Abstract
The complex nature of rubbing faults makes it difficult to use traditional signal analysis methods for feature extraction. Various time-frequency analysis approaches based on signal decomposition, such as empirical mode decomposition (EMD) and ensemble EMD (EEMD), have been widely utilized recently to analyze rub-impact faults. However, traditional EMD suffers from “mode-mixing”, and in both EMD and EEMD the relevance of the extracted components to rubbing processes must be determined. In this paper, we introduce a new informative intrinsic mode function (IMF) selection method for EEMD and a hybrid feature model for diagnosing rub-impact faults of various intensities. Our method uses a novel selection procedure that combines the degree-of-presence ratio of rub impact and a Kullback–Leibler divergence-based similarity measure into an IMF quality metric with adaptive threshold-based selection to pick the meaningful signal-dominant modes. Signals reconstructed using the selected IMFs contained explicit information about the rubbing faults and are used for hybrid feature extraction. Experimental results demonstrated that the proposed approach effectively defines meaningful IMFs for rubbing processes, and the presented hybrid feature model allows for the classification of rub-impact faults of various intensities with good accuracy.
Highlights
Rotating machines, such as turbines, are widely used for power generation and usually operate under severe operating conditions characterized by high temperatures and high rotational speeds.The goal of the turbine design process is to maintain a small clearance between the rotor blades and the stator to increase torque and reduce air reluctance
This paper presents a new informative intrinsic mode function (IMF) selection procedure that can be used to select the modes obtained by ensemble EMD (EEMD) in rubbing fault analysis
Nc is the number of bladed rotor intensity classes simulated in this study, Ns is the number of instances for each condition, and N f is the number of extracted features
Summary
Rotating machines, such as turbines, are widely used for power generation and usually operate under severe operating conditions characterized by high temperatures and high rotational speeds.The goal of the turbine design process is to maintain a small clearance between the rotor blades and the stator to increase torque and reduce air reluctance. If faults are not detected at an early stage, rubbing can cause excessive damage to the rotating machine, significantly increasing the maintenance cost. Rubbing faults are recognized as highly complex nonlinear and nonstationary faults [2,3], which cause a large number of transients to appear in the signal. This makes it difficult to utilize traditional signal-processing techniques for feature extraction, such as time-domain and frequency-domain fast Fourier transform (FFT) analysis, which cannot efficiently detect transient phenomena in a Sensors 2018, 18, 2040; doi:10.3390/s18072040 www.mdpi.com/journal/sensors
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