Joint Instantaneous Amplitude-Frequency Analysis of Vibration Signals for Vibration-Based Condition Monitoring of Rolling Bearings

TL;DR

This paper introduces a low-complexity, envelope analysis-based method for vibration condition monitoring of rolling bearings, utilizing joint amplitude-frequency representations to improve fault detection accuracy.

Contribution

It proposes three novel envelope representations and six discriminative features that enhance fault diagnosis in bearing vibration signals, with real-time applicability.

Findings

High accuracy in fault detection and diagnosis

Effective energy-frequency variation capture

Moderate computational complexity for real-time use

Abstract

Vibrations of damaged bearings are manifested as modulations in the amplitude of the generated vibration signal, making envelope analysis an effective approach for discriminating between healthy and abnormal vibration patterns. Motivated by this, we introduce a low-complexity method for vibration-based condition monitoring (VBCM) of rolling bearings based on envelope analysis. In the proposed method, the instantaneous amplitude (envelope) and instantaneous frequency of the vibration signal are jointly utilized to facilitate three novel envelope representations: instantaneous amplitude-frequency mapping (IAFM), instantaneous amplitude-frequency correlation (IAFC), and instantaneous energy-frequency distribution (IEFD). Maintaining temporal information, these representations effectively capture energy-frequency variations that are unique to the condition of the bearing, thereby enabling the extraction of discriminative features with high sensitivity to variations in operational conditions. Accordingly, six new highly discriminative features are engineered from these representations, capturing and characterizing their shapes. The experimental results show outstanding performance in detecting and diagnosing various fault types, demonstrating the effectiveness of the proposed method in capturing unique variations in energy and frequency between healthy and faulty bearings. Moreover, the proposed method has moderate computational complexity, meeting the requirements of real-time applications. Further, the Python code of the proposed method is made public to support collaborative research efforts and ensure the reproducibility of the presented work