Time-frequency manifold for gear fault signature analysis

Abstract

Time-frequency analysis can reveal intrinsic feature of representing non-stationary signal for machine health diagnosis. This paper proposes a novel time-frequency feature, called timefrequency manifold, by addressing manifold learning on the timefrequency distributions (TFDs). The new feature is produced from an analyzed signal in three steps. First, a high-dimensional phase space is reconstructed as a preparation for manifold analysis. Second, the TFDs are calculated to represent the nonstationary information in the reconstructed space. Third, the manifold learning is conducted on the TFDs to produce the nonlinear manifold structure. The time-frequency manifold combines non-stationary information and nonlinear information, and may thus provide better representation of machine health pattern. The new feature is exactly suited for machine health diagnosis, which is verified by an application to gear fault signature analysis.

A New De-Noising Method Based on Enhanced Time-Frequency Manifold and Kurtosis-Wavelet Dictionary for Rolling Bearing Fault Vibration Signal

“…In recent years, many scholars began to combine time-frequency analysis with manifold learning, which not only analyzed the time-frequency manifold structure of signals but also achieved a good effect of noise suppression [23][24][25]. He et al [26] analyzed the nonlinear timefrequency manifold structure of defective signals, and the extracted signal features were suitable for the diagnosis of mechanical faults. Li et al [27] extracted the time-frequency manifold of RF signals and successfully separated and classified the signals.…”