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For the fault diagnosis problems of the underwater vehicle sensor systems, the solution is combined by the Principal Component Analysis (PCA) and Self-Organizing Fuzzy Cerebellar Model Articulation Controller (SOFCMAC). The signal prediction model approach based on PCA and SOFCMAC is proposed in this paper. According to the history data, it can predict the signal data in the future time using the...
The traditional principal component analysis (PCA) method divides the variable space into two parts: Principal subspace and Residual subspace by orthogonal decomposition. It has been widely used in fault detection process, but it is difficult to interpret the modes of the fault because of model compound effect, and the ability to distinguish the pattern which is no significant is affected. In industrial...
Fault detection for multi-mode process are becoming a hotspot. By effective mode division and accurate online identification, a multi-mode hybrid data set can be transformed into multiple single-mode data sets, then the traditional PCA-based approach can still be adopt to process monitoring. However, in the treatment of “fast response” multi-mode procedure, the above idea does not seem to apply, which...
A fault detection method based on empirical likelihood is presented to deal with the incipient fault in process and equipment. The problem of incipient fault detection is studied in the view of distribution test by a moving window approach. The original fault detection problem is transformed into distribution test, and a set of empirical likelihood values is computed. Based on the likelihood values,...
As one of the most widely used parts and components of rotating machineries, fault detection of rolling bearing is of great significance. In this paper, a new method named EMD-DPCA is proposed based on Empirical Mode Decomposition (EMD) and Dynamic Principal Component Analysis (DPCA). Firstly, the vibration signals are decomposed by EMD and Intrinsic Mode Functions (IMFs) are achieved. Then DPCA model...
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