Rotational Machine Health Monitoring and Fault Detection Using EMD-Based Acoustic Emission Feature Quantification

Acoustic emission (AE)-signal-based techniques have recently been attracting researchers' attention to rotational machine health monitoring and diagnostics due to the advantages of the AE signals over the extensively used vibration signals. Unlike vibration-based methods, the AE-based techniques are in their infant stage of development. From the perspective of machine health monitoring and fault detection, developing an AE-based methodology is important. In this paper, a methodology for rotational machine health monitoring and fault detection using empirical mode decomposition (EMD)-based AE feature quantification is presented. The methodology incorporates a threshold-based denoising technique into EMD to increase the signal-to-noise ratio of the AE bursts. Multiple features are extracted from the denoised signals and then fused into a single compressed AE feature. The compressed AE features are then used for fault detection based on a statistical method. A gear fault detection case study is conducted on a notional split-torque gearbox using AE signals to demonstrate the effectiveness of the methodology. A fault detection performance comparison using the compressed AE features with the existing EMD-based AE features reported in the literature is also conducted.

I. Introduction

Acoustic Emission (AE) can be defined as a transient elastic wave generated by the rapid release of energy within a material, for example, the formation of a crack inside the material [1]. AE techniques have been extensively used in the field of nondestructive testing (NDT) for detecting and locating the flaws in structures in many industrial applications, such as pipelines [2], aircraft [3], wind mill [4], and structure health monitoring [5]. Recently, AE-based techniques have began to attract researchers' attention to machine health monitoring and fault diagnostics [6]–[10]. As reported in [11], in comparison with the most widely used vibration signals, AE signals have the following advantages: 1) insensitive to structural resonance and unaffected by typical mechanical background noises; 2) more sensitive to activities of faults; 3) provide good trending parameters; and 4) localization of measurements to the machine being monitored, i.e., AE signals are sensitive to the location of the faults. These advantages make the AE-based techniques potentially more competitive than the vibration-based techniques for rotational machinery health monitoring and fault detection.