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Robust inferential control using kernel density methods

COMPUTERS & CHEMICAL ENGINEERING 2000年第2-7期24卷 835-840页

作者： Goulding, PR Lennox, B Chen, Q Sandoz, DJ Univ Manchester Sch Engn Manchester Lancs England SimSci Ltd Stockport England

The use of kernel density estimation (KDE) methods to address the issue of control under process uncertainty and unreliability is investigated. It is shown how the KDE-derived joint probability density function of plant operational data can be used to assist in this task. It is also shown how the estimated density function can be used to support robust inference of important plant variables in addition to the detection and isolation of faults. (C) 2000 Elsevier Science Ltd. All rights reserved.

关键词： fault detection and isolation multivariate statistical process control control systems chemical process control kernel density methods

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Distribution-Free Adaptive Step-Down Procedure for Fault Identification

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2016年第8期32卷 2701-2716页

作者： Turkoz, Mehmet Kim, Sangahn Jeong, Young-Seon Al-Khalifa, Khalifa N. Hamouda, Abdel Magid Rutgers State Univ Dept Ind & Syst Engn Piscataway NJ USA Chonnam Natl Univ Dept Ind Engn Gwangju 500757 South Korea Qatar Univ Dept Mech & Ind Engn Doha Qatar

Identifying the faulty variables of the out-of-control signal in high-dimensional process is an important problem for quality control areas. Even though there have been several procedures for fault variable identifications, most of the existing approaches assume the multivariate normal distribution of observations and are sensitive to the correlations between variables. Therefore, in this paper, we propose a new fault variable identification method that does not assume any specific distribution of observations. The proposed procedure based on one class classification method identifies the changed variables by identifying unchanged variables at each step using the information obtained from the previous steps. This strategy can reduce computational times when a few variables are changed in a high-dimensional process. In addition, the proposed procedure is robust to the correlations between variables, resulting in stable performance regardless of the number of changed variables. The experiment results with diverse dataset demonstrate superiority of the proposed distribution-free procedure. Copyright (C) 2016 John Wiley & Sons, Ltd.

关键词： distribution-free fault variable identification multivariate statistical process control support vector data description

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Development of Drowsy Driving Accident Prediction by Heart Rate Variability Analysis

Development of Drowsy Driving Accident Prediction by Heart R...

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Annual Summit and Conference of Asia-Pacific-Signal-and-Information-processing-Association (APSIPA)

作者： Abe, Erika Fujiwara, Koichi Hiraoka, Toshihiro Yamakawa, Toshitaka Kano, Manabu Kyoto Univ Dept Syst Sci Kyoto Japan Kumamoto Univ Prior Org Innovat & Excellence Kumamoto Japan Kumamoto Univ Dept Comp Sci & Elect Engn Kumamoto Japan

ISBN: (纸本)9786163618238

Drowsy driving accidents can be prevented if it can be predicted in advance. The present work aims to develop a new method for predicting a drowsy driving accident based on the fact that the autonomic nervous function affects heart rate variability (HRV), which is the fluctuation of the RR interval (RRI) obtained from an electrocardiogram (ECG). The proposed method uses HRV features derived through HRV analysis as input variables of multivariate statistical process control (MSPC), which is a well-known anomaly detection method in process control. Driving simulator experiments demonstrated that driver drowsiness was successfully predicted seven out of eight cases before drowsy driving accidents occur.

关键词： accident prevention electrocardiography medical signal detection neurophysiology statistical process control ECG HRV analysis MSPC RR interval anomaly detection method autonomic nervous function driver drowsiness drowsy driving accident prediction electrocardiogram heart rate variability analysis multivariate statistical process control Accidents Data models Feature extraction Heart rate variability Monitoring process control Rail to rail inputs

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Non-intrusive Load Monitoring Based on Regularized ResNet with multivariate control Chart 20th

Non-intrusive Load Monitoring Based on Regularized ResNet wi...

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20th International Conference on Computational Science and Its Applications (ICCSA)

作者： Oh, Cheolhwan Jeong, Jongpil Sungkyunkwan Univ Dept Smart Factory Convergence Suwon 16419 Gyeonggi Do South Korea

ISBN: (纸本)9783030588021;9783030588014

With the development of industry and the spread of the Smart Home, the need for power monitoring solution technologies for effective energy management systems is increasing. Of these, non-intrusive load monitoring (NILM), is an efficient way to solve the electricity consumption monitoring problem. NILM is a technique to measure the power consumption of individual devices by analyzing the power data collected through smart meters and commercial devices. In this paper, we propose a deep neural network (DNN)-based NILM technique that enables energy disaggregation and power consumption monitoring simultaneously. Energy disaggregation is performed by learning a deep residual network for performing multilabel regression. Real-time monitoring is performed using a multivariate control chart technique using latent variables extracted through weights of the trained model. The energy disaggregation and monitoring performance of the proposed method is verified using the public NILM Electricity Consumption and Occupancy (ECO) data set.

关键词： Non-intrusive load monitoring Deep neural network multivariate statistical process control Regularization

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Fault detection in the Tennessee Eastman benchmark process with nonlinear singular spectrum analysis

Fault detection in the Tennessee Eastman benchmark process w...

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20th World Congress of the International-Federation-of-Automatic-control (IFAC)

作者： Krishnannair, S. Aldrich, C. Univ Zululand ZA-3886 Kwa Dlangezwa South Africa Univ Stellenbosch Dept Proc Engn Private Bag XI ZA-7602 Matieland South Africa Curtin Univ Western Australia Sch Mines Dept Min Engn & Met Engn GPO Box U1987 Perth WA 6845 Australia

multivariate statistical process monitoring methods aim at detecting and identifying faults in the performance of processes over time in order to keep the process under control. Singular spectrum analysis (SSA) is a potential tool for multivariate process monitoring. It allows the decomposition of dynamic process variables or time series into additive components that can be monitored separately to identify hidden faults that may otherwise not be detectable. However, SSA is a linear method and can give misleading information when it is applied to dynamic processes with strong nonlinearity. Therefore, in this paper, nonlinear versions of SSA based on the use of auto-associative neural networks or auto encoders and dissimilarity matrices are considered. This is done based on the benchmark Tennessee Eastman process that is widely used in the evaluation of statistical process monitoring methods. (C) 2017, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Fault detection and diagnosis multivariate statistical process control Singular Value Decomposition Singular Spectrum Analysis

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An Overview of Fault Detection Techniques in Automated Monitoring Systems

An Overview of Fault Detection Techniques in Automated Monit...

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11th Scandinavian Conference on Artificial Intelligence (SCAI)

作者： Zhong, Shengtong Norwegian Univ Sci & Technol Dept Comp & Informat Sci NO-7491 Trondheim Norway

ISBN: (纸本)9781607507543;9781607507536

This paper gives an overview of different methods for automated fault detection. Emphasis will be put on the properties of model based techniques (which we will further divide into analytical model based and knowledge based), multivariate statistical process control and machine learning techniques. The machine learning techniques are not traditionally viewed as a standard method for fault detection, so they are especially highlighted in this paper. Each method is presented in detail, and we will also discuss alternative extensions for various applications. The paper is ended by proposing to use machine learning techniques as a robust and well-functioning method in general.

关键词： Fault detection model based techniques multivariate statistical process control machine learning methods

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Structural Damage Fault Detection Using Artificial Neural Network Profile Monitoring 7

Structural Damage Fault Detection Using Artificial Neural Ne...

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7th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO)

作者： Awad, Mahmoud AlHamaydeh, Mohammad Mohamed, Ahmed Fares Amer Univ Sharjah Dept Ind Engn Sharjah U Arab Emirates Amer Univ Sharjah Dept Civil Engn Sharjah U Arab Emirates

ISBN: (纸本)9781509054541

In today's world, structural development with reliability and integrity is an ever demanding process. Fault detection is the identification of normal healthy behavior of a system or process and recognition of any deviation from such normal behavior. Fault detection in structural systems provides important liability and financial advantages since it gives the decision-makers lead-time and flexibility to manage the health of the system. Structural systems are critical systems that require continuous monitoring of damage accumulation caused by earthquake loads that may cause catastrophic failures. We present in this research a data-driven methodology for fault detection of structural systems using multivariate statistical process control (MVSPC). The proposed method based on modeling overall structural damage using artificial neural networks (ANN) as a function of the earthquake load intensity. Hotelling T-2 U technique is then used to identify any shifts of the ANN model weights from their healthy states. The proposed method is tested and validated using simulation data fora four-story RC building with varying concrete strengths. The methodology presented in this paper is scalable and can be applied to a wide range of systems to assess their health via an inspection check to anticipate and potentially avoid failures.

关键词： Structural damage profile monitoring multivariate statistical process control

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Support Vector Data Description based multivariate Cumulative Sum control Chart

Support Vector Data Description based Multivariate Cumulativ...

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International Conference on Advanced Design and Manufacturing Engineering (ADME 2011)

作者： He, Shuguang Zhang, Chunyan Tianjin Univ Sch Management Tianjin 300072 Peoples R China

ISBN: (纸本)9783037852156

A SVDD (Support Vector Data Description) based MCUSUM (multivariate Cumulative Sum) chart is proposed and referred as S-MCUSUM chart, which has an advantage of distribution free. Numerical experiments on the performance of the S-MCUSUM chart is compared to the COT (Cumulative of T) chart. The results show that the COT chart is somewhat better than the S-MCUSUM chart for multivariate normally distributed data. However, the S-MCUSUM chart is much better than the COT chart for banana-shaped distributed data which is a typical non-normal distribution.

关键词： Support vector data description Cumulative sum chart multivariate statistical process control One class classification

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Advances in Data-driven Monitoring Methods for Complex process

Advances in Data-driven Monitoring Methods for Complex Proce...

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3rd International Conference on Applied Mechanics, Materials and Manufacturing (ICAMMM 2013)

作者： Chen, Ru Qing Jiaxing Univ Coll Mech & Elect Engn Jiaxing 314001 Zhejiang Peoples R China

ISBN: (纸本)9783037858882

In modern industrial processes, effective performance monitoring and quality prediction are the key to ensure plant safety and enhance product quality. The research significance and background of process monitoring and fault diagnosis technologies are described and the current advances in data-based process monitoring methods are summed up in this paper. Then the multivariate statistical process control (MSPC) methods for process with single constraint, especially for single non-Gaussian process or nonlinear process are elaborated. As real industrial process data often show strong non-Gaussian and dynamic behaviors, study on monitoring technologies for dynamic non-Gaussian process is of great importance. Finally, some challenges such as non-Gaussian and dynamic process, fault detection and diagnosis as well as new MSPC methods are indicated.

关键词： Data-driven technique process monitoring and fault diagnosis multivariate statistical process control Dynamic and non-Gaussian process

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process Fault Diagnosis Method Based on MSPC and LiNGAM and its Application to Tennessee Eastman process 14th

Process Fault Diagnosis Method Based on MSPC and LiNGAM and ...

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14th IFAC Workshop on Intelligent Manufacturing Systems (IMS)

作者： Uchida, Yoshiaki Fujiwara, Koichi Saito, Tatsuki Osaka, Taketsugu Nagoya Univ Dept Mat Proc Engn Nagoya Aichi 4648601 Japan Kobe Steel Kobe Corp Res Labs Kobe Hyogo 6512271 Japan

This paper proposes a new fault diagnosis method that combines multivariate statistical process control (MSPC) and a linear non-gaussian acyclic model (LiNGAM), referred to as MSPC-LiNGAM. MSPC is a widely adopted process monitoring method based on principal component analysis (PCA). In MSPC, T-2 and Q statistics are used as monitoring indexes for fault detection. Contribution plots based on T-2 and Q statistics have been proposed for fault diagnosis. However, contribution plots do not always appropriately diagnose causes of faults. In this study, a new fault diagnosis method based on MSPC and a Linear Non-Gaussian Acyclic Model (LiNGAM) is proposed. In the proposed method, referred to as MSPC-LiNGAM, the causality among the T-2 or Q statistic in addition to process variables is calculated by LiNGAM without prior knowledge of processes, and process variables that have the strength of causality to the T-2 or Q statistic are identified as candidates of the causes of the fault. The proposed MSPC-LiNGAM was applied to a simulation data of the Tennessee Eastman (TE) process. The result showed that the proposed method appropriately diagnosed faults even when the conventional contribution plots did not correctly identify causes of faults.

关键词： Fault detection diagnosis multivariate statistical process control Linear non-Gaussian acyclic model Causal inference Contribution plot

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