The invention provides a mechanical wearing part performance assessment and prediction method based on based on EMD (empirical mode
decomposition)-SVD (
singular value decomposition) and an MTS (Mahalanobis-Taguchi
system), and belongs to the technical field of mechanical wearing part fault diagnosis. The method comprises: first of all, performing
noise reduction
processing on acquired signals of a monitored object, then performing EMD on the signals, selecting effective IMF (intrinsic mode function) components and residual functions to form an initial matrix, performing SVD on the initial matrix, and performing normalization
processing on obtained characteristic values to obtain characteristic vectors; then using an MTS method to calculate an MD (
Mahalanobis Distance), and using a Taguchi method to perform optimization and reduction on the characteristic vectors; and converting the MD into a
confidence value, performing assessment on the performance of mechanical wearing parts through tracking the trend of the
confidence value, and performing prediction on a fault through a correlation module or a matching matrix between the
confidence value and conditions of the monitored object. The method provided by the invention avoids the problem of easily occurring errors when a conventional method is used for
processing non-linear non-stationary signals, and reduces fault generation probability, thereby being suitable for industrial real-time monitoring.