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

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