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Automatic fault detection in gearboxes by dynamic fuzzy data analysis
Journal article   Peer reviewed

Automatic fault detection in gearboxes by dynamic fuzzy data analysis

A. Joentgen, L. Mikenina, R. Weber, A. Zeugner and H.-J. Zimmermann
Fuzzy sets and systems, v 105(1), pp 123-132
1999
DOI: https://doi.org/10.1016/S0165-0114(98)00442-4

Abstract

Automatic fault detection Cluster analysis Dynamic data analysis
The main objective of machine diagnosis is the early recognition of mechanical defects in a machine, which is often referred to as preventive maintenance. This may result in the reduction of faults and higher machine availability. Preventive maintenance can be performed periodically in fixed time intervals, by demand due to machine faults, or continuously, depending on the state of a machine. To minimize maintenance and repair time, state-dependent maintenance of machines is usually applied. It assumes precise and reliable monitoring of machine's states, which is often provided by an expert who can distinguish malfunctions in operation from other changes in machine's states. In Fuzzy-Neuro Systems '98 — Computational Intelligence, pp. 98–105, and Int. J. Fuzzy Sets and Systems 105 (1999) 81–90, we have introduced a clustering method for dynamic objects, i.e., objects which are described by (time) trajectories of features. The aim of this paper is to show exemplarily that this method can be used for early recognition of changes in a machine's state and thus for automatic fault detection. Moreover, the method can be applied to automatic feature selection. A major advantage of this method is that it requires less expert knowledge than traditional approaches.

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Computer Science, Theory & Methods
Mathematics, Applied
Statistics & Probability
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