Diesel engine lubricating system fault diagnosis method based on Bayes network

Abstract

The invention relates to a diesel engine lubricating system fault diagnosis method based on the Bayes network. According to the method, fault types and external symptoms of a lubricating system are abstracted into fault layer nodes and symptom layer nodes, and a diesel engine lubricating system Bayes network model is established; performance parameters of the diesel engine lubricating system are detected by utilizing a data acquisition system, a linear proportion transformation method is employed to carry out classification processing on the performance parameters, and the actual work state information of the lubricating system is acquired; a Hugin combined tree algorithm is employed to convert a corrected lubricating system Bayes network model into a combined tree. Before inference diagnosis, on the basis of the actual work state of the lubricating system, through resetting the prior probability of the fault layer nodes, adaptability correction on the Bayes network model is carried out, so the actual work state of the lubricating system can be accurately described through the model, nondeterminacy of model inference is reduced, and thereby fault diagnosis accuracy is improved.

Description

technical field [0001] The invention relates to a fault diagnosis method for a lubricating system of a diesel engine based on a Bayesian network. Background technique [0002] Diesel engines play an important role in various fields of the national economy. However, the structure of the diesel engine is complicated, and many components work under the harsh conditions of high temperature, high pressure and high load, which makes the system failure rate higher and the maintenance cost is very high. Statistics show that among the various operating expenses of diesel engines, the expenditure on maintenance reaches 15%-30%. Another statistics show that when performing equipment management and maintenance, the time spent in determining faults accounts for 70%-90% of the total time. It can be seen that the low-efficiency diesel engine fault diagnosis method wastes a lot of manpower and material resources, and brings great inconvenience to industrial production. [0003] Diesel en...

AI Technical Summary

Problems solved by technology

Its shortcoming is that the Bayesian network model built by this method has a fixed form, and cannot be adaptively adjusted according to the dynamic changes of the lubrication system, and cannot accurately describe the actual state of the lubrication system. low; and this method is a kind of static reasoning for the fault diagnosis of the lubrication system, and its process is not based on the actual operation information of the diesel engine lubrication system, so it cannot truly realize the diagnosis of the diesel engine lubrication system fault, and it is difficult to guide the staff to carry out targeted maintenance

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