An Unsupervised Method based on Support Vector Machines and Higher-Order Statistics for Mechanical Faults Detection

Authors

  • Danton Ferreira

Abstract

In this paper an unsupervised method to detect mechanical faults using support vector machines and higher-order statistics is proposed. The method extracts compact vector features – based on higher-order statistics – from vibration signals and uses the one-class support vector machine to build a closed region around the data from the health structure. The method was evaluated considering two cases:  fault detection in a cantilever beam and in a three-phase induction motor. In both cases, the vibrations were collected by a 3 axis accelerometer sensor. The acquisition system was controlled by a microcontroller ARDUINO®. After collecting the data, higher-order statistics-based features were extracted. These features were presented to the one-class support vector machine for fault detection. The proposed method was capable of constructing a closed region in a two-dimensional space so that events inside this region are signed as no faults and events outside this region are signed as faults. The method has two important characteristics: (i) it requires only information from the health structures to be designed, and (ii) it operates in a low dimensional space (only two) constructed by higher-order statistics features and, therefore, it requires low computational cost in the operational phase.

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Published

2020-05-02

How to Cite

Ferreira, D. (2020). An Unsupervised Method based on Support Vector Machines and Higher-Order Statistics for Mechanical Faults Detection. IEEE Latin America Transactions, 18(6), 1093–1101. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/1484