Envelope analysis and data-driven approaches to acoustic feature extraction for predicting the remaining useful life of rotating machinery

Abstract

The ability to predict the Remaining Useful Life (RUL) of Rotating Machines is a highly desirable function of Automated Condition Monitoring (ACM) systems. Typically, vibration signals are acquired through contact with the machine and used for monitoring. In this paper, a novel implementation of the ubiquitous feature extraction approach Envelope Analysis (EA) is applied to acoustic noise signals (< 25kHz) to predict the RUL of a rotating machine. A well known drawback of the EA approach is that the frequency band of interest must be known or pre-estimated. Therefore, this approach is compared to a Data-Driven approach to feature extraction which utilizes an Information Theoretic approach to feature selection that does not require any a-priori information regarding the frequency band of interest. It is shown that the DataDriven approach, with an accuracy of 97.7%, significantly outperforms the EA approach, with an accuracy of 93.7%. This study also shows that the improved performance of the Data-Driven approach is due to new information being uncovered in spectral locations across the entire spectrum from 0 to 25kHz, and not just within one frequency band typically used by the EA approach.

Low Complexity Non-Linear Spectral Features and Wear State Models for Remaining Useful Life Estimation of Bearings

“…Data-driven bearing prognostic systems are constructed using signal processing techniques with real measured sensor acquired signals, to analyse and detect trends providing valuable evidence of system degradation [12,13]. Sensing modalities to acquire bearing degradation signatures that have been widely explored in recent years include vibration signals [1,3,14,15], acoustic emissions [16,17], stator current measurements [18][19][20], thermalimaging [21], and multiple sensor fusion [22,23]. Of these, vibration signals, acquired from mounted accelerometers is often attributed as the most favourable approach for conditionbased monitoring (CbM) in general, due to the non-invasive nature of the measurement data, low cost, robustness and ease of implementation in practice [24].…”

A data-driven framework for remaining useful life estimation

“…This method mainly relies on fault detection and failure prediction beyond the point of the fault. Kavanagh et al [12] develop a two-stage RUL estimation algorithm. First, they use the envelop analysis for feature extraction, then the mutual information for feature subset selection.…”