Design And Development Of A Remote Fault Monitoring System For Transformer Oil Pumps

Oil pump is the core power equipment of the forced oil circulating cooling system for the locomotive transformer.Transformer pump failures affect the stable operation of the locomotives.Real-time monitoring and fault diagnosis of the transformer oil pumps are the main methods to ensure the normal operation of the locomotive traction transformers.Therefore,the remote fault monitoring for the oil pumps is chosen as the research topic of this thesis.The common faults and fault mechanisms of oil pumps are analyzed,then the displacement between rotor and stator is chosen as the sensitive signal for fault monitoring.Based on the research results from the literatures,a displacement monitoring scheme is proposed,which employs induction coils installed on both ends of a motor stator as the sensors.In the proposed scheme,8 detection coils are used.Through time division multiplexing of the 8 coils,6 differential outputs are generated to detect the displacements in vertically radial direction,horizontally radial direction and axial direction,respectively.Considering the actual needs of oil pump fault monitoring,a knowledge-based fault diagnosis method is proposed,where least squares support vector machine is used as the fault classification algorithm due to its less requirement for samples,faster speed,excellent generation ability and high accuracy.Based on the algorithm research,a remote fault monitoring system for transformer oil pumps is designed.The fault monitoring system composes three parts:on-site monitoring device,cloud data center,client center implemented by APP and WEB.The outputs of the coil pairs are acquired by on-site monitoring device,then the signal processing,such as amplification,digital filtering and root mean square calculation,are conducted.Positioning module is used to get the location information of oil pumps.The displacement and location information of oil pumps are then sent to cloud data center through 3G module by on-site monitoring device.On cloud platform,the database structure is designed,and a communication program and a fault diagnosis program based on least squares support vector machine classification algorithm are designed with modularization idea.The client center includes an APP client and a WEB client.Client is designed to present the real-time operation information and position information of the oil pump,at the same time it realizes the management operation of the oil pumps.The field test was carried out with an actual oil pump.The operation data of the oil pump under normal and faulty conditions in 9 operating conditions are obtained first.Samples from the first 7 operating conditions are used to train the least east squares support vector machine classification model,and samples from all 9 operating conditions are used to test the classification model.The testing results prove that the least squares support vector machine can accurately identify faults of the oil pumps under both trained operating condition and non-trained operating condition with 100%accuracy rate.The test results show that the remote fault monitoring of oil pumps is completed by the fault monitoring system designed in this thesis.The scheduled goals are thus accomplished.