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Reduction of Ventricular Tachycardia False Alarms Using multivariate statistical process control and Frequency Analysis 25

Reduction of Ventricular Tachycardia False Alarms Using Mult...

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25th Iranian Conference on Biomedical Engineering / 3rd International Iranian Conference on Biomedical Engineering (ICBME)

作者： Safari, Amirhossein Mohebbi, Maryam KN Toosi Univ Technol Biomed Engn Tehran Iran

ISBN: (纸本)9781538679524

In this paper, we present a false alarm reduction algorithm for Ventricular Tachycardia (VT) arrhythmias in intensive care unit (ICU) using multivariate statistical process control (MSPC) and frequency analysis of electrocardiogram (ECG) signal. First, we decompose the ECG signal into three different frequency bands. The ECG beats are detected, and VT beats are labeled. In the next step, several features consist of time domain features, bispectrum features, and Poincare plot features are extracted from ECG Signal The extracted feature vector of each ECG beat is monitored using MSPC for detecting anomalies. The performance of the proposed method is evaluated using the Ventricular Tachycardia cases of 2015 Physionet challenge database. This dataset consists of 2 ECG channel, arterial blood pressure (ABP) and/or photoplethysmograph (PPG) signal, and an alarm annotation for each record. The obtained sensitivity and specificity were 86.5%, and 80.7% respectively. We have also investigated the advantage of using ABP signal in improving the results of false alarm reduction. For this purpose, some biological features are extracted from ABP and used as an extra feature vector. Results show that using ABP signal can improve the performance of the algorithm.

关键词： False alarm reduction multivariate statistical process control Ventricular tachycardia arrhythmias Multiple-signal processing Frequency analysis

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Drowsiness Detection Using multivariate statistical process control 22nd

Drowsiness Detection Using Multivariate Statistical Process ...

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22nd International Conference on Computational Science and its Applications (ICCSA)

作者： Antunes, Ana Rita Braga, Ana Cristina Goncalves, Joaquim Polytech Inst Cavado & Ave 2Ai P-4750810 Barcelos Portugal Univ Minho ALGORITMI Ctr P-4710057 Braga Portugal

ISBN: (纸本)9783031105364;9783031105357

Drowsiness at the wheel has been studied for different countries since it is important for road safety and its prevention. Since it is considered a public health problem, solutions must be found to avoid worse scenarios and to identify a low-cost system. Therefore, this work aims to detect the drowsy state, without labeling it manually, considering the heart rate variability. To make this possible, driving simulations were performed, using a wearable device. In terms of methodology, multivariate statistical process control, considering principal component analysis, was implemented, and compared with a similar study. Three principal components were computed taking into consideration time, frequency, and non-linear domain, every two minutes. Thereafter, Hotelling T-2 and squared prediction error statistics were estimated. These statistics were estimated considering each principal component, individually. Thereby, the results achieved seemed to be promising to identify drowsiness peaks. However, the study developed has limitations, like the identification of points out-of-control occurred due to signal noise and it does not identify all the drowsiness peaks. Conversely, it was not used information from the participants' awake states as a reference. Therewith, new simulations must be done, and new information must be added to avoid noise and to detect more drowsiness peaks.

关键词： Drowsy Driving Simulation Heart rate variability multivariate statistical process control Principal component analysis

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Monitoring of a Thermoelectric Power Plant based on multivariate statistical process control 10

Monitoring of a Thermoelectric Power Plant based on Multivar...

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IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)

作者： Fonseca, Joyce M. F. Sousa, Bruno M. Aguiar, Webber E. Braga, Anisio R. Lemos, Andre P. Michel, Hugo C. C. Braga, Carmela M. P. Univ Fed Minas Gerais PPGEE Belo Horizonte MG Brazil GPEMG CEMIG Belo Horizonte MG Brazil Univ Fed Minas Gerais Belo Horizonte MG Brazil

ISBN: (纸本)9781509025831

Thermoelectric power plants have critical units, such as the boiler and the turbine-generator, which are complex multivariate systems. These units exhibit non-stationary behavior and multiple operational modes that imply constant changes of set points of key performance variables. A methodology based on MSPC (multivariate statistical process control) techniques and PCA (Principal Component Analysis) is presented with an adaptive mean estimator that deals with frequent changes of set points, both for design and just in time monitoring. The proposed methodology' is implemented in a thermoelectric power plant using a commercial PIMS (process Information Management System) software suite. Experimental results illustrate and validate the proposition, its just-in-time implementation and usage.

关键词： multivariate statistical process control principal component analysis Hotelling's T-2 control chart thermoelectric power plant boiler turbine-generator unit PIMS

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Fault Diagnosis Method of Feeding Mechanism in Printing Machine Based on multivariate statistical process control 4th

Fault Diagnosis Method of Feeding Mechanism in Printing Mach...

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4th China Academic Conference on Printing and Packaging (CACPP)

作者： Xu, Zhuofei Zhang, Haiyan Hou, Heping Ren, Linghui Sun, Yankun Xu, Qianqian Xian Univ Technol Sch Printing Packaging & Digital Media Xian Shanxi Peoples R China Xian Aerosp Huayang Mech & Elect Equipment CO LTD Xian Shanxi Peoples R China

ISBN: (纸本)9789811000720;9789811000706

Device feeding is considered as the primary condition for printing production industry, which also determines the accuracy and quality of printing production. Having observed the difficulty of detecting the feeding fault in printing machine, the present study intends to propose an effective method on the basis of multivariate statistical process control. Firstly, the printing deviations in both circumferential and axial directions are calculated. Then, a principle model in normal working condition is build. Secondly, the working state of feeding unit is evaluated according to PSE control limits and data changing. Finally, the fault diagnosis and explanation are undertaken through the analysis of principle loading and the calculation of scoring. This study aims to establish a theory of fault diagnosis for press feeding system, which will offer a new theoretical instruction for the research in same field.

关键词： Feed mechanism in printing machine Principal component analysis multivariate statistical process control Fault diagnosis

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Multistate multivariate statistical process control

Multistate multivariate statistical process control

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Quality and Productivity Research Conference (QPRC)

作者： Odom, Gabriel J. Newhart, Kathryn B. Cath, Tzahi Y. Hering, Amanda S. Baylor Univ Dept Stat Sci Waco TX 76798 USA Univ Miami Sylvester Comprehens Canc Ctr Miami FL USA Colorado Sch Mines Dept Civil & Environm Engn Golden CO 80401 USA

For high-dimensional, autocorrelated, nonlinear, and nonstationary data, adaptive-dynamic principal component analysis (AD-PCA) has been shown to do as well or better than nonlinear dimension reduction methods in flagging outliers. In some engineered systems, designed features can create a known multistate scheme among multiple autocorrelated, nonlinear, and nonstationary processes, and incorporating this additional known information into AD-PCA can further improve it. In simulations with one of three types of faults introduced, we compare accounting for the states versus ignoring them. We find that multistate AD-PCA reduces the proportion of false alarms and reduces the average time to fault detection. Conversely, we also investigate the impact of assuming multiple states when only one exists, and find that as long as the number of observations is sufficient, this misspecification is not detrimental. We then apply multistate AD-PCA to real-world data collected from a decentralized wastewater treatment system during in control and out of control conditions. Multistate AD-PCA flags a strong system fault earlier and more consistently than its single-state competitor. Furthermore, accounting for the physical switching system does not increase the number of false alarms when the process is in control and may ultimately assist with fault attribution.

关键词： multistate adaptive-dynamic principle component analysis multivariate statistical process control nonlinear time-varying process

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An integrated approach for multivariate statistical process control using Mahalanobis-Taguchi System and Andrews function

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INTERNATIONAL JOURNAL OF QUALITY & RELIABILITY MANAGEMENT 2017年第8期34卷 1186-1208页

作者： Sikder, Sagar Panja, Subhash Chandra Mukherjee, Indrajit Indian Stat Inst SQC & OR Unit Mumbai Maharashtra India Jadavpur Univ Dept Mech Engn Kolkata India Indian Inst Technol Shailesh J Mehta Sch Management Mumbai Maharashtra India

Purpose - The purpose of this paper is to develop a new easy-to-implement distribution-free integrated multivariate statistical process control (MSPC) approach with an ability to recognize out-of-control points, identify the key influential variable for the out-of-control state, and determine necessary changes to achieve the state of statistical control. Design/methodology/approach - The proposed approach integrates the control chart technique, the Mahalanobis-Taguchi System concept, the Andrews function plot, and nonlinear optimization for multivariate process control. Mahalanobis distance, Taguchi's orthogonal array, and the main effect plot concept are used to identify the key influential variable responsible for the out-of-control situation. The Andrews function plot and nonlinear optimization help to identify direction and necessary correction to regain the state of statistical control. Finally, two different real life case studies illustrate the suitability of the approach. Findings - The case studies illustrate the potential of the proposed integrated multivariate process control approach for easy implementation in varied manufacturing and process industries. In addition, the case studies also reveal that the multivariate out-of-control state is primarily contributed by a single influential variable. Research limitations/implications - The approach is limited to the situation in which a single influential variable contributes to out-of-control situation. The number and type of cases used are also limited and thus generalization may not be debated. Further research is necessary with varied case situations to refine the approach and prove its extensive applicability. Practical implications - The proposed approach does not require multivariate normality assumption and thus provides greater flexibility for the industry practitioners. The approach is also easy to implement and requires minimal programming effort. A simple application Microsoft Excel is suitable fo

关键词： control chart Mahalanobis distance Quality control Mahalanobis-Taguchi System multivariate statistical process control

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Quality and Operation Management System for Steel Products through multivariate statistical process control

Quality and Operation Management System for Steel Products t...

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World Congress on Engineering (WCE 2013)

作者： Shigemori, Hiroyasu JFE Steel Corp Instrument & Control Engn Dept Steel Res Lab Fukuyama Hiroshima 7218510 Japan

ISBN: (纸本)9789881925107

A new quality and operation management method is proposed for products in steel production processes. The proposed method is based on multivariate statistical process control, that is one of the applications of principal component analysis. The quality and operation management system using the proposed method was developed and has been applied to actual plants. The proposed method enables to detect the deviation from the normal correlation among the manufacturing conditions of the objective production processes. Significant effects on speedup of quality anomaly detection have been achieved in commercial production processes. The developed system has made contributions to reducing probability of occurring a large amount of product defects.

关键词： quality management operation management steel products multivariate statistical process control principal component analysis

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A MATLAB toolbox for data pre-processing and multivariate statistical process control

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2019年 194卷 103863-000页

作者： Yi, Gang Herdsman, Craig Morris, Julian Univ Strathclyde Dept Pure & Appl Chem CPACT 295 Cathedral St Glasgow G1 1XL Lanark Scotland BP Chem Ltd Saltend Lane Kingston Upon Hull HU12 8DS N Humberside England

A multivariate statistical Data Pre-screening/Data Pre-processing Toolbox (Pre-Screen) has been designed and developed for use by practising process engineers and researchers who wish to pre-process process data prior to multivariate data analysis, process data modelling or building predictive and inferential models. Many commercial data analysis packages do not fully address the initial data cleaning and data conditioning tasks which can consume up to 80% of the modelling time. The software toolkit has been developed specifically with the aim of focusing on the industrial needs for the initial data pre-screening of large industrial data sets. The core feature of Pre-Screen is that it has been specifically developed to make the analysis of large data sets as fast and visual as possible, and accessible for both process and control engineers, analytical scientists and academic R&D without taking away the need for engineering science understanding. The toolbox builds on top of the MATLAB numerical computing environment, with powerful user interface procedures providing user friendly, mouse/menu driven software. The toolbox has been complied to allow use by those whom do not have access to MATLAB.

关键词： multivariate data analysis Data pre-processing Data pre-screening multivariate statistical process control Principal component analysis multivariate control charts Contribution plots Monitoring Fault diagnosis GUI

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Epileptic Seizure Monitoring by Using multivariate statistical process control

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IFAC Proceedings Volumes 2013年第31期46卷 249-254页

作者： Hirotsugu Hashimoto Koichi Fujiwara Yoko Suzuki Miho Miyajima Toshitaka Yamakawa Manabu Kano Taketoshi Maehara Katsuya Ohta Tetsuo Sasano Masato Matsuura Eisuke Matsushima Dept. of Systems Science Kyoto University Yoshida-Honmachi Sakyoku Kyoto 606-8501 JAPAN Section of Liaison Psychiatry and Palliative Medicine Tokyo Medical and Dental University 1-5-45 Yushima Bunkyo-ku Tokyo 113-8510 JAPAN Dept. of Electrical and Electronic Engineering Shizuoka University 3-5-1 Jyohoku Nakaku-ku Hamamatsu 432-8561 JAPAN Department of Neurosurgery Tokyo Medical and Dental University 1-5-45 Yushima Bunkyo-ku Tokyo 113-8510 JAPAN Section of Life Sciences and Biofunctional Informatics Tokyo Medical and Dental University 1-5-45 Yushima Bunkyo-ku Tokyo 113-8510 JAPAN

Although refractory epileptic patients suffer from uncontrolled seizures, their quality of life (QoL) may be improved if the seizure can be predicted in advance. In the preictal period, the excessive neuronal activity of epilepsy affects the autonomic nervous system. Since the fluctuation of the R-R interval (RRI) of an electrocardiogram (ECG), called heart rate variability (HRV), reflects the autonomic nervous function, an epileptic seizure may be predicted through monitoring RRI data. The present work proposes an HRV-based epileptic seizure monitoring method by utilizing multivariate statistical process control (MSPC) technology. Various HRV features are derived from the RRI data in both the interictal period and the preictal period recorded from epileptic patients, and an MSPC-based seizure prediction model is built from the interictal HRV features. The result of applying the proposed monitoring method to a clinical data demonstrates that seizures can be detected at least one minutes prior to the seizure onset. The possibility of realizing an HRV-based seizure monitoring system is shown.

关键词： Epilepsy Seizure prediction Heart rate variability analysis process monitoring multivariate statistical process control

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Variable Selection for multivariate statistical process control

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JOURNAL OF QUALITY TECHNOLOGY 2010年第3期42卷 242-259页

作者： Gonzalez, Isabel Sanchez, Ismael Univ Carlos III Madrid Dept Mech Engn Madrid 28911 Spain Univ Carlos III Madrid Dept Stat Madrid 28911 Spain

This article proposes a methodology to select a subset of variables to measure and monitor for multivariate statistical process control (SPC). In contrast with most dimensionality reduction approaches for SPC, we reduce the number of variables that must be measured, thereby reducing the time and cost associated with inspection. We develop a two-stage procedure that selects the variables in a manner that retains as much information on the full set of variables as possible. In the first stage, the variables are sorted according to some measure of information content, which has broad applicability outside of SPC. In the second stage, the selected variables are determined using two alternative tools. The first tool is a general criterion based on the amount of residual information in the nonselected variables. The second tool is based on the performance of the control chart in detecting simulated out-of-control events. We illustrate the usefulness of the approach with simulation results and a real metal-forming application.

关键词： multivariate statistical process control Principal Variables Singular Value Decomposition Variable Selection

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