Fault Diagnosis Of Bearings Based On Time-delayed Correlation And Demodulation B-Spline Neural Networks

As one of the most common parts of various rolling mechanical equipments, rolling bearing is very vulnerable. Therefore, great importance has been attached to the theories,methods and applications of failure diagnosis of rolling bearing.Vibration analysis is a very important means for condition monitoring and fault diagnosis. This paper aims at the research on the methods of signal processing and pattern recognition. Therefore, firstly, a experimental platform was set up for the failure diagnosis of rolling bearing, on which we have done a lot of experiments; Then the vibration signals on the condition of normal rolling bearing,rolling bearing with failure on the outer circle and rolling bearing with failure on the inner circle were collected. After Analyzing on the vibration signal based on analyzing methods of Time-domain,Cepstrum,FFT,Time-delayed correlation demodulation, we got the spectrum through which the type of the failure of rolling bearing was found. By constructing BP networks and B-spline neural networks, dealing with the character of the vibration signal through Unification and putting them into two different networks to recognize, we could finally recognize the real type of the failure of rolling bearing. By applying the .NET platform, a condition monitoring software designing for rolling bearing was completed to fulfill the task of Human-computer interactive more effectively.The time delayed correlation demodulation is established in order to suppress the noise and demodulating the signal. The auto-correlation functions of vibration signals measured on bearing cases are first computed, which will reduce the noise greatly, but not change the modulation signature of the signals. Then the auto-correlation functions are delayed for some time lags in order to decrease the affection of noise before demodulated by Hilbert Transform. The effectiveness of this method id confirmed by simulated data and experimental data. Moreover, the faults on the outer ring, inner ring and rolling element can be recognized by the time delayed correlation demodulation. Experimental vibration signals measured from the rolling element bearings verify that time-delayed correlation demodulation is better than conventional methods, such as spectrum analysis and envelope analysis. In this paper the time-delayed size of the time-delayed correlation demodulation is investigated. Different time-delayed sizes have been analyzed, and their results are almost same. Therefore, the time-delayed size can be selected freely in one revolution.The effective character of the vibration signal was got out, it could be used for the input of neural networks. BP neural networks and B-spline neural networks is mainly applied for recognition. Although the result of the recognition of BP networks is comparatively good, its backwards seems to be evident, mainly due to its "BLACK BOX" character and slow convergence, while the inner result and interconnection of B-spline neural networks is transparent and the structure of the networks can be regulated conveniently according to the practical needs when training the networks, meanwhile the local study and the character of storage of B-spline neural networks make the fast convergence, which can be useful for the On-line monitoring. By comparing the two different networks we found that the training speed and the recognition rate of B-spline neural networks are superior to BP neural networks.Finally, by applying .NET platform, designing the software of the condition monitoring system of rolling bearing, using the C# and Matlab to realize the interface of Human-computer interactive, the whole system is becoming much more intelligent and operating the system is becoming much easier.