Chiller Fault Vibration Diagnosis Based On Blind Source Separation

It is very difficult to extract accurete signals when refrigerators are usually installed together because all kinds of complicate signals disturb mutually. Through the blind signal separation technology the useful information can be acquired from the complex data because it does not need the massive samples and priori knowledge of producing and dissemination of signals. In this paper, the mixing models and algorithm are emphatically discussed which are suitable for fault feature extraction of refrigerator on the condition that the ambient noise, the fault source and the prior knowledge are unknown. The multiplex diagnosis parameters and the richer fault information are supplied through the vibration source and the noise source for the study on refrigerate. We can solve the difficult problem of the signal characteristics extrcaction of the refrigerate using the method which can both enhance the diagnosis accuracy and provide the solution of failure diagnosis for many kinds of equipments.In this paper, the improved second-order statistics algorithm is study and compared by the tradition JADE algorithm because it reduces the limiting condition of statistical independence which can realize quick convergence and also separate in the noise energy big situation. When the signal noise ratio is lower than 20dB, the improved algorithm is better. But after the signal noise ratio is higher than 20dB, two algorithms tended to be consistent. Because of the noise influence, blind deconvolution algorithm is improved to eliminate noise elimination are carried on simultaneously. The nonlinear function suitable to blind deconvolution is selected by the similarity factor to maximize the output signal generalized energy. The delay factor should be chosen according to the separating time and the convergence performance. When the delay factor increases, the value of convergence error function reduces and the convergence performance would be strengthened. When the delay factor increases to the certain extent, the convergence performance is no longer enhanced remarkably and the separating time extends.The mixing situation of two breakdown source and the multi-breakdown source are simulated through the test separately using the ICA algorithm, the Bussgang algorithm and improved blind deconvolution algorithm to extract the typical fault signal characteristics. It is indicated that the mixing model and the iterative algorithm will influence the separating results of vibration signals. After the comparison, it is discovered that the improved blind separating algorithm enhances the separation precision to be highest, and the convolution mixing model is suitable to the vibration diagnosis of refrigerator. This is because the different disseminating ways leads to the result that the observation signals on the identical time become the superimposition of the source signals in the different time. Finally, the vibration signals of the kind of screw refrigerator are collected and the breakdown characteristics of attrition, air current hit and meshing are extracted.