A double-max MEWMA scheme for simultaneous monitoring and fault isolation of multivariate multistage auto-correlated processes based on novel reduced-dimension statistics

Pirhooshyaran, Mohammad; Niaki, Seyed Taghi Akhavan

doi:10.1016/j.jprocont.2015.03.008

Cited by 21 publications

(12 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this subsection, we summarize the other approaches in the literature which we compare the proposed algorithm with. Seasonal auto regressive integrated moving average (SARIMA): Auto regressive moving average models [34][35][36] decompose the series into moving average and auto regressive ones. "Integrated" means the model uses a differencing process to reach stationarity considering a specific confidence level.…”

Section: Alternative Algorithmsmentioning

confidence: 99%

Feature engineering and forecasting via derivative-free optimization and ensemble of sequence-to-sequence networks with applications in renewable energy

Pirhooshyaran

Scheinberg

Snyder

2020

Energy

Self Cite

View full text Add to dashboard Cite

This study introduces a framework for the forecasting, reconstruction and feature engineering of multivariate processes along with its renewable energy applications. We integrate derivative-free optimization with an ensemble of sequence-to-sequence networks and design a new resampling technique called additive resampling, which, along with Bootstrap aggregating (bagging) resampling, are applied to initialize the ensemble structure. Moreover, we explore the proposed framework performance on three renewable energy sources-wind, solar and ocean wave-and conduct several short-to long-term forecasts showing the superiority of the proposed method compared to numerous machine learning techniques. The findings indicate that the introduced method performs more accurately when the forecasting horizon becomes longer. In addition, we modify the framework for automated feature selection. The model represents a clear interpretation of the selected features. Furthermore, we investigate the effects of different environmental and marine factors on the wind speed and ocean output power, respectively, and report the selected features. Finally, we explore the online forecasting setting and illustrate that the model outperforms alternatives through different measurement errors.

show abstract

Section: Alternative Algorithmsmentioning

confidence: 99%

Feature engineering and forecasting via derivative-free optimization and ensemble of sequence-to-sequence networks with applications in renewable energy

Pirhooshyaran

Scheinberg

Snyder

2020

Energy

Self Cite

View full text Add to dashboard Cite

show abstract

“…Huang et al proposed an autoregressive (AR) moving average method to describe a time‐series model, and two special cause charts were developed in monitoring the predicted values. Pirhooshyaran and Niaki developed a double‐max multivariate exponentially weighted moving average (DM‐MEWMA) chart based on a novel statistic to simultaneously detect shifts in mean and variability of multistage processes. Akhundjanov and Pascual adopted the EWMA chart to monitor the correlated multivariate processes of Poisson distribution without an assumption of negative correlations.…”

Section: Introductionmentioning

confidence: 99%

Deep recurrent neural network‐based residual control chart for autocorrelated processes

Chen

2019

Quality & Reliability Eng

View full text Add to dashboard Cite

With the growth of automation in process industries, there is correlation in the process variables. Deep learning has achieved many great successes in image and visual analysis. This paper concentrates on developing a deep recurrent neural network (RNN) model to characterize process variables at vary time lags, and then a residual chart is developed to detect mean shifts in autocorrelated processes. The experiment results indicate that the RNN‐based residual chart outperforms other typical methods (eg, autoregressive [AR]‐based control chart, back propagation network [BPN]‐based residual chart). This paper provides guideline for deep learning technique employed as an effective tool in autocorrelated process control.

show abstract

“…Wang et al [33] applied the generalized likelihood ratio test and the multivariate exponentially weighted moving covariance control chart to monitor the mean vector and the covariance matrix of a multivariate normal process, simultaneously. Other research for simultaneous monitoring of multivariate process consists of Khoo [34], Hawkins and Maboudou-Tchao [35], Ramos et al [36], and Pirhooshyaran and Niaki [37]. For detailed information on simultaneous monitoring of the process location and dispersion, refer to the review paper provided by McCracken and Chakraborti [38].…”

Section: Introductionmentioning

confidence: 99%

Maximum Multivariate Exponentially Weighted Moving Average and Maximum Multivariate Cumulative Sum Control Charts for Simultaneous Monitoring of Mean and Variability of Multivariate Multiple Linear Regression Profiles

Ghashghaei

Amiri

2017

Scientia Iranica

View full text Add to dashboard Cite

Abstract. In some applications, quality of product or performance of a process is described by some functional relationships among some variables known as multivariate linear pro le in the literature. In this paper, we propose Max-MEWMA and Max-MCUSUM control charts for simultaneous monitoring of mean vector and covariance matrix in multivariate multiple linear regression pro les in Phase II. The proposed control charts also have the ability to diagnose whether the location or variation of the process is responsible for out-of-control signal. The performance of the proposed control charts is compared with that of the existing method through Monte-Carlo simulations. Finally, the applicability of the proposed control charts is illustrated using a real case of calibration application in the automotive industry.

show abstract

A double-max MEWMA scheme for simultaneous monitoring and fault isolation of multivariate multistage auto-correlated processes based on novel reduced-dimension statistics

Cited by 21 publications

References 31 publications

Feature engineering and forecasting via derivative-free optimization and ensemble of sequence-to-sequence networks with applications in renewable energy

Feature engineering and forecasting via derivative-free optimization and ensemble of sequence-to-sequence networks with applications in renewable energy

Deep recurrent neural network‐based residual control chart for autocorrelated processes

Maximum Multivariate Exponentially Weighted Moving Average and Maximum Multivariate Cumulative Sum Control Charts for Simultaneous Monitoring of Mean and Variability of Multivariate Multiple Linear Regression Profiles

Contact Info

Product

Resources

About