Study On Vibration And Noise Of Fault Diagnosis For Air-conditioning Unit Based On Blind Source Seperation

It is very difficult to extract accurate signals when the air conditioning units 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 the fault feature extraction of air-conditioning unit 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 the refrigeration unit and the cooling tower. We can solve the he difficult problem of the signal characteristics extraction of the air conditioning units using the method which can both enhance the diagnosis accuracy and provide the solution of failure diagnosis for all kinds of equipment group in spacious situation.In this paper, the improved second-order statistics algorithm is compared by the traditional JADE algorithm because the different autocorrelation function or the non-zero time sequence correlation reduces the limiting condition of statistical independence which can realize quick convergence and also the separation in the noise energy big situation. When the signal noise ratio is lower than 20dB, the improved second-order statistics algorithm is better. But after the signal-to-noise ratio is higher than 20dB, two algorithms tended to be consistent. Blind deconvolution algorithm is improved to eliminate noise effect, and the non-surveillance's blind separation process and surveillance 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 sources 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 the large-scale air conditioning units. 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 JZKA31.5 screw unit are collected and the breakdown characteristics of attrition, air current hit and meshing are extracted. The blind extraction processing including whitening pretreatment and diagonalization will lead to some change of the amplitude value of the vibration signal. But if the waveform is available to express the fault feature, the amplitude value proportion does not affect the diagnosis result.The acoustic diagnosis experiments of the blower are designed on the analysis vibration characteristic, the frequency characteristic and the acoustic radiation characteristic, and the acoustics diagnostic model is established based on the nonlinear mixing model. It is discovered in the tests that the rotational speed is bigger, more obvious difference of amplitude value in the low frequency part is. This article takes the cooling tower acoustics diagnosis as an example. Through testing the noise characteristic the priori knowledge is achieved initially. The independent acoustic source signals are extracted from the observation signals based on the nonlinear RBFN separation network to distinguish the major failure types from the cooling tower when the interference noise is taken an acoustic source, and the test results are compared with the separation results based on the blind deconvolution and BP model. In the simulation experiment, it is discovered that the error of estimation using the fourth-order cumulant method is bigger than the second-order cumulant method. When the number of sources increases, higher order cumulant will depress the algorithm performance and improve the computation load. Therefore, the algorithm based on the second-order statistics is better for the nonlinear separation effect of acoustic signals.The blind source separation is studied in the complex big space background environment when the signals of the equipment group are mixed and disturbed mutually. Firstly, the algorithm stability is tested from the trace concept. And then the expected signals are attained by eliminating the unwanted random signals from the interference noise. Finally the most remarkable breakdown characteristics are extracted in turn according to the independent measure relations. The computational process will be simplified greatly by this method. The improved natural gradient algorithm still is satisfied with the orthogonal restraint and did not rely on the studying rate. In this paper, vibration and acoustics diagnosis has been completed in an air conditioning room. It is determined that main fault source was from the compressor through separating the noise spectrum in the air conditioning room based on nonlinear blind separating model when several heating pumps and water pumps are installed intently. The misalignment and rubbing faults has been diagnosed by vibration signal characteristic based on the blind deconvolution algorithm, which realized the vibration and sound diagnosis in the big space for the machine group.