 publications

References 35 publications

Applying Two-Stage Neural Network Based Classifiers to the Identification of Mixture Control Chart Patterns for an SPC-EPC Process

Applying Two-Stage Neural Network Based Classifiers to the Identification of Mixture Control Chart Patterns for an SPC-EPC Process

Multiple‐Fault Diagnosis Method Based on Multiscale Feature Extraction and MSVM_PPA

Recognition of Concurrent Control Chart Patterns by Integrating ICA and SVM

Contact Info

Product

Resources

About