Machine Fault Detection Based on Filter Bank Similarity Features Using Acoustic and Vibration Analysis

Abstract

Vibration and acoustic analysis actively support the nondestructive and noninvasive fault diagnostics of rotating machines at early stages. Nonetheless, the acoustic signal is less used because of its vulnerability to external interferences, hindering an efficient and robust analysis for condition monitoring (CM). This paper presents a novel methodology to characterize different failure signatures from rotating machines using either acoustic or vibration signals. Firstly, the signal is decomposed into several narrow-band spectral components applying different filter bank methods such as empirical mode decomposition, wavelet packet transform, and Fourier-based filtering. Secondly, a feature set is built using a proposed similarity measure termed cumulative spectral density index and used to estimate the mutual statistical dependence between each bandwidth-limited component and the raw signal. Finally, a classification scheme is carried out to distinguish the different types of faults. The methodology is tested in two laboratory experiments, including turbine blade degradation and rolling element bearing faults. The robustness of our approach is validated contaminating the signal with several levels of additive white Gaussian noise, obtaining high-performance outcomes that make the usage of vibration, acoustic, and vibroacoustic measurements in different applications comparable. As a result, the proposed fault detection based on filter bank similarity features is a promising methodology to implement in CM of rotating machinery, even using measurements with low signal-to-noise ratio.

A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery

“…Georgoulas et al [150] Symbolic Aggregate approximation + KNN Gao et al [151] Stransform + morphological pattern spectrum + KNN Rajeswari et al [152] EEMD + hybrid binary bat + KNN Geramifard et al [153] Hidden Markov model + KNN Holguín-Londoño [154] Filter bank + KNN…”

“…The parameters of this model were used as input of KNN for fault detection and diagnosis in synchronous motors. Holguín-Londoño [154] proposed filter bank methods to decompose bandwidth-limited signals into a set of narrow-band components. The similarity between the input signal and each extracted narrow-band component was used as fault features and KNN was used to identify different types of faults.…”

Evaluation of Aircraft Auxiliary Power Unit Near-Field Acoustics for Condition Monitoring

“…From the perspective of condition monitoring, microphones have been shown to detect faults in bearings, gears, and rotors using statistical features [8][9][10], frequency transform [11][12][13][14], Bicoherence analysis [11,12,14], spectral subbands [15] and symmetric dot patterns [16]. The proposed methodologies have been shown to be useful on individual components placed in a controlled environment to minimize acoustic reflections.…”

Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study

“…In Holguı´n-London˜o et al 245 vibration and AE analysis methodology based on filter bank similarity features is proposed. Moreover, as a feature extraction stage, empirical mode decomposition (EMD), wavelet packet transform (WPT) and Fourier-based filtering methods are applied to decompose the signals into several narrowband spectral components.…”