Abstract

Owing to recent studies on the relationship between the transmission error and the localized gear fault, TE-based differential diagnosis (DD) technology has emerged as a promising approach to identifying the tooth flank spall and the gear fillet crack which have similar signatures but quite different fault mechanisms and prognoses. However, due to complex structural factors, the fault modes can hardly be identified from the calculated overall transmission error (OTE). In light of this, a novel DD framework based on encoder-measured data is proposed in this work. Specifically, the OTE signal calculated from resampled data are filtered by flexible time synchronous average. A reordered singular values decomposition (RSVD) approach guided by the sparse index L2/L1 norm is then developed for fault-dependent residual OTE extraction. Experimental results validate that the faulty events can be accurately identified by the proposed framework without prior information under different speed conditions for planet gear DD.

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