Application of Cluster Analysis of Acoustic Emission Signals to Measure Wear in Sliding Friction Units

A new approach to measuring wear in sliding friction units based on the use of cluster analysis of acoustic emission signals accompanying the operation of the triboassembly is proposed and experimentally investigated. A special feature of the proposed approach is that the contribution of each mechanism of damage to the friction unit, identified in a multidimensional field of acoustic emission features with a time reference for their manifestation, is taken into account in the total wear, which makes it possible to track the dynamics of wear of the friction unit directly during observation (measurement). The study was conducted by comparing the use of two promising threshold-free clustering algorithms for acoustic data relative to the standard amplitude threshold method for detecting and identifying acoustic emission events, which allowed us to gain an idea of the advantages of the proposed approach relative to the current level of development of this type of technology. The results showed that the use of multiparametric cluster analysis to detect the type of damage (wear) mechanism allows for a minimum 45 % increase in the probability of detecting friction surfaces setting compared to the amplitude threshold method, while maintaining the probability of detecting a bully at the same level. It has been found that the proposed approach makes it possible to estimate wear without stopping the friction unit with an average discrepancy in the wear assessment between alternative methods of measuring wear and the proposed one at an average level of 16 % throughout the measurements. The results obtained allow us to consider the proposed approach as a promising on-line method for acoustic emission diagnostics and monitoring of the technical condition of sliding friction catches.

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