Hankel Matrix-Based Condition Monitoring of Rolling Element Bearings: An Enhanced Framework for Time-Series Analysis

Abstract

Robust bearing fault detection is significant to reduce the machinery down-time and to prevent catastrophic failure. Many algorithms are proposed for the faults feature extraction, but it remains challenging to monitors the condition of the mechanical systems from the overwhelming interference noise contained signal in a short response time. To address this problem, as an extension of our recent work, this article introduces an enhanced framework using acquired time-series signals. Specifically, an improved Hankel matrix-based method is proposed for the identification of the state from the sampled vibration signal for each spindle turn, where matrix similarity is employed for the mechanical operation state monitoring. The experimental results indicate that the proposed method performs considerably well in fault identification (100% identification accuracy in three tests) even with a few data samples and phase shift. This work, therefore, would have more hopeful prospects in a variety of engineering fault detection applications.