Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
Interpreting Kullback-Leibler divergence with the Neyman-Pearson lemma
Journal of Multivariate Analysis - Special issue dedicated to Professor Yasunori Fujikoshi
Brief paper: Disturbance decoupling in fault detection of linear periodic systems
Automatica (Journal of IFAC)
Brief paper: Structural analysis for the sensor location problem in fault detection and isolation
Automatica (Journal of IFAC)
Sensor placement for fault isolation in linear differential-algebraic systems
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Issues of Fault Diagnosis for Dynamic Systems
Issues of Fault Diagnosis for Dynamic Systems
Diagnosability Analysis Based on Component-Supported Analytical Redundancy Relations
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Brief An LMI approach to design robust fault detection filter for uncertain LTI systems
Automatica (Journal of IFAC)
Optimal stochastic fault detection filter
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Brief Robust fault detection in uncertain dynamic systems
Automatica (Journal of IFAC)
Robust residual generation for diagnosis including a reference model for residual behavior
Automatica (Journal of IFAC)
Hi-index | 22.14 |
Analyzing fault diagnosability performance for a given model, before developing a diagnosis algorithm, can be used to answer questions like ''How difficult is it to detect a fault f"i?'' or ''How difficult is it to isolate a fault f"i from a fault f"j?''. The main contributions are the derivation of a measure, distinguishability, and a method for analyzing fault diagnosability performance of discrete-time descriptor models. The method, based on the Kullback-Leibler divergence, utilizes a stochastic characterization of the different fault modes to quantify diagnosability performance. Another contribution is the relation between distinguishability and the fault to noise ratio of residual generators. It is also shown how to design residual generators with maximum fault to noise ratio if the noise is assumed to be i.i.d. Gaussian signals. Finally, the method is applied to a heavy duty diesel engine model to exemplify how to analyze diagnosability performance of non-linear dynamic models.