Active Fault Diagnosis Based on Stochastic Tests

Active Fault Diagnosis Based on Stochastic TestsThe focus of this paper is on stochastic change detection applied in connection with active fault diagnosis (AFD). An auxiliary input signal is applied in AFD. This signal injection in the system will in general allow us to obtain a fast change detection/isolation by considering the output or an error output from the system. The classical cumulative sum (CUSUM) test will be modified with respect to the AFD approach applied. The CUSUM method will be altered such that it will be able to detect a change in the signature from the auxiliary input signal in an (error) output signal. It will be shown how it is possible to apply both the gain and the phase change of the output signal in CUSUM tests. The method is demonstrated using an example.

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Stochastic change detection based on an active fault diagnosis approach

2007 46th IEEE Conference on Decision and Control ◽

10.1109/cdc.2007.4434429 ◽

2007 ◽

Cited By ~ 1

Author(s):

Niels Kjolstad Poulsen ◽

Henrik Niemann

Keyword(s):

Fault Diagnosis ◽

Change Detection ◽

Active Fault ◽

Diagnosis Approach

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RAPPROCHEMENT BETWEEN ACTIVE FAULT DIAGNOSIS AND CHANGE DETECTION IN ARMAX SYSTEMS

IFAC Proceedings Volumes ◽

10.3182/20060329-3-au-2901.00075 ◽

2006 ◽

Vol 39 (1) ◽

pp. 493-498

Author(s):

Niels Kjølstad Poulsen ◽

Henrik Niemann

Keyword(s):

Fault Diagnosis ◽

Change Detection ◽

Active Fault

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On-line Auxiliary Input Signal Design for Active Fault Detection and Isolation Based on Set-membership and Moving Window Techniques

International Journal of Control Automation and Systems ◽

10.1007/s12555-019-0182-6 ◽

2019 ◽

Vol 17 (11) ◽

pp. 2796-2806 ◽

Cited By ~ 1

Author(s):

Jing Wang ◽

Junde Wang ◽

Meng Zhou

Keyword(s):

Fault Detection ◽

Input Signal ◽

Active Fault ◽

Fault Detection And Isolation ◽

Moving Window ◽

Signal Design ◽

Set Membership ◽

On Line ◽

Active Fault Detection ◽

Auxiliary Input

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Detecting Change between Urban Road Environments along a Route Based on Static Road Object Occurrences

Applied Sciences ◽

10.3390/app11083666 ◽

2021 ◽

Vol 11 (8) ◽

pp. 3666

Author(s):

Zoltán Fazekas ◽

László Gerencsér ◽

Péter Gáspár

Keyword(s):

Change Detection ◽

Traffic Safety ◽

Recognition System ◽

Driver Assistance Systems ◽

Traffic Sign ◽

Traffic Signs ◽

Safety Risk ◽

Urban Road ◽

Environment Type ◽

Cusum Method

For over a decade, urban road environment detection has been a target of intensive research. The topic is relevant for the design and implementation of advanced driver assistance systems. Typically, embedded systems are deployed in these for the operation. The environments can be categorized into road environment-types. Abrupt transitions between these pose a traffic safety risk. Road environment-type transitions along a route manifest themselves also in changes in the distribution of traffic signs and other road objects. Can the placement and the detection of traffic signs be modelled jointly with an easy-to-handle stochastic point process, e.g., an inhomogeneous marked Poisson process? Does this model lend itself for real-time application, e.g., via analysis of a log generated by a traffic sign detection and recognition system? How can the chosen change detector help in mitigating the traffic safety risk? A change detection method frequently used for Poisson processes is the cumulative sum (CUSUM) method. Herein, this method is tailored to the specific stochastic model and tested on realistic logs. The use of several change detectors is also considered. Results indicate that a traffic sign-based road environment-type change detection is feasible, though it is not suitable for an immediate intervention.

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Design and Application of a Low Frequency VCO

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.668-669.808 ◽

2014 ◽

Vol 668-669 ◽

pp. 808-811

Author(s):

Hui Min Zhang ◽

Qing Ping Wu ◽

Zheng Yuan Zhou ◽

Xun Wang

Keyword(s):

Experimental Test ◽

Input Signal ◽

Operational Amplifier ◽

Output Signal ◽

Analog Circuit ◽

Low Frequency ◽

Voltage Controlled Oscillator ◽

Frequency Range ◽

Innovative Design ◽

Design Cost

The low frequency voltage controlled oscillator (VCO) is designed using integrated operational amplifier. The frequency of the output signal of VCO changes with the magnitude of the input signal voltage, and show a linear relationship within a certain range through the experimental test. Experiments show that, under the input of certain amplitude and frequency range of the square wave, triangle wave, saw-tooth wave, the output waveform of VCO respectively is ambulance, fire siren and other kinds of ambulance siren Signal. This innovative design’ cost is low, realized by analog circuit. It can be used in the practice of teaching case, electronic production or development of sound panels.

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Active Fault Diagnosis Based on Consistencies to a Class of Hybrid Systems by Using Genetic Algorithms and Markov Decision Process

2020 IEEE ANDESCON ◽

10.1109/andescon50619.2020.9272124 ◽

2020 ◽

Author(s):

Ivan Escandon ◽

Mariela Cerrada

Keyword(s):

Genetic Algorithms ◽

Fault Diagnosis ◽

Hybrid Systems ◽

Markov Decision Process ◽

Decision Process ◽

Active Fault ◽

Markov Decision

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Adaptive method to predict and track unknown system behaviors using RLS and LMS algorithms

Facta universitatis - series Electronics and Energetics ◽

10.2298/fuee2101133t ◽

2021 ◽

Vol 34 (1) ◽

pp. 133-140

Author(s):

Teimour Tajdari

Keyword(s):

Input Signal ◽

Output Signal ◽

Adaptive Filters ◽

Adaptive Method ◽

Lms Algorithm ◽

Recursive Least Squares ◽

Least Mean Square ◽

Filter Output ◽

System Output ◽

Unknown System

This study investigates the ability of recursive least squares (RLS) and least mean square (LMS) adaptive filtering algorithms to predict and quickly track unknown systems. Tracking unknown system behavior is important if there are other parallel systems that must follow exactly the same behavior at the same time. The adaptive algorithm can correct the filter coefficients according to changes in unknown system parameters to minimize errors between the filter output and the system output for the same input signal. The RLS and LMS algorithms were designed and then examined separately, giving them a similar input signal that was given to the unknown system. The difference between the system output signal and the adaptive filter output signal showed the performance of each filter when identifying an unknown system. The two adaptive filters were able to track the behavior of the system, but each showed certain advantages over the other. The RLS algorithm had the advantage of faster convergence and fewer steady-state errors than the LMS algorithm, but the LMS algorithm had the advantage of less computational complexity.

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