Fault detection in wastewater treatment plants using distributed PCA methods

Sanchez-Fernandez, A.; Fuente, José Luis de la; Sainz-Palmero, G.I.

doi:10.1109/etfa.2015.7301504

Cited by 19 publications

(12 citation statements)

References 10 publications

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“…h j = 0 (13) (iv) The equality constraints corresponding to the KKT conditions given by Equations (12) and (13) were solved symbolically to obtain Lagrange multipliers and model parameters, θ(x), as an explicit function of measurements,x. (v) The solutions obtained in the previous step were examined and solutions with imaginary parts were ignored.…”

Section: Problem Definitionmentioning

confidence: 99%

“…(iv) The equality constraints corresponding to the KKT conditions given by Equations (12) and (13) were solved symbolically to obtain Lagrange multipliers and model parameters, ( )…”

Section: Wastewater Treatment Systemmentioning

confidence: 99%

“…Adaptive PCA was used in Baggiani and Marsili-Libelli [12] to compare the current plant operation with an exact performance based on a reference data set and sensor outputs. In Sanchez-Fernández et al [13], a distributed PCA was applied to detect faults by minimizing the communication cost between the blocks in WWTPs. The classical principal component analysis was presented using Benchmark Simulation Model No.…”

Section: Introductionmentioning

confidence: 99%

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Fault Detection in Wastewater Treatment Systems Using Multiparametric Programming

Mid

Dua

2018

Processes

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In this work, a methodology for fault detection in wastewater treatment systems, based on parameter estimation, using multiparametric programming is presented. The main idea is to detect faults by estimating model parameters, and monitoring the changes in residuals of model parameters. In the proposed methodology, a nonlinear dynamic model of wastewater treatment was discretized to algebraic equations using Euler’s method. A parameter estimation problem was then formulated and transformed into a square system of parametric nonlinear algebraic equations by writing the optimality conditions. The parametric nonlinear algebraic equations were then solved symbolically to obtain the concentration of substrate in the inflow, , inhibition coefficient, , and specific growth rate, , as an explicit function of state variables (concentration of biomass, ; concentration of organic matter, ; concentration of dissolved oxygen, ; and volume, ). The estimated model parameter values were compared with values from the normal operation. If the residual of model parameters exceeds a certain threshold value, a fault is detected. The application demonstrates the viability of the approach, and highlights its ability to detect faults in wastewater treatment systems by providing quick and accurate parameter estimates using the evaluation of explicit parametric functions.

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Section: Problem Definitionmentioning

confidence: 99%

“…(iv) The equality constraints corresponding to the KKT conditions given by Equations (12) and (13) were solved symbolically to obtain Lagrange multipliers and model parameters, ( )…”

Section: Wastewater Treatment Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fault Detection in Wastewater Treatment Systems Using Multiparametric Programming

Mid

Dua

2018

Processes

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“…One of the early applications of PCA to WWT process monitoring was by Wise et al, and PCA was also applied to the similar task of chemical systems monitoring by Kresta et al Baggiani and Marsili‐Libelli discussed implementing adaptive PCA to detect faults in a large‐scale centralized WWT plant by monitoring 3 process variables. Sanchez‐Fernández et al divided the fault detection problem of a large‐scale centralized WWT plant by monitoring subsystems within the plant with distributed PCA. Kazor et al were the first to focus exclusively on SPC of a decentralized WWT facility monitoring in a very complex and realistic setting with over 25 variables measured every 10 minutes.…”

Section: Introductionmentioning

confidence: 99%

Multistate multivariate statistical process control

Odom

Newhart

Cath

et al. 2018

Appl Stoch Models Bus & Ind

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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. KEYWORDS multistate adaptive-dynamic principle component analysis, multivariate statistical process control, nonlinear time-varying process 880

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“…The most important step in this technique is to establish the criteria used to divide the plant. Some authors opted for one-variable blocks [13], while other authors constructed blocks that group more than one variable ( [8], [10], [12], [14]). In the second option (multivariable blocks), there are some possibilities: the previous knowledge of the plant can be used to group variables that have any physical connection ( [9], [15]), however, as not always is available enough information about the plant to do the decentralization, many authors have tested different methods to divide the plant only analysing measured data ( [8], [12], [14], [16]), obtaining significant relations between variables.…”

Section: Introductionmentioning

confidence: 99%

Decentralized and Dynamic Fault Detection Using PCA and Bayesian Inference

Sanchez-Fernandez

Fuente

Sainz-Palmero

2018

2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA)

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This paper proposes a dynamic and decentralized fault detection method. The plant is divided in groups whose members are selected using linear and non-linear modelling techniques. In each group a Principal Component Analysis model does the fault detection, including delayed data to get a dynamic method. Then, a central node fuses the results of each group, using Bayesian Index Criterion (BIC), to get a global detection outcome. The method was tested on a widely used benchmark and compared with other proposal to check its effectiveness.

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Fault detection in wastewater treatment plants using distributed PCA methods

Cited by 19 publications

References 10 publications

Fault Detection in Wastewater Treatment Systems Using Multiparametric Programming

Fault Detection in Wastewater Treatment Systems Using Multiparametric Programming

Multistate multivariate statistical process control

Decentralized and Dynamic Fault Detection Using PCA and Bayesian Inference

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