Auxiliary signal design in fault detection and diagnosis
The object of this book is to present a systematic method for auxiliary signal design in fault detection and diagnosis. It covers systems that can be represented by linear or linearised multiple-input, multiple-output stochastic models. It is very illustrative since each new concept is demonstrated with simple examples and plots. Some fundamental problems in change detection have been investigated. A basic knowledge of probability theory, statistical inference, matrix and control theory is required. Postgraduates and researchers will find it an interesting reference to fault detection and test signal design. The book can also be used as a tutorial material for final year undergraduates, especially those who work on a project related to test signal designs, fault detection or modeling.
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Sequential Probability Ratio Test
Auxiliary Signals for Improving Fault Detection
Extension To Multiple Hypothesis Testing
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accepted algorithm amplitude constraint auxiliary input auxiliary signal design average sampling number behaviour bodeplots change detection Chapter compute considered Control cost function covariance matrix cumulative sums design scheme detection and diagnosis diagnosis scheme discussed Ei(N example false alarm false alarm rate fault detection fault develops fault occurs FDD scheme flow-rate Hence hypothesis testing identification IEEE Trans increase information increment initial conditions input signal input-output models Kalman filters leakage level sensor level transmitter linear log-likelihood ratios lower bound maximized mixture density model parameters modelling errors models corresponding no-fault noise nonlinear normal operation off-line auxiliary signal on-line design On-line Nl one-stage design Parameter Estimate plant PRBS probability ratio test residual Section sequential probability ratio shows signal is applied SPRT based stock tank system identification tank level technique true two-stage Type fault Type II faults upper threshold values valve parameter variance Wald's SPRT wrong decisions