Single Sensor Source Separation for Acoustical Machine Diagnostics
Introduction
Acoustical recordings in the context of machine diagnosis usually consist of a multitude of signal components. In order to analyse the individual components of the ma- chine regarding their condition or even to detect faults it is advantageous to handle the signals separately accord- ing to their origin of generation. To become indepen- dent from recording situations, we propose single sen- sor source separation algorithms as preprocessing step for automatic machine diagnostic algorithms. Because of the underlying purely additive model, one promising method for source separation is the non-negative spectro- gram analysis, e.g. the non-negative matrix factorization (NMF). The basic NMF can be extended e.g. by sepa- rating steady state signals, transient signals, and even harmonic components with time-varying pitch and par- tial amplitudes. With such extensions, the non-negative spectrogram analysis can be easily adopted to a wide range of separation tasks. In this contribution an al- gorithm is proposed to separate a measured signal of an electrical machine, containing the superposition of a steady state motor noise signal and the transient signal of a defect bearing.
Paper: SpGnDiOhVo11.pdf
Slides: Slides.pdf
Experimental Results:
Separation of steady state and transient noise.
| speed | mix | defect bearing | motor noise |
| 500 | x(n) | str | sst |
| 800 | x(n) | str | sst |
| 1000 | x(n) | str | sst |
Separation of noise and harmonic (increasing pitch).
| mix | defect bearing | motor noise |
| x(n) | str | sst |
Matlab Code:
NonNegativeMachineDiagnostics.m
© by Martin Spiertz - 24.03.2011 - spiertz@ient.rwth-aachen.de