Case studies in real-time fault isolation in a decentralized wastewater treatment facility

Abstract Decentralized wastewater treatment (WWT) can be an energy and resource efficient alternative to the traditional, centralized WWT paradigm for water-stressed communities. However, to operate economically, decentralized facilities do not typically have a WWT operator on-site full-time, so a real-time monitoring scheme is needed to quickly detect system faults and isolate the features associated with or affected by faults to ensure adequate treated water quality. Data collected from WWT facilities exhibit temporal dependence and experience natural fluctuations in the mean due to environmental and operator-controlled factors, violating the assumptions of many existing fault detection and isolation (FD&I) methods. To address this, we develop a complete data-driven FD&I method tuned to handle the unique features of WWT data that can be run in real-time and illustrate how it performs with data from a decentralized WWT facility in Golden, Colorado, USA. Enhanced visualization techniques are designed to assist operators in identifying features associated with the fault. We present three case studies with known faults and demonstrate how this method can aid operators in detecting and diagnosing the cause of a fault more quickly.

[1]  H. Zou.  The Adaptive Lasso and Its Oracle Properties , 2006 .

[2]  Philippe Castagliola,et al.  An adaptive exponentially weighted moving average chart for the mean with variable sampling intervals , 2017, Qual. Reliab. Eng. Int..

[3]  Zhonghua Li,et al.  A new adaptive CUSUM control chart for detecting the multivariate process mean , 2011, Qual. Reliab. Eng. Int..

[4]  G. Irwin,et al.  Process monitoring approach using fast moving window PCA , 2005 .

[5]  Kamran Paynabar,et al.  Large Multistream Data Analytics for Monitoring and Diagnostics in Manufacturing Systems , 2018, ArXiv.

[6]  Wei Jiang,et al.  High-Dimensional Process Monitoring and Fault Isolation via Variable Selection , 2009 .

[7]  Trevor Hastie,et al.  Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.

[8]  Yan-Kwang Chen,et al.  Hotelling's T2 charts with variable sample size and control limit , 2007, Eur. J. Oper. Res..

[9]  Dongdong Xiang,et al.  A Robust Multivariate EWMA Control Chart for Detecting Sparse Mean Shifts , 2016 .

[10]  Thomas W. Rauber,et al.  Cascade Feature Selection and ELM for automatic fault diagnosis of the Tennessee Eastman process , 2017, Neurocomputing.

[11]  Fugee Tsung,et al.  An adaptive dimension reduction scheme for monitoring feedback‐controlled processes , 2009, Qual. Reliab. Eng. Int..

[12]  M. A. Graham,et al.  Nonparametric (distribution-free) control charts: An updated overview and some results , 2019, Quality Engineering.

[13]  Si-Zhao Joe Qin,et al.  Reconstruction-based contribution for process monitoring , 2009, Autom..

[14]  Geert Gins,et al.  Industrial Process Monitoring in the Big Data/Industry 4.0 Era: from Detection, to Diagnosis, to Prognosis , 2017 .

[15]  Yuan Yao,et al.  Variable selection method for fault isolation using least absolute shrinkage and selection operator (LASSO) , 2015 .

[16]  Tzahi Y Cath,et al.  Data-driven performance analyses of wastewater treatment plants: A review. , 2019, Water research.

[17]  M. A. Graham,et al.  Phase I Statistical Process Control Charts: An Overview and Some Results , 2008 .

[18]  Peter A Vanrolleghem,et al.  Adaptive Consensus Principal Component Analysis for On-Line Batch Process Monitoring , 2004, Environmental monitoring and assessment.

[19]  Carlos F. Alcala,et al.  Reconstruction-based contribution for process monitoring with kernel principal component analysis , 2010, Proceedings of the 2010 American Control Conference.

[20]  Charles W. Champ,et al.  A multivariate exponentially weighted moving average control chart , 1992 .

[21]  Stefan H. Steiner,et al.  An Overview of Phase I Analysis for Process Improvement and Monitoring , 2014 .

[22]  Giovanna Capizzi,et al.  Recent Advances in Process Monitoring: Nonparametric and Variable-Selection Methods for Phase I and Phase II , 2015 .

[23]  Yuan Yao,et al.  Reconstruction-Based Multivariate Process Fault Isolation Using Bayesian Lasso , 2018 .

[24]  Dongdong Xiang,et al.  Univariate Dynamic Screening System: An Approach For Identifying Individuals With Irregular Longitudinal Behavior , 2014, Technometrics.

[25]  Tzahi Y. Cath,et al.  Multistate multivariate statistical process control , 2018 .

[26]  Wei Jiang,et al.  A LASSO-Based Diagnostic Framework for Multivariate Statistical Process Control , 2011, Technometrics.

[27]  Dongdong Xiang,et al.  Surveillance of cardiovascular diseases using a multivariate dynamic screening system , 2015, Statistics in medicine.

[28]  Chouaib Chakour,et al.  New Adaptive Moving Window PCA for Process Monitoring , 2012 .

[29]  Hao Yan,et al.  Real-Time Monitoring of High-Dimensional Functional Data Streams via Spatio-Temporal Smooth Sparse Decomposition , 2018, Technometrics.

[30]  Jun Li,et al.  Nonparametric dynamic screening system for monitoring correlated longitudinal data , 2016 .

[31]  Abdel Magid Hamouda,et al.  Variable Selection-based Multivariate Cumulative Sum Control Chart , 2017, Qual. Reliab. Eng. Int..

[32]  Christos Georgakis,et al.  Disturbance detection and isolation by dynamic principal component analysis , 1995 .

[33]  Tzahi Y. Cath,et al.  Fault isolation for a complex decentralized waste water treatment facility , 2020, Journal of the Royal Statistical Society: Series C (Applied Statistics).

[34]  Tzahi Y. Cath,et al.  Flexible hybrid membrane treatment systems for tailored nutrient management: A new paradigm in urban wastewater treatment , 2013 .

[35]  Fugee Tsung,et al.  A Variable-Selection-Based Multivariate EWMA Chart for Process Monitoring and Diagnosis , 2012 .

[36]  H. Hotelling.  The Generalization of Student’s Ratio , 1931 .

[37]  Daniel Aguado,et al.  Multivariate statistical monitoring of continuous wastewater treatment plants , 2008, Eng. Appl. Artif. Intell..

[38]  Kris Villez,et al.  Performance evaluation of fault detection methods for wastewater treatment processes , 2011, Biotechnology and bioengineering.

[39]  Tzahi Y. Cath,et al.  Decentralized wastewater treatment for distributed water reclamation and reuse: The good, the bad, and the ugly-experience from a case study , 2013 .

[40]  Giovanna Capizzi,et al.  A Least Angle Regression Control Chart for Multidimensional Data , 2011, Technometrics.

[41]  R. Crosier.  Multivariate generalizations of cumulative sum quality-control schemes , 1988 .

[42]  Jun Li,et al.  Construction of an efficient multivariate dynamic screening system , 2017, Qual. Reliab. Eng. Int..

[43]  Pavel Krupskii,et al.  Copula-based monitoring schemes for non-Gaussian multivariate processes , 2019, Journal of Quality Technology.

[44]  R. Rhodes Trussell,et al.  Potable reuse treatment trains throughout the world , 2013 .

[45]  Douglas C. Montgomery,et al.  Some Current Directions in the Theory and Application of Statistical Process Monitoring , 2014 .

[46]  Kaushik Ghosh,et al.  Optimal variable selection for effective statistical process monitoring , 2014, Comput. Chem. Eng..

[47]  A. Hering,et al.  Comparison of linear and nonlinear dimension reduction techniques for automated process monitoring of a decentralized wastewater treatment facility , 2016, Stochastic Environmental Research and Risk Assessment.

[48]  S Marsili-Libelli,et al.  Real-time fault detection and isolation in biological wastewater treatment plants. , 2009, Water science and technology : a journal of the International Association on Water Pollution Research.

[49]  José Manuel Benítez,et al.  Fault detection based on time series modeling and multivariate statistical process control , 2018, Chemometrics and Intelligent Laboratory Systems.

[50]  Dongdong Xiang,et al.  A robust self-starting spatial rank multivariate EWMA chart based on forward variable selection , 2017, Comput. Ind. Eng..

[51]  Si-Zhao Joe Qin,et al.  Survey on data-driven industrial process monitoring and diagnosis , 2012, Annu. Rev. Control..

[52]  C. Yoo,et al.  Nonlinear process monitoring using kernel principal component analysis , 2004 .

[53]  Bin Liu,et al.  A Mixture of Variational Canonical Correlation Analysis for Nonlinear and Quality-Relevant Process Monitoring , 2018, IEEE Transactions on Industrial Electronics.

[54]  Fouzi Harrou,et al.  Statistical monitoring of a wastewater treatment plant: A case study. , 2018, Journal of environmental management.

[55]  Shun Matsuura,et al.  Multivariate EWMA control chart based on a variable selection using AIC for multivariate statistical process monitoring , 2015 .