Combining blind separation and cyclostationary techniques for monitoring distributed wear in gearbox rolling bearings

Abstract

This work seeks to study the potential effectiveness of the Blind Signal Extraction (BSE) as a pre-processing tool for the\ud detection of distributed faults in rolling bearings. In the literature, most of the authors focus their attention on the\ud detection of incipient localized defects. In that case, classical techniques (i.e. envelope analysis) are robust in recognizing\ud the presence of the fault and its characteristic frequency. However, when the fault grows, the classical approach fails, due\ud to the change of the fault signature. De facto, in this case the signal does not contain impulses at the fault characteristic\ud frequency, but more complex components with strong non-stationary contents. Moreover, signals acquired from complex\ud machines often contain contributions from several different components as well as noise; thus the fault signature can\ud be hidden in the complex system vibration. Therefore, pre-processing tools are needed in order to extract the bearing\ud signature, from the raw system vibration. In this paper the authors focalize their attention on the application of the BSE in\ud order to extract the bearing signature from the raw vibration of mechanical systems. The effectiveness and sensitivity of\ud BSE is here exploited on the basis of both simulated and real signals. Among different procedures for the BSE computation,\ud the Reduced-Rank Cyclic Regression algorithm (RRCR) is used. Firstly a simulated signal including the effect of\ud gear meshing as well as a localized fault in bearings is introduced in order to tune the parameters of the RRCR. Next, two\ud different real cases are considered, a bearing test-rig as an example of simple machine and a gearbox test-rig as an\ud example of complex machine. In both examples, the bearings were degreased in order to accelerate the wear process.\ud The BSE is compared with the usual pre-processing technique for the analysis of cyclostationary signals, i.e. the extraction\ud of the residual signal. The fault detection is carried out by the computation of the Integrated Cyclic Modulation\ud Spectrum on the extracted signals. The results indicate that the extracted signals via BSE clearly highlight the distributed\ud fault signature, in particular both the appearance of the faults as well as their development are detected, whilst noise still\ud hides fault grow in the residual signals

Experimental Investigation for Distributed Defects in Ball Bearing Using Vibration Signature Analysis

“…Even when local defect grows, it becomes distributed one, generate more complex signal with strong non-stationary contents. Time and frequency domain methods are used for monitoring the health of bearings; however correlation with the prediction of amplitude of spectral components with the extent of defect is necessary for diagnostic purpose [12].…”

Vibration-Based Bearing Fault Detection and Diagnosis via Image Recognition Technique Under Constant and Variable Speed Conditions

“…For extracting useful features, usually the acquired signals are processed using Fourier transform (FT) or its variants (i.e., fast FT (FFT), discrete FT (DFF)) [10], wavelet transform (WT) or its variants (i.e., continuous WT (CWT), discrete WT (DWT), wavelet packet transform (WPT)) [11,12], envelope analysis [13,14], or statistical methods (e.g., principal component analysis (PCA), linear discriminant analysis (LDA)) [15][16][17][18][19]. Thus, the extracted features could be in time domain, frequency domain, or time-frequency domain, which usually have physical meanings, or purely statistical features, which usually do not have physical meanings [20,21]. However, these features are generally in the form of a vector or scalar, for which they have some descriptions of the waveform in the time domain and some parameters of the spectrum in the frequency domain, or just a statistical vector index.…”

A Methodology Based on Cyclostationary Analysis for Fault Detection of Hydraulic Axial Piston Pumps

“…The objective of BSS is ambitious in the case of mechanical systems because there are many sources and their number is hardly known; a list of difficulties for the application of BSS techniques in the field of mechanical systems can be found in [28]. However, in many applications, the complete knowledge of the sources is not required and the ambition of the BSS can be reduced; instead of extracting the set of independent sources, the contribution of any source could be extracted by introducing the concept known in the literature as blind component separation (BCS) or blind signal extraction (BSE) [29].…”