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ASCS Online Fault Detection and Isolation Based on an Improved MPCA

Chinese Journal of Mechanical Engineering 2014年第5期27卷 1047-1056页

作者： PENG Jianxin LIU Haiou HU Yuhui XI Junqiang CHEN Huiyan Science and Technology on Vehicle Transmission Laboratory Beijing Institute of Technology The State Key Laboratory of the Vehicle Transmission China North Vehicle Research Institute

Multi-way principal component analysis（MPCA）has received considerable attention and been widely used in process monitoring.A traditional MPCA algorithm unfolds multiple batches of historical data into a two-dimensional matrix and cut the matrix along the time axis to form ***,low efficiency of subspaces and difficult fault isolation are the common disadvantages for the principal component *** paper presents a new subspace construction method based on kernel density estimation function that can effectively reduce the storage amount of the subspace *** MPCA model and the knowledge base are built based on the new ***,fault detection and isolation with the squared prediction error（SPE）statistic and the Hotelling（T2）statistic are also realized in process *** a fault occurs,fault isolation based on the SPE statistic is achieved by residual contribution analysis of different *** fault isolation of subspace based on the T2 statistic,the relationship between the statistic indicator and state variables is constructed,and the constraint conditions are presented to check the validity of fault ***,to improve the robustness of fault isolation to unexpected disturbances,the statistic method is adopted to set the relation between single subspace and multiple subspaces to increase the corrective rate of fault *** fault detection and isolation based on the improved MPCA is used to monitor the automatic shift control system（ASCS）to prove the correctness and effectiveness of the *** research proposes a new subspace construction method to reduce the required storage capacity and to prove the robustness of the principal component model,and sets the relationship between the state variables and fault detection indicators for fault isolation.

关键词： multi-way principal component analysis(MPCA) fault detection fault isolation automatic shift control system

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