In the case of fault diagnosis for roller bearings, the conventional diagnosis approaches by using the time interval of energy impacts in time-frequency distribution or the pass-frequencies are based on the assumption that machinery operates under a constant rotational speed. However, when the rotational speed varies in the broader range, the pass-frequencies vary with the change of rotational speed and bearing faults cannot be identified by the interval of impacts. Researches related to automatic diagnosis for rotational machinery in variable operating conditions were quite few. A novel automatic feature extraction method is proposed based on a pseudo-Wigner-Ville distribution (PWVD) and an extraction of symptom parameter (SP). An extraction method for instantaneous feature spectrum is presented using the relative crossing information (RCI) and sequential inference approach, by which the feature spectrum from time-frequency distribution can be automatically, sequentially extracted. The SPs are considered in the frequency domain using the extracted feature spectrum to identify among the conditions of a machine. A method to obtain the synthetic symptom parameter is also proposed by the least squares mapping (LSM) technique for increasing the diagnosis sensitivity of SP. Practical examples of diagnosis for bearings are given in order to verify the effectiveness of the proposed method. The verification results show that the features of bearing faults, such as the outer-race, inner-race and roller element defects have been effectively extracted, and the proposed method can be used for condition diagnosis of a machine under the variable rotational speed.
As the offshore life and production base,the offshore platform plays an important role in offshore oil exploitation.The acoustic emission(AE)technology can be applied to damage detection and early warning of the offshore platform,and then can effectively guarantee the safe operation of the offshore platform,prevent accidents and casualties.The steel jacket offshore platform is currently the most widely used in shallow sea oil field of our country.Considering the complex structure of the steel jacket offshore platform and using AE technology,this paper has carried on research on effects of the pipe diameter size,the welding angle on the AE signal propagation characteristics,and at the same time,influence of the marine environment(seawater temperature,salinity)on the AE testing.These research contents have very important reference value for the application of the AE technology in offshore platform monitoring.
The failure of a drilling pump is always due to the break of the drilling pump valve, which is one of the most important but also the weakest parts of the drilling pump. Over the decades, the degradation of drilling pump valves has been investigated extensively and various failure mechanisms have been proposed. However, no experimental test on the fluid has been successfully performed to support some of these mechanisms. In this paper, tests of the flow within the valve play are carried out to investigate the factors resulting in the failure of the valve. In the tests, particle image velocimetry(PIV) technology is employed to measure the flow field distribution of the valve play in the model. From these tests, the distributions of velocity and vorticity of fluid in various valves with different valve angles and different valve lifts are obtained, from which the features of flow fields are derived and generalized. Subsequently, a general rule of the influence of valve angles and valve lifts on the flow velocity is concluded according to chart analyses of maximal velocities and mean velocities. Finally, an analysis is made on the possibility of valve failure caused by erosion and abrasion in a working valve, with the application of the failure mechanisms of drilling pump valves. PIV measurement improves the study on the failure of the drilling pump valve, and the results show good agreement with previous computational fluid dynamics(CFD) simulations.
