Fault Diagnosis And Health Management-Analysis Of Power Assembly And Bearing Modules

The power assembly provides power for the diesel engine of locomotive,it is an important part of ensuring diesel engine's normal operation.So,the fault diagnosis of power assembly is important.The bearing also occupies an important position in the locomotive,and it is also prone to fail,especially rolling bearing's structure is more complex and there are more types of faults.With the development of modern methods of intelligent diagnosis,the diagnosis methods of fault diagnosis and health management system(PHM)are more diversified.This paper used the fault tree and neural network to analyze the faults of power assembly and rolling bearings.The fault tree is a graphical method to describe the faults,the causality of fault became more intuitive.This paper analyzed the common faults of power assembly according to the steps and rules of the fault tree analysis,then established the fault tree.The characteristics of the neural network indicate that it is suitable for the pattern recognition of faults.In this thesis,feed forward neural networks wildly applied is used to diagnose the faults of power assembly and bearings.The fault of cylinder liner wear is cited here,the model of network is established,then trained and tested the network,finally the diagnosis is accomplished by BP network.In the meantime,there are some measures to optimize the BP network,then compared these improvement measures.The results proved that using LM optimization algorithm is the best improvement measure,and it can meet the requirements of fault diagnosis;RBF neural network can avoid the inherent defects of the BP network.In this thesis,two methods are used to design the RBF network model,then compared their results.In general,the training speed of RBF network is faster than BP network,and the performance is better,the diagnostic accuracy is higher.Finally,the wavelet packet analysis theory is used to analyze the vibration signal of locomotive rolling bearing's surface damage,then the fault is diagnosed by RBF network.The results proved that the combination of wavelet packet analysis and neural network is very effective in fault diagnosis,and the fault status of bearing can be accurately identified.