Application for analysis of the multiple coherence function in diagnostic signal separation processes

Diagnosing the condition of the machine during its operation by non-invasive methods is most often reduced to measuring the acceleration of vibrations occurring on the housing, as close as possible to the observed element or changes in sound pressure in the immediate vicinity of the machine. For proper inference about the condition of a given machine element, the registered signals should be undisturbed by signals coming from other components and free from external interference. In the case of simple stationary machines, it is quite simple, but in the case of more complex systems, such as a car, which in addition is in motion, things get complicated In the available literature we find examples of the effectiveness of using ordinary coherence function to separate signals from two independent sources[1,2,3]. This work presents attempt to build an algorithm that uses signals from a multi-point measurement system to analyze multiple coherence functions, which allows to separate signals from various sources. It can then get diagnostic information from the signal thus separated. The effectiveness of the algorithm was tested on a model simulating signal mixing, and then using signal coherence function and knowledge of the transmittance function, the signals were separated.