Abstract
We present techniques for residual-generation as the basis for statistical hypothesis testing for fault detection in stochastic systems. If a system model is not available, we perform system identification using an ARMAX or NARMAX structure. We propose that a Kalman filter is used to estimate the ARMAX model, and a feedforward neural network is used to estimate the NARMAX model. The test of hypothesis can be done directly on the residual if the system is single-output. For multi-output systems, we show how a neuron can be used to implement a Chi-squared test.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
