scholarly journals Improved Statistical Pattern Analysis Monitoring for Complex Multivariate Processes Using Empirical Likelihood

Processes ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 1619
Author(s):  
Jianwen Shao ◽  
Xin Zhang ◽  
Wenhua Chen ◽  
Xiaomin Shen
Keyword(s):  
Fault Detection ◽  
Empirical Likelihood ◽  
Pattern Analysis ◽  
Estimation Method ◽  
Mean Value ◽  
Monitoring Strategy ◽  
Multivariate Processes ◽  
Statistical Pattern ◽  
Non Gaussian ◽  
High Order Statistics

This article developed an improved statistical pattern analysis (SPA) monitoring strategy for fault detection of complex multivariate processes using empirical likelihood. The technique based on statistical pattern analysis performs fault detection by inspecting change in the statistics of process variables (e.g., mean value, correlation coefficient, variance, kurtosis, etc.). It is capable of monitoring non-Gaussian or even nonlinear processes. However, the original SPA framework explicitly computes all the high-order statistics, which significantly increases the scale and dimensionality of the problem, especially in the case of complex multivariate processes. To alleviate this difficulty, we propose monitoring changes in the statistics with the same order using empirical likelihood, which is a widely used estimation method to construct confidence limits or regions for parameters with similar properties. As a result, changes in statistics of the same order can be translated into a single index; hence more information on the faulty conditions can be observed. Furthermore, by considering statistics of the same order, the scale of the problem is reduced significantly. The improved statistical pattern analysis monitoring strategy is suitable for monitoring complex multivariate processes. The performance of the improved method is illustrated by an application study to fault detection of the Tennessee Eastman (TE) process.

scholarly journals SPA-Based Modified Local Reachability Density Ratio wSVDD for Nonlinear Multimode Process Monitoring

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Zhaojing Wang ◽  
Weidong Yang ◽  
Hong Zhang ◽  
Ying Zheng
Keyword(s):  
Treatment Process ◽  
Pattern Analysis ◽  
Density Ratio ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Process Data ◽  
Wastewater Treatment Process ◽  
Non Gaussian ◽  
High Order Statistics

Many industrial processes are operated in multiple modes due to different manufacturing strategies. Multimodality of process data is often accompanied with nonlinear and non-Gaussian characteristics, which makes data-driven monitoring more complicated. In this paper, statistics pattern analysis (SPA) is introduced to extract low- and high-order statistics from raw process data. Support vector data description (SVDD), which can deal with nonlinear and non-Gaussian problems, is applied to monitor multimode process in this paper. To improve detection performance of SVDD for training multimode data with outliers, modified local reachability density ratio (mLRDR) is proposed as a weight factor to be embedded in the weighted-SVDD (wSVDD) model, in which the local neighbors in terms of both space and time are considered. Finally, the effectiveness and superiority of our proposed method are demonstrated by the Tennessee-Eastman (TE) process and wastewater treatment process (WWTP).

scholarly journals Improved Process Monitoring Scheme Using Multi-Scale Independent Component Analysis

2021 ◽  
Author(s):  
K Ramakrishna Kini ◽  
Muddu Madakyaru
Keyword(s):  
Fault Detection ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Noisy Environment ◽  
Process Data ◽  
Multi Scale ◽  
Efficient Information ◽  
Non Gaussian

AbstractThe task of fault detection is crucial in modern chemical industries for improved product quality and process safety. In this regard, data-driven fault detection (FD) strategy based on independent component analysis (ICA) has gained attention since it improves monitoring by capturing non-gaussian features in the process data. However, presence of measurement noise in the process data degrades performance of the FD strategy since the noise masks important information. To enhance the monitoring under noisy environment, wavelet-based multi-scale filtering is integrated with the ICA model to yield a novel multi-scale Independent component analysis (MSICA) FD strategy. One of the challenges in multi-scale ICA modeling is to choose the optimum decomposition depth. A novel scheme based on ICA model parameter estimation at each depth is proposed in this paper to achieve this. The effectiveness of the proposed MSICA-based FD strategy is illustrated through three case studies, namely: dynamic multi-variate process, quadruple tank process and distillation column process. In each case study, the performance of the MSICA FD strategy is assessed for different noise levels by comparing it with the conventional FD strategies. The results indicate that the proposed MSICA FD strategy can enhance performance for higher levels of noise in the data since multi-scale wavelet-based filtering is able to de-noise and capture efficient information from noisy process data.

scholarly journals Gaussian process regression for monitoring and fault detection of wastewater treatment processes

2017 ◽  
Vol 75 (12) ◽  
pp. 2952-2963 ◽  
Author(s):  
Oscar Samuelsson ◽  
Anders Björk ◽  
Jesús Zambrano ◽  
Bengt Carlsson
Keyword(s):  
Wastewater Treatment ◽  
Missing Data ◽  
Fault Detection ◽  
Gaussian Process ◽  
Flow Rate ◽  
Sequential Monte Carlo ◽  
Estimation Method ◽  
Gaussian Process Regression ◽  
Local Optima ◽  
Efficient Operation

Monitoring and fault detection methods are increasingly important to achieve a robust and resource efficient operation of wastewater treatment plants (WWTPs). The purpose of this paper was to evaluate a promising machine learning method, Gaussian process regression (GPR), for WWTP monitoring applications. We evaluated GPR at two WWTP monitoring problems: estimate missing data in a flow rate signal (simulated data), and detect a drift in an ammonium sensor (real data). We showed that GPR with the standard estimation method, maximum likelihood estimation (GPR-MLE), suffered from local optima during estimation of kernel parameters, and did not give satisfactory results in a simulated case study. However, GPR with a state-of-the-art estimation method based on sequential Monte Carlo estimation (GPR-SMC) gave good predictions and did not suffer from local optima. Comparisons with simple standard methods revealed that GPR-SMC performed better than linear interpolation in estimating missing data in a noisy flow rate signal. We conclude that GPR-SMC is both a general and powerful method for monitoring full-scale WWTPs. However, this paper also shows that it does not always pay off to use more sophisticated methods. New methods should be critically compared against simpler methods, which might be good enough for some scenarios.

Fault Detection of Electric Motors Application Using ML Estimation Method

2012 ◽  
Vol 591-593 ◽  
pp. 1958-1961
Author(s):  
Juggrapong Treetrong
Keyword(s):  
Fault Detection ◽  
Estimation Method ◽  
Load Condition ◽  
Fault Analysis ◽  
Electric Motors ◽  
Ml Estimation ◽  
Stator Current ◽  
Fault Severity ◽  
Severity Levels ◽  
Stator Fault

This paper proposes a new method of motor fault detection. ML Estimation is proposed as a key technique for signal processing. The stator current is used data for motor fault analysis. ML Estimation is generally applied to estimate signals for nonlinear model. The expectation is that the method can provide information for fault analysis. The method is tested on 3 different motor conditions: healthy, stator fault, and rotor fault motor at full load condition. Based on experiments, the method can differentiate conditions clearly and be also able to measure fault severity levels.

scholarly journals A Combined RMS-MEAN Value Approach for an Inverter Open-Circuit Fault Detection

2019 ◽  
Vol 63 (3) ◽  
pp. 169-177
Author(s):  
Mohamed Amine Khelif ◽  
Azeddine Bendiabdellah ◽  
Bilal Djamal Eddine Cherif
Keyword(s):  
Fault Detection ◽  
Induction Machines ◽  
Mean Value ◽  
Open Circuit ◽  
Major Research ◽  
Three Phase ◽  
Paper Work ◽  
The Mean ◽  
Simulation Results ◽  
Structural Failures

Currently, with the power electronics evolution, a major research axis is oriented towards the diagnosis of converters supplying induction machines. Indeed, a converter such as the inverter is susceptible to have structural failures such as faulty leg and/or open-circuit IGBT faults. In this paper, the detection of the faulty leg and the localization of the open-circuit switch of an inverter are investigated. The fault detection technique used in this work is based essentially upon the monitoring of the root mean square (RMS) value and the calculation of the mean value of the three-phase currents. In the first part of the paper work, the faulty leg is detected by monitoring the RMS value of the three-phase currents and comparing them to the nominal value of the phase current. The second part, the open-circuit IGBT fault is localized simply by knowing the polarity of the calculated mean value current of the faulty phase. The work is first accomplished using simulation work and then the obtained simulation results are validated by experimental work conducted in our LDEE laboratory to illustrate the effectiveness, simplicity and rapidity of the proposed technique.

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