scholarly journals THE DEVELOPMENT AND INVESTIGATION ANALYSIS OF AN ARX-BASED GENERALIZED LIKELIHOOD RATIO (GLR) STICTION DETECTION METHOD

2018 ◽  
Vol 80 (4) ◽  
Author(s):  
Nur Amalina Shairah Abdul Samat ◽  
Haslinda Zabiri ◽  
Bashariah Kamaruddin
Keyword(s):  
Case Studies ◽  
Likelihood Ratio ◽  
Null Hypothesis ◽  
Detection Method ◽  
Detection Methods ◽  
Statistical Hypothesis ◽  
Decision Threshold ◽  
Statistical Hypothesis Testing ◽  
Generalized Likelihood Ratio ◽  
Significance Level

Control valve stiction is one of the main sources of nonlinearity which can result in many deleterious effects on the control loop performance of a process. The study of stiction detection methods has now becoming one of the essential research areas in process control. In this present work, an ARX-based Generalized Likelihood Ratio (GLR) stiction detection method is proposed and its effectiveness is analyzed. The implementation of the proposed method involves three main stages; 1) ARX model identification, 2) GLR test, and 3) statistical hypothesis testing. The proposed detection method was applied to two benchmark simulated case studies. Results showed that the method effectively detect stiction. The presence of stiction is declared if the GLR test statistics,  exceeds the decision threshold limit, , and the null hypothesis is rejected at 5% significance level. On the other hand, if  value lies below , the null hypothesis is accepted and the absence of stiction is confirmed. In addition, it is also observed that the proposed method is reasonably insensitive and robust to the changes in the process gain,  and time constant,  as it generally allows up to ±10% changes in the two parameters for both case studies.

Fault Detection in Linear Discrete Dynamic Systems by a Pattern Recognition of a Generalized-Likelihood-Ratio

1990 ◽  
Vol 112 (2) ◽  
pp. 276-282 ◽  
Author(s):  
S. Tanaka ◽  
P. C. Mu¨ller
Keyword(s):  
Pattern Recognition ◽  
Likelihood Ratio ◽  
Dynamic Systems ◽  
Abrupt Change ◽  
Detection Method ◽  
Discrete Dynamical System ◽  
Generalized Likelihood Ratio ◽  
Robust Detection ◽  
Chi Squared ◽  
Discrete Dynamic Systems

The detection of an abrupt change in the parameters of a linear discrete dynamical system is considered in the framework of the easily implemented generalized-likelihood-ratio (GLR) method. This paper proposes a robust detection method based on a pattern recognition of the maximum GLR provided by the conventional step-hypothesized GLR method. A numerical example demonstrates that the proposed method is highly superior to the conventional step-hypothesized GLR method and to the Chi-squared test in both detection rate and detection speed.

Object Detection Method Based on Generalized Likelihood Ratio Tests Method in Photon Images

2010 ◽  
Vol 30 (1) ◽  
pp. 91-96
Author(s):  
赵磊 Zhao Lei ◽  
俞信 Yu Xin ◽  
陈翼男 Chen Yi′nan ◽  
阎吉祥 Yan Jixiang
Keyword(s):  
Object Detection ◽  
Likelihood Ratio ◽  
Detection Method ◽  
Likelihood Ratio Tests ◽  
Generalized Likelihood Ratio

scholarly journals Outlier Diagnostic in Geodetic Intersection

2014 ◽  
Vol 10 (2) ◽  
pp. 61-65
Author(s):  
Jana Ižvoltová ◽  
Vladimír Koťka
Keyword(s):  
Hypothesis Testing ◽  
Outlier Detection ◽  
Detection Method ◽  
Extreme Values ◽  
Critical Value ◽  
Statistical Hypothesis ◽  
Statistical Hypothesis Testing ◽  
Intersection Method

Abstract Residuals are differences between observed and predicted variables. This paper describes outlier detection method with using studentized internal and external residuals, which was applied to find extreme values in dataset that comes from the planar intersection method. The detected outlier is analysed by the statistical hypothesis testing, where critical value is defined as a quantil of Studentized distribution.

A zero-velocity detection method with transformation on generalized likelihood ratio statistical curve

Measurement ◽  
2018 ◽  
Vol 127 ◽  
pp. 463-471 ◽  
Author(s):  
Xixiang Liu ◽  
Songbing Wang ◽  
Tongwei Zhang ◽  
Rong Huang ◽  
Qiming Wang
Keyword(s):  
Likelihood Ratio ◽  
Detection Method ◽  
Generalized Likelihood Ratio ◽  
Zero Velocity

scholarly journals Unsupervised Change Detection Driven by Floating References: A Pattern Analysis Approach

2021 ◽  
Author(s):  
RG Negri ◽  
Alejandro Frery
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Pattern Analysis ◽  
Detection Method ◽  
Simulated Data ◽  
Detection Methods ◽  
Practical Significance ◽  
Significance Level ◽  
Natural Factors ◽  
Better Than

© 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature. The Earth’s environment is continually changing due to both human and natural factors. Timely identification of the location and kind of change is of paramount importance in several areas of application. Because of that, remote sensing change detection is a topic of great interest. The development of precise change detection methods is a constant challenge. This study introduces a novel unsupervised change detection method based on data clustering and optimization. The proposal is less dependent on radiometric normalization than classical approaches. We carried experiments with remote sensing images and simulated datasets to compare the proposed method with other unsupervised well-known techniques. At its best, the proposal improves by 50% the accuracy concerning the second best technique. Such improvement is most noticeable with uncalibrated data. Experiments with simulated data reveal that the proposal is better than all other compared methods at any practical significance level. The results show the potential of the proposed method.

scholarly journals Unsupervised Change Detection Driven by Floating References: A Pattern Analysis Approach

2021 ◽  
Author(s):  
RG Negri ◽  
Alejandro Frery
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Pattern Analysis ◽  
Detection Method ◽  
Simulated Data ◽  
Detection Methods ◽  
Practical Significance ◽  
Significance Level ◽  
Natural Factors ◽  
Better Than

© 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature. The Earth’s environment is continually changing due to both human and natural factors. Timely identification of the location and kind of change is of paramount importance in several areas of application. Because of that, remote sensing change detection is a topic of great interest. The development of precise change detection methods is a constant challenge. This study introduces a novel unsupervised change detection method based on data clustering and optimization. The proposal is less dependent on radiometric normalization than classical approaches. We carried experiments with remote sensing images and simulated datasets to compare the proposed method with other unsupervised well-known techniques. At its best, the proposal improves by 50% the accuracy concerning the second best technique. Such improvement is most noticeable with uncalibrated data. Experiments with simulated data reveal that the proposal is better than all other compared methods at any practical significance level. The results show the potential of the proposed method.

scholarly journals Possibility Measure of Accepting Statistical Hypothesis

Mathematics ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 551
Author(s):  
Jung-Lin Hung ◽  
Cheng-Che Chen ◽  
Chun-Mei Lai
Keyword(s):  
Hypothesis Testing ◽  
Null Hypothesis ◽  
Alternative Hypothesis ◽  
Statistical Testing ◽  
Statistical Hypothesis ◽  
Statistical Hypothesis Testing ◽  
Test Statistic ◽  
Possibility Measure ◽  
Testing Approach ◽  
Hypothesis Testing Approach

Taking advantage of the possibility of fuzzy test statistic falling in the rejection region, a statistical hypothesis testing approach for fuzzy data is proposed in this study. In contrast to classical statistical testing, which yields a binary decision to reject or to accept a null hypothesis, the proposed approach is to determine the possibility of accepting a null hypothesis (or alternative hypothesis). When data are crisp, the proposed approach reduces to the classical hypothesis testing approach.

scholarly journals A Novel Blind Event Detection Method for Wireless Sensor Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Thuc Kieu-Xuan ◽  
Sungsoo Choi ◽  
Insoo Koo
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Likelihood Ratio ◽  
Event Detection ◽  
Detection Method ◽  
False Alarm Probability ◽  
Energy Detection ◽  
Wireless Sensor ◽  
Decision Threshold ◽  
Set Up

Student'st-distribution is utilized to derive a novel method for event detection in wireless sensor networks. Numerical analysis is used to show that under the same conditions, the proposed event detection method is comparable to likelihood ratio-based detection method and that it significantly outperforms energy detection method in terms of detection performance. Moreover, the proposed method does not require perfect knowledge of noise variance to set up a decision threshold in terms of a false alarm probability as the likelihood ratio based detection and the energy detection do.

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