Centrifugal Oil Pump Rotor/Bearing Fault Diagnosis Based On Vibration Detection

Centrifugal pump is the key equipment of oil pipeline transportation system to provide power support.Due to harsh working environment and complicated structure of the centrifugal pumpand frequent operation of the transportation system,the sudden failure rate of the centrifugal pump is higher than average.Once the fault of oil pump was not found in time,it may cause malignantshutdown events.The shutdown of the system may cause chained safety accidents,such as pipeline leakage,fire,explosions and so on,which may cause casualties,major economic losses and environmental damage.Therefore,for the safety of the whole system,it is very important to carry out effective research on fault diagnosis of centrifugal oil pump regularly,to detect early faults in time and to avoid malignant shutdown events.On the one hand,the structure and principle of centrifugal oil pump are discussed.Then the forms and mechanism of common failures of pump are analyzed in detail.Moreover,investigation and case study about centrifugal pump failures in the oil station and are done to discover the most frequent failure parts.Then research is focus on these parts.On the other hand,the principle of vibration detetion based fault diagnosis method is studied.The method is focused on fault feature recognition.Therefore,time and frequency domin characteristics of common faults are analyzed and simulated.Then study on the key points of centrifugal pump fault disgnosis which is summarized according to the experience of pump stations to find out the weaknesses of the commonly used method.And in order to improve the method,particularly pay attention to de-noising method,feature extraction and pattern recognition.First,as the basic principle of noise reduction by singular value decomposition has been expounded,the determination of effective order and the structure of reconstruction matrix are researched significantly.After that,the algorithm is applied to noise reduction of vibration signals for rotor and bearing to obtain denoised signals which have high signal-to-noise ratio.Second,a method for feature extraction from denoised signal based on wavelet energy entropy has been presented.Vibration signals of Rotor and bearing obtained under different conditions are analyzed based on time-frequency analysis method.As a result,the feature vectors of denoised vibration signals are obtained.Third,patterns of common faultsof centrifugal pump rotor and bearing have been recognized by using artificial neural network.With obtained feature vectors inputted as the grid,the diagnosis of common faults of centrifugal pump rotor and bear has been realized through the training and learning abilities of back propagation neural network.