Fault Detection for A Class of Closed-loop Hypersonic Vehicle System via Hypothesis Test Method

2020 ◽  
Author(s):  
Xunhong Lv ◽  
Yifan Fang ◽  
Zehui Mao ◽  
Bin Jiang ◽  
Ruiyun Qi
Keyword(s):  
Fault Detection ◽  
Closed Loop ◽  
Hypothesis Test ◽  
Hypersonic Vehicle ◽  
Test Method ◽  
Vehicle System

scholarly journals PENGARUH MODEL QUANTUM TEACHING BERBASIS PENDIDIKAN KARAKTER TERHADAP KOMPETENSI PENGETAHUAN IPA SISWA KELAS V SD GUGUS DEWI SARTIKA DENPASAR TIMUR TAHUN AJARAN 2017/2018

2018 ◽  
Vol 1 (2) ◽  
Author(s):  
Ni KomangMega Lestari
Keyword(s):  
Character Education ◽  
Hypothesis Test ◽  
Test Method ◽  
Control Group ◽  
Teaching Model ◽  
Control Group Design ◽  
Significance Level ◽  
Model Base ◽  
5Th Grade ◽  
Academic Year

This research aims to know the effect of quantum teaching model base character education towards science competency knowledge in 5th grade students of Gugus Dewi Sartika East Denpasar academic year 2017/2018. This study was an experiment with quasy experiment design using the Non-Equivalent Control Group Design. The population of this research is all 5th grade of Gugus Dewi Sartika East Denpasar amounting to 328 students. Sample were taken by random sampling technique. The sample of this research is 5th A grade in SDN  10 Kesiman amount 42 students as the experiment group and 5th B grade in SDN 3 Kesiman amount 53 student as the control group. The data were collected using the test method in the form of a standard multiple choice objective test. The data were analyzed using descriptive statistic and inferential in shape gain score from science competency knowledge. The result if data analysis obtained mean of gain skor from experiment group 16X"> =0,34 > 16X"> =0,25 control group. From the hypothesis test using t-test while at 5% significance level and df 83 obtained thitung =3,214 > ttabel=2,000. Based on these result can be concluded that quantum teaching model base character education influence of science competency knowledge of 5th grade students SD Gugus Dewi Sartika East Denpasar academic year 2017/2018.

Design of H_/H∞ fault detection observer for closed-loop nonlinear system with disturbance

2020 ◽  
Vol 40 (4) ◽  
pp. 589-599
Author(s):  
Zhengquan Chen ◽  
Lu Han ◽  
Yandong Hou
Keyword(s):  
Fault Detection ◽  
Closed Loop ◽  
External Disturbance ◽  
Adaptive Threshold ◽  
Linear Matrix ◽  
Content Type ◽  
Non Linear ◽  
Runge Kutta ◽  
Non Linear System ◽  
Residual Generator

Purpose This paper proposes a novel method of fault detection, which is based on H_/H∞ Runge–Kutta observer and an adaptive threshold for a class of closed-loop non-linear systems. The purpose of this paper is to improve the rapidity and accuracy of fault detection. Design/methodology/approach First, the authors design the H_/H∞ Runge–Kutta fault detection observer, which is used as a residual generator to decouple the residual from the input. The H_ performance index metric in the specified frequency domain is used to describe how sensitive the residual to the fault. The H∞ norm is used to describe the residual robustness to the external disturbance of the systems. The residual generator is designed to achieve the best tradeoff between robustness against unknown disturbances but sensitivity to faults, thus realizing the accurate detection of the fault by suppressing the influence of noise and disturbance on the residual. Next, the design of the H_/H∞ fault detection observer is transformed into a convex optimization problem and solved by linear matrix inequality. Then, a new adaptive threshold is designed to improve the accuracy of fault detection. Findings The effectiveness and correctness of the method are tested in simulation experiments. Originality/value This paper presents a novel approach to improve the accuracy and rapidity of fault detection for closed-loop non-linear system with disturbances and noise.

scholarly journals A Hypothesis Test Method for Detecting Multifractal Scaling, Applied to Bitcoin Prices

2020 ◽  
Vol 13 (5) ◽  
pp. 104
Author(s):  
Chuxuan Jiang ◽  
Priya Dev ◽  
Ross A. Maller
Keyword(s):  
Time Series ◽  
Heavy Tails ◽  
Financial Time Series ◽  
Hypothesis Test ◽  
Composite Index ◽  
Test Method ◽  
Scaling Functions ◽  
Financial Time ◽  
Nasdaq Composite Index ◽  
Multifractal Scaling

Multifractal processes reproduce some of the stylised features observed in financial time series, namely heavy tails found in asset returns distributions, and long-memory found in volatility. Multifractal scaling cannot be assumed, it should be established; however, this is not a straightforward task, particularly in the presence of heavy tails. We develop an empirical hypothesis test to identify whether a time series is likely to exhibit multifractal scaling in the presence of heavy tails. The test is constructed by comparing estimated scaling functions of financial time series to simulated scaling functions of both an iid Student t-distributed process and a Brownian Motion in Multifractal Time (BMMT), a multifractal processes constructed in Mandelbrot et al. (1997). Concavity measures of the respective scaling functions are estimated, and it is observed that the concavity measures form different distributions which allow us to construct a hypothesis test. We apply this method to test for multifractal scaling across several financial time series including Bitcoin. We observe that multifractal scaling cannot be ruled out for Bitcoin or the Nasdaq Composite Index, both technology driven assets.

scholarly journals Fault Detection and Exclusion for Tightly Coupled GNSS/INS System Considering Fault in State Prediction

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 590 ◽  
Author(s):  
Shizhuang Wang ◽  
Xingqun Zhan ◽  
Yawei Zhai ◽  
Baoyu Liu
Keyword(s):  
Fault Detection ◽  
Navigation System ◽  
High Efficiency ◽  
Hypothesis Test ◽  
Global Navigation Satellite Systems ◽  
Measurement Unit ◽  
Decision Strategy ◽  
Integration Model ◽  
State Prediction ◽  
Tightly Coupled

To ensure navigation integrity for safety-critical applications, this paper proposes an efficient Fault Detection and Exclusion (FDE) scheme for tightly coupled navigation system of Global Navigation Satellite Systems (GNSS) and Inertial Navigation System (INS). Special emphasis is placed on the potential faults in the Kalman Filter state prediction step (defined as “filter fault”), which could be caused by the undetected faults occurring previously or the Inertial Measurement Unit (IMU) failures. The integration model is derived first to capture the features and impacts of GNSS faults and filter fault. To accommodate various fault conditions, two independent detectors, which are respectively designated for GNSS fault and filter fault, are rigorously established based on hypothesis-test methods. Following a detection event, the newly-designed exclusion function enables (a) identifying and removing the faulty measurements and (b) eliminating the effect of filter fault through filter recovery. Moreover, we also attempt to avoid wrong exclusion events by analyzing the underlying causes and optimizing the decision strategy for GNSS fault exclusion accordingly. The FDE scheme is validated through multiple simulations, where high efficiency and effectiveness have been achieved in various fault scenarios.

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