Fault Diagnosis Method Of Rolling Bearing Based On Entropy Selecting The IMF Component

Rolling bearing is the key component as well as one of the quick-wear parts of the rotating machinery. For a long time, the fault diagnosis of the rolling bearing used in rotating machinery is the priority and difficulty in the industry research. The fault diagnosis of the rolling bearing can be realized by analyzing the vibration signals which are produced during the running of the rotating machinery. As most of the rotating machinery is running under variable speed condition, so the vibration signals generated in this process are mostly non-stationary signal. These variable speed signals will become more obviously when the fault is happened on rolling bearing. In addition, there are rich fault characteristic information contained in these variable speed signals. However, the existing signal processing methods cannot effectively complete the fault diagnosis of the rolling bearing under the variable speed condition.In this paper, a fault diagnosis method of the rolling bearing based on entropy selecting IMF component is proposed based on the study of existing signal processing method. Methods of EMD decomposition, Shannon entropy, Fourier transform, Order tracking and Envelope demodulation are organic combined in this method and the processing and analysis of the vibration signals are achieved under the variable speed condition. Finally, the fault diagnosis of the rolling bearing is accomplished by utilizing the analysis results. The specific steps are as follows.Firstly, the collected vibration signals are preprocessed by using EMD decomposition and they are adaptively resolved in a series of intrinsic mode function (IMF). This method plays the role of the filtering by resolving the original vibration signal into different frequency components.Secondly, choose the appropriate IMF component to extract the rotating speed information from the vibration signals and diagnose the fault of rolling bearings respectively. In order to better extract and analyze the fault signal, the Shannon entropy of each IMF component obtained after the EMD decomposition needs to be calculated first, then according to the Shannon entropy, the disorder degree of the signal can be determined. The smaller the Shannon entropy, the more orderly the signal; the greater the Shannon entropy, the more chaotic the signal.When the rolling bearing malfunctions, the periodic impact would occur when the fault surface contacted with other components, which would produce evenly spaced pulse. Thus, the IMF component with minimum Shannon entropy is chosen to extract the fault features of the rolling bearing in this paper. Speed information is generally located in the low frequency signal, it is hard to judge the signal frequency distribution by using the Shannon entropy, so the FFT transform for IMF component is needed in order to know the frequency distribution of each IMF component in this paper. Then, the appropriate component is chosen to extract the speed information from the vibration signals.Finally, the fault features are extracted to conduct the fault diagnosis of the rolling bearing. The uniform angle reampling processing is conducted to the IMF component with minimum entropy by utilizing the extracting speed information and it is converted from the time domain non-stationary signal into angle domain stationary signal. Then the fault type of rolling bearing could be judged by the envelope order spectrum which are got from the envelope demodulation which is done to the angle domain stationary signal.At the end of this paper, the experiments are carried out to verify the above method by using the multifunctional rotor test bench. The experimental results show that this method can realize the fault diagnosis of the rolling bearing accurately and reliably under the variable speed condition.