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Real-time remote monitoring of small-scaled biological wastewater treatment plants by a multivariate statistical process control and neural network-based software sensors

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process BIOCHEMISTRY 2008年第10期43卷 1107-1113页

作者： Lee, Min Woo Hong, Sung Hun Choi, Hyeoksun Kim, Ji-Hoon Lee, Dae Sung Park, Jong Moon Kyungpook Natl Univ Dept Environm Engn Taegu 702701 South Korea Pohang Univ Sci & Technol Dept Chem Engn Pohang 790784 Gyeongbuk South Korea Pohang Univ Sci & Technol Sch Environm Sci & Engn Pohang 790784 Gyeongbuk South Korea

A real-time remote monitoring system for wastewater treatment plants (WWTPs) has been developed to give local operators a guideline that would allow them to arrive at the optimum operational strategy in the early stage of a process disturbance. Especially, small-scaled WWTPs in Korea's rural areas show a large fluctuation in their influent loading and, therefore, they require an efficient operation for treatment of organic matter, nitrogen and phosphorus. However, under requirements to lower running costs, most of the small-scaled WWTPs are being forced to operate with a minimum number of operators. It is too costly for them to employ a local expert to maintain plant systems properly. In recent years, recognition of these problems has raised great interests in real-time remote monitoring systems. They serve the key information needed for efficient operation, and help to transfer knowledge from the experts at a remote control center to local operators in real-time. In this study, both operation data and measurement values from a novel mobile multi-sensor system were transmitted on-line by a telecommunication system. Then multivariate statistical process controls and software sensor techniques were applied to supervise local WWTPs. The developed remote monitoring system makes it possible to monitor the current plants' statuses and to support the operation of local wastewater systems. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： remote monitoring multivariate statistical process control principal component analysis neural network software sensor wastewater treatment plant

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Patient-Specific Epileptic Seizure Prediction in Long-Term Scalp EEG Signal Using multivariate statistical process control

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IRBM 2019年第6期40卷 320-331页

作者： Rukhsar, S. Khan, Y. U. Farooq, O. Sarfraz, M. Khan, A. T. Indian Inst Technol Dept Elect Engn Jodhpur 342037 Rajasthan India Aligarh Muslim Univ Fac Engn & Technol Dept Elect Engn Aligarh 202002 Uttar Pradesh India Aligarh Muslim Univ Fac Engn & Technol Dept Elect Engn Aligarh 202002 Uttar Pradesh India

An accurate epileptic seizure prediction algorithm can alleviate the problem and reduce risks in the life of a patient suffering from epilepsy. The main motive of this work is to propose a model which can predict seizures well in advance of its occurrence. multivariate statistical process control (MSPC) has been used for seizure predictions in long-term scalp EEG signal. It has been observed that excessive neuronal activity in the preictal period of seizure changes the electrical characteristic from chaotic to rhythmic behavior. These changes have been utilized for prediction. Eight temporal based features are used for predicting the seizures by using multivariate statistical process control, which is widely known as an anomaly monitoring method. 90 seizures from the CHB-MIT EEG data of ten patients are analyzed. Result: The results of the proposed method demonstrated that 80 seizures out of 90 in preictal period were correctly predicted prior to the seizure onset, thereby giving a sensitivity of 88.89%. The false positive rate is observed to 0.39 per hour. Conclusion: This study proposed a temporal based patient-specific epileptic seizure prediction method using MSPC in long-term scalp EEG signals. It also provides the possibility of realizing an EEG-based epileptic seizure prediction system which requires less computational power. Significance: The proposed method does not require preictal data for modeling. The extracted features are computationally easy. The tested result shows good accuracy on the CHB-MIT data base. (C) 2019 AGBM. Published by Elsevier Masson SAS. All rights reserved.

关键词： multivariate statistical process control Squared prediction error (Q) T-2 Hotelling parameters PCA Epilepsy prediction

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Multistate multivariate statistical process control

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APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY 2018年第6期34卷 880-892页

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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Improvement of causal analysis using multivariate statistical process control

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SOFTWARE QUALITY JOURNAL 2008年第3期16卷 377-409页

作者： Chang, Ching-Pao Chu, Chih-Ping Natl Cheng Kung Univ Dept Comp Sci & Informat Engn Tainan 701 Taiwan

statistical process control (SPC) is a conventional means of monitoring software processes and detecting related problems, where the causes of detected problems can be identified using causal analysis. Determining the actual causes of reported problems requires significant effort due to the large number of possible causes. This study presents an approach to detect problems and identify the causes of problems using multivariate SPC. This proposed method can be applied to monitor multiple measures of software process simultaneously. The measures which are detected as the major impacts to the out-of-control signals can be used to identify the causes where the partial least squares (PLS) and statistical hypothesis testing are utilized to validate the identified causes of problems in this study. The main advantage of the proposed approach is that the correlated indices can be monitored simultaneously to facilitate the causal analysis of a software process.

关键词： multivariate statistical process control causal analysis software process improvement

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Application of multivariate statistical process control to batch operations

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COMPUTERS & CHEMICAL ENGINEERING 2000年第2-7期24卷 291-296页

作者： Lennox, B Hiden, HG Montague, GA Kornfeld, G Goulding, PR Univ Manchester Sch Engn Control Technol Ctr Manchester M13 9PL Lancs England GSE Syst Ltd Hexham Northd England Univ Newcastle Upon Tyne Dept Chem Engn Newcastle Upon Tyne NE1 7RU Tyne & Wear England Biochem Ltd Kundl Austria

This paper summarises the results of a 2-year study focusing on the development of a condition monitoring system for a fed-batch fermentation system operated by Biochemie Ltd. in Austria. Consumer pressure has resulted in a greater emphasis in industry on product quality. As a direct consequence, the importance of accurate process monitoring has increased steadily in recent years. This paper demonstrates the application of multivariate statistical routines to provide process operators with a monitoring tool capable of detecting process abnormalities. (C) 2000 Elsevier Science Ltd. All rights reserved.

关键词： fault detection fault diagnosis fermentation multivariate statistical process control principal component analysis

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Design multivariate statistical process control procedure in the case of Ethio cement

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INTERNATIONAL JOURNAL OF QUALITY & RELIABILITY MANAGEMENT 2022年第7期39卷 1617-1636页

作者： Tegegne, Daniel Ashagrie Azene, Daniel Kitaw Atanaw, Eshetie Berhan Addis Ababa Inst Technol Mech & Ind Engn Addis Ababa Ethiopia

PurposeThis study aims to design a multivariate control chart that improves the applicability of the traditional Hotelling T-2 chart. This new type of multivariate control chart displays sufficient information about the states and relationships of the variables in the production process. It is used to make better quality control decisions during the production ***/methodology/approachmultivariate data are collected at an equal time interval and are represented by nodes of the graph. The edges connecting the nodes represent the sequence of operation. Each node is plotted on the control chart based on their Hotelling T-2 statistical distance. The changing behavior of each pair of input and output nodes is studied by the neural network. A case study from the cement industry is conducted to validate the control *** finding of this paper is that the points and lines in the classic Hotelling T-2 chart are effectively substituted by nodes and edges of the graph respectively. Nodes and edges have dimension and color and represent several attributes. As a result, this control chart displays much more information than the traditional Hotelling T-2 control chart. The pattern of the plot represents whether the process is normal or not. The effect of the sequence of operation is visible in the control chart. The frequency of the happening of nodes is recognized by the size of nodes. The decision to change the product feature is assisted by finding the shortest path between nodes. Moreover, consecutive nodes have different behaviors, and that behavior change is recognized by neural ***/valueModifying the classical Hotelling T-2 control chart by integrating with the concept of graph theory and neural network is new of its kind.

关键词： multivariate statistical process control Hotelling T-2 Neural network Graph theory Clinker production process control GANNT chart

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Improvement of causal analysis using multivariate statistical process control

Improvement of causal analysis using multivariate statistica...

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22nd ACM Symposium on Applied Computing

作者： Chang, Ching-Pao Chu, Chih-Ping Natl Cheng Kung Univ Dept Comp Sci & Informat Engn Tainan 701 Taiwan

关键词： multivariate statistical process control causal analysis software process improvement

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Application of multivariate statistical process control to batch operations

Application of multivariate statistical process control to b...

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7th International Symposium on process Systems Engineering

关键词： fault detection fault diagnosis fermentation multivariate statistical process control principal component analysis

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Dynamic multivariate statistical process control using partial least squares and canonical variate analysis

Dynamic multivariate statistical process control using parti...

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European Symposium on Computer Aided process Engineering-9 (Escape-9)

作者： Simoglou, A Martin, EB Morris, AJ Univ Newcastle Upon Tyne CPACT Foresight Ctr Proc Analyt & Control Technol Newcastle Upon Tyne NE1 7RU Tyne & Wear England

multivariate statistical techniques have been shown to be useful tools for multivariate statistical process control (MSPC) and process modelling. However, these approaches have been mainly applied to static systems. In the present work, a well known system representation, the state space model, is developed to deal with dynamic situations. The states of the system are approximated using two multivariate statistical projection techniques, Partial Least Squares (PLS) and Canonical Variate Analysis (CVA). These two model representations are compared both in terms of their predictive ability and also their monitoring power using a simulation example. An application to an industrial fluidised bed reactor will be presented at the conference following company approval.

关键词： multivariate statistical process control state space models partial least squares canonical variate analysis statistical process monitoring prediction of product quality

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On the Feasibility of Distinguishing Between process Disturbances and Intrusions in process control Systems using multivariate statistical process control 46

On the Feasibility of Distinguishing Between Process Disturb...

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46th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W)

作者： Iturbe, Mikel Camacho, Jose Garitano, Inaki Zurutuza, Urko Uribeetxeberria, Roberto Mondragon Univ Dept Elect & Comp Fac Engn E-20500 Arrasate Mondragon Spain Univ Granada Dept Signal Theory Telemat & Commun CITIC E-18071 Granada Spain

ISBN: (纸本)9781467388917

process control Systems (PCSs) are the operating core of Critical Infrastructures (CIs). As such, anomaly detection has been an active research field to ensure CI normal operation. Previous approaches have leveraged network level data for anomaly detection, or have disregarded the existence of process disturbances, thus opening the possibility of mislabelling disturbances as attacks and vice versa. In this paper we present an anomaly detection and diagnostic system based on multivariate statistical process control (MSPC), that aims to distinguish between attacks and disturbances. For this end, we expand traditional MSPC to monitor process level and controller level data. We evaluate our approach using the Tennessee-Eastman process. Results show that our approach can be used to distinguish disturbances from intrusions to a certain extent and we conclude that the proposed approach can be extended with other sources of data for improving results.

关键词： process control systems multivariate statistical process control Tennessee-Eastman

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