Fault Detection for Large Scale Systems Using Dynamic Principal Components Analysis with Adaptation

Mina, Jesus; Verde, Cristina

doi:10.15837/ijccc.2007.2.2351

Cited by 17 publications

(6 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Generally, the smoothing constant should not be too small, so that a short-term trend in the intensity of events in the recent past can be detected.Other EWMA Algorithm in Network Practice 165 publications have also shown the need for taking into account the autocorrelation of input data. As it is emphasized in [18], in the case of dynamic systems the autocorrelation in variables is taking into account incorporating time lags of the time series during the modeling stage.…”

Section: Autocorrelationmentioning

confidence: 99%

EWMA Algorithm in Network Practice

Čisar¹,

Bošnjak²,

Čisar³

2010

INT J COMPUT COMMUN

View full text Add to dashboard Cite

Intrusion detection is used to monitor and capture intrusions into computer and network systems which attempt to compromise their security. Many intrusions manifest in changes in the intensity of events occuring in computer networks. Because of the ability of exponentially weighted moving average (EWMA) control charts to monitor the rate of occurrences of events based on their intensity, this technique is appropriate for implementation in control limits based algorithms. The paper also gives a review of a possible optimization method. The validation check of results will be performed on authentic network samples.

show abstract

Section: Autocorrelationmentioning

confidence: 99%

EWMA Algorithm in Network Practice

Čisar¹,

Bošnjak²,

Čisar³

2010

INT J COMPUT COMMUN

View full text Add to dashboard Cite

show abstract

“…To account for autocorrelation, a dynamic PCA duplicates a process variable in a data set and lags it by the number of time steps with the strongest autocorrelation for a given variable . Dynamic PCA is another common extension of PCA for monitoring industrial processes as well as WTPs and WWTPs. ,− For most WTP and WWTP applications, a lag of a single time step is sufficient …”

Section: Introductionmentioning

confidence: 99%

“… 36 Dynamic PCA is another common extension of PCA for monitoring industrial processes as well as WTPs and WWTPs. 26 , 36 − 39 For most WTP and WWTP applications, a lag of a single time step is sufficient. 9 …”

Section: Introductionmentioning

confidence: 99%

A Holistic Evaluation of Multivariate Statistical Process Monitoring in a Biological and Membrane Treatment System

et al. 2023

View full text Add to dashboard Cite

Unsupervised process monitoring for fault detection and data cleaning is underdeveloped for municipal wastewater treatment plants (WWTPs) due to the complexity and volume of data produced by sensors, equipment, and control systems. The goal of this work is to extensively test and tune an unsupervised process monitoring method that can promptly identify faults in a full-scale decentralized WWTP prior to significant system changes. Adaptive dynamic principal component analysis (AD-PCA) is a dimension reduction method modified to address autocorrelation and nonstationarity in multivariate processes and is evaluated in this work for its ability to continuously detect drift, shift, and spike faults. For spike faults, univariate drift faults, and multivariate shift faults, implementing AD-PCA on data that are subset by treatment processes and operating states with significant differences in covariates and whose model parameters use week-long training windows, moderate cumulative variance, and a high threshold for detection was found to detect faults prior to existing operational thresholds. To improve the consistency with which the AD-PCA method detects out-of-control conditions in real time, additional work is needed to remove outliers prior to model fitting and to detect multivariate drift faults in which the covariates change slowly.

show abstract

“…It reduces multi a dimensional data set to a set of directions, called principal components (PC), in the decreasing order of importance, retaining variability of the data and their mutual variations, highlighting broadly the similarities and differences [14][15][16][17]. Hence, PCA has been used extensively in power system analysis, especially in fault detection, classification and distance prediction where multiple dimensional data are obtained regarding voltage, current, power, frequency etc and/or a combination of these parameters [18][19][20][21][22]. Hence, fault analysis becomes an important issue in power system research.…”

Section: Introductionmentioning

confidence: 99%

Power system fault identification and localization using multiple linear regression of principal component distance indices

Mukherjee

Kundu

Das

2020

IJAPE

View full text Add to dashboard Cite

<span>This paper is focused on the application of principal component analysis (PCA) to classify and localize power system faults in a three phase, radial, long transmission line using receiving end line currents taken almost at the midpoint of the line length. The PCA scores are analyzed to compute principal component distance index (PCDI) which is further analyzed using a ratio based analysis to develop ratio index matrix (R) and ratio error matrix (RE) and ratio error index (REI) which are used to develop a fault classifier, which produces a 100% correct prediction. The later part of the paper deals with the development of a fault localizer using the same PCDI corresponding to six intermediate training locations, which are analyzed with tool like Multiple Linear Regression (MLR) in order to predict the fault location with significantly high accuracy of only 87 m for a 150 km long radial transmission line.</span>

show abstract

Fault Detection for Large Scale Systems Using Dynamic Principal Components Analysis with Adaptation

Cited by 17 publications

References 14 publications

EWMA Algorithm in Network Practice

EWMA Algorithm in Network Practice

A Holistic Evaluation of Multivariate Statistical Process Monitoring in a Biological and Membrane Treatment System

Power system fault identification and localization using multiple linear regression of principal component distance indices

Contact Info

Product

Resources

About