Online process monitoring scheme for fault detection based on Independent Component Analysis (ICA) and Local Outlier Factor (LOF)

Lee, Jaeshin; Kang, Byung Hee; Shin, KwangSup; Kang, Suk‐Ho

doi:10.1109/iccie.2010.5668311

Cited by 7 publications

(6 citation statements)

References 24 publications

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“…Reference J. Lee et al 16 conducted a study that resulted in the proposal of an online monitoring method for fault detection with the aim of improving the performance of a factory. This is based on the multivariate statistical control process, which allows to calculate the value of the statistical monitoring, through observation at certain time intervals.…”

Section: Performance Monitoringmentioning

confidence: 99%

Gamification in project management

Marcão¹,

Pestana²,

Sousa³

2017

Proceedings of the Second International Conference on Economic and Business Management (FEBM 2017)

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The area of monitoring and project management presents a collection of methodologies. Some are more sequential and others more iterative, each one with benefits in different aspects. In this way, it is intended to gather a set of good practices of project management, identifying the most beneficial characteristics of different methodologies. Since one of the dimensions of project management is the management of human resources, it is urgent to value the motivation of employees. Since in many of the developed studies, the performance of the employee appears as a variable dependent on motivation, it becomes urgent to identify concepts that allow addressing it, such as the concept of gamification. By monitoring the evolution of the performance of each employee, we were able to evaluate the success of the model. Although there are several methodologies in the market, none of them results from a symbiosis between traditional and iterative methodologies, and there is no reference to any model that can dynamically address the employee's motivation. This document focuses on the literature review of the theme of monitoring and project management, whereas the focus is on the concept of gamification as the engine of motivation and the monitoring of the performance of employees, as members of a project team.

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Section: Performance Monitoringmentioning

confidence: 99%

Gamification in project management

Marcão¹,

Pestana²,

Sousa³

2017

Proceedings of the Second International Conference on Economic and Business Management (FEBM 2017)

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“…However, because the k NN method detection index is based on the distance between samples, it can easily be influenced by outliers. To solve this problem, a local outlier factor (LOF) was proposed . Then, Ma et al used the local neighbourhood standardization strategy in the LOF method and proposed the neighbourhood standardized LOF method to improve the performance of LOF.…”

Section: Introductionmentioning

confidence: 99%

“…To solve this problem, a local outlier factor (LOF) was proposed. 20 Then, Ma et al 21 used the local neighbourhood standardization strategy in the LOF method and proposed the neighbourhood standardized LOF method to improve the performance of LOF. Owing to the complexity and multimodal characteristics of industrial processes, the distributions of collected data are usually different.…”

Section: Introductionmentioning

confidence: 99%

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

Guo

Wang

2019

Journal of Chemometrics

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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.

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“…However, because the detection index of the kNN method is related to the distance, it can easily be affected by outliers. In recent years, a new fault detection method, local outlier factor (LOF), was proposed . To improve the fault detection performance of the LOF method, Ma et al used the local neighborhood standardization strategy in the LOF method and proposed the neighborhood standardized LOF method.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, a new fault detection method, local outlier factor (LOF), was proposed. 18 To improve the fault detection performance of the LOF method, Ma et al 19 used the local neighborhood standardization strategy in the LOF method and proposed the neighborhood standardized LOF method. However, this method regards outliers as fault samples and has some limitations.…”

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confidence: 99%

kNN based on probability density for fault detection in multimodal processes

Guo

Wang

2018

Journal of Chemometrics

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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.

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Online process monitoring scheme for fault detection based on Independent Component Analysis (ICA) and Local Outlier Factor (LOF)

Cited by 7 publications

References 24 publications

Gamification in project management

Gamification in project management

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

kNN based on probability density for fault detection in multimodal processes

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