NDLI: ASCS online fault detection and isolation based on an improved MPCA

Content Provider	Springer Nature Link
Author	Peng, Jianxin Liu, Haiou Hu, Yuhui Xi, Junqiang Chen, Huiyan
Copyright Year	2014
Abstract	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 subspaces. However, low efficiency of subspaces and difficult fault isolation are the common disadvantages for the principal component model. This paper presents a new subspace construction method based on kernel density estimation function that can effectively reduce the storage amount of the subspace information. The MPCA model and the knowledge base are built based on the new subspace. Then, fault detection and isolation with the squared prediction error (SPE) statistic and the Hotelling (T 2) statistic are also realized in process monitoring. When a fault occurs, fault isolation based on the SPE statistic is achieved by residual contribution analysis of different variables. For fault isolation of subspace based on the T 2 statistic, the relationship between the statistic indicator and state variables is constructed, and the constraint conditions are presented to check the validity of fault isolation. Then, 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 isolation. Finally 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 algorithm. 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.
Starting Page	1047
Ending Page	1056
Page Count	10
File Format	PDF
ISSN	10009345
Journal	Chinese Journal of Mechanical Engineering
Volume Number	27
Issue Number	5
e-ISSN	21928258
Language	English
Publisher	Chinese Mechanical Engineering Society
Publisher Date	2014-07-25
Publisher Place	Beijing
Access Restriction	Subscribed
Subject Keyword	multi-way principal component analysis (MPCA) fault detection fault isolation automatic shift control system Mechanical Engineering Theoretical and Applied Mechanics Manufacturing, Machines, Tools Engineering Thermodynamics, Heat and Mass Transfer Power Electronics, Electrical Machines and Networks Electronics and Microelectronics, Instrumentation
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Mechanical Engineering

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