Analysis of the classifier fusion efficiency in the diagnostics of the accelerometer

• Application of the selected classifiers as the ensemble to analyze the accelerometer.
• Comparative analysis of multiple classification modules’ efficiency and their applicability to the classifier fusion.
• Analysis of the ensemble accuracy compared to the separate classifiers’ outcomes.
• Analysis of the accelerometer behaviour.

The paper presents the construction and application of multiple classifiers to increase the accuracy of the fault detection module in the diagnostic task. The structure of the ensemble of classifiers is presented and the applied voting mechanisms explained. Methods of storing knowledge in the intelligent diagnostic systems are introduced and their taxonomy provided. Next, the selected algorithms implemented in the fault detection operation are briefly described. Problems with the practical implementation of the proposed solution are considered. The scheme is used to detect faults in the analog part of the MEMS accelerometer. The paper is concluded with the possible prospects for the proposed scheme.