Mean Shift Clustering-Based Analysis of Nonstationary Vibration Signals for Machinery Diagnostics

Vibration analysis is a powerful tool for condition monitoring of rotating machinery. In the nonstationary case, this analysis often involves denoising and extraction of the time-varying harmonic components buried within the vibration signal. However, the complexity of many contemporary techniques-especially in relation to nonstationary signals-and their dependence on prior knowledge of the system kinematics in order to be effective is an inhibitor to autonomous fault detection and monitoring of nonstationary systems. In this article, a nonparametric, blind spectral preprocessing approach to simultaneously denoise and extract the harmonic content from nonstationary vibration signals is presented. The proposed approach utilizes mean shift clustering in conjunction with the short-time Fourier transform to separate time-varying harmonics from background noise within the frequency spectrum, without the need for a priori knowledge of the system. The technique is fully invertible, allowing the time signals corresponding to the separated time-varying harmonic and residual components to be reconstructed. The performance of the proposed technique is compared against existing preprocessing methods and validated using several industrial data sets: first, using vibration data obtained from a low-speed, nonstationary industrial automated people mover gearbox, next, using vibration data from an aircraft engine containing outer race faults, and finally, using nonstationary vibration data from a wind turbine containing frequent speed fluctuations.