Abstract
The purpose of this work is to demonstrate how a new acoustic emission (AE) technique can be used to monitor friction surface degradation in a four-ball tribosystem under different types of lubrication. The AE method is based on a novel signal spectral categorization technique, and it was used to identify concurrent degradation processes in bearing steel. The correlation of AE features with the development of specific microstructural features on the contact surfaces has been used to identify the AE "signature" of specific damage mechanisms, and thus to monitor the progression of wear. The proposed approach enables the construction of a chronology of lubricant and/or contacting material degradation during tribological testing with a high degree of confidence. Furthermore, it provides an efficient means for automated wear monitoring and for real-time, non-supervised interpretation of the state of wear in a given tribosystem.
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