Adaptive Multi-Model Fault Diagnosis of Dynamic Systems for Motion Tracking

作     者：Daniels, Annalena Benciolini, Tommaso Wollherr, Dirk Leibold, Marion 

作者机构：Tech Univ Munich Chair Automat Control Engn D-80333 Munich Germany 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2024年第12卷

页      面：197540-197556页

核心收录：

基　　金：Use of AI systems disclosure: grammarly.com  chatgpt.com  and deepl.com were used to improve the grammar and readability of the manuscript 

主　　题：Adaptation models Maximum likelihood estimation Computational modeling Vectors Fault detection Switches Dynamical systems Adaptive systems System identification Robot sensing systems Parameter estimation fault diagnosis multiple model algorithm maximum likelihood estimation interacting multiple model algorithm 

摘      要：For most real-world systems, the exact description of possible faults is unknown, making these faults difficult to detect, and even more difficult to identify. The most promising way is to use multiple hypotheses for faults to find the best fitting fault model by comparing system measurements with the predictions of the multi-model algorithm. However, this may lead to the need for infinite hypotheses. We propose a novel multi-model approach that considers a small number of different models with a known macro-structure and unknown parameters, combining system identification with simultaneous fault diagnosis. The unknown parameters in the models are estimated using a maximum likelihood approach. The fitted models are then used in an interacting multiple model algorithm to determine the most likely model that best describes the system behavior at any moment in time. An overfitting problem emerging from short data sequences is discussed, and two solutions are introduced. First, a regularization term in the probability estimation is suggested to penalize frequent parameter changes that signal possible overfitting. Second, an algorithm with a shifted data set is presented. The effectiveness of the algorithms is demonstrated on a motion tracking problem where the different fault hypotheses represent the macro-behavior of a moving object, and the real system switches between different modes. In a comparison, the proposed algorithms are the only ones that reliably identify the defined faults. They can be easily adapted to other fault diagnosis problems.