Maintenance decision-making method for diesel engine fuel oil system through cost analysis in combination with Bayesian network model

Abstract

The purpose of the invention is to provide a maintenance decision-making method for a diesel engine fuel oil system through cost analysis in combination with a Bayesian network model. The method comprises the steps of: firstly, establishing the Bayesian network model of the diesel engine fuel oil system, and carrying out fault diagnosis on the fuel oil system based on the model to obtain an occurrence probability of each fault; secondly, carrying out non-dimensional processing on factors which influence a maintenance operation cost of the fuel oil system by use of a standardization formula; thirdly, fusing a plurality of influence factors in maintenance operations by use of an RBF (Radial Basis Function) neural network, and evaluating a corresponding maintenance cost; and finally, comprehensively evaluating a fault occurrence probability and a maintenance cost through a multiplication formula, and ordering the maintenance operations according to a product decreasing rule to obtain an optimal maintenance strategy of the fuel oil system. According to the method, through the cost analysis in combination with the Bayesian network model, the fault probability and the maintenance cost are comprehensively evaluated, and a decision making is carried out on a maintenance strategy of the fuel oil system so that a decision-making result has reference value.

Description

technical field [0001] The invention relates to an engine failure detection and maintenance method. Background technique [0002] The health status of the fuel system directly determines the performance of the diesel engine. Accurate diagnosis and effective maintenance of the health status of the fuel system are important to ensure the safe operation of diesel engines. However, the structure of the fuel system is complex, and the structure and function of each component are highly correlated, so there is a strong coupling between faults and external symptoms. For example, the failure of fuel injection timing in the fuel system may lead to a decrease in the maximum fuel injection pressure, and at the same time cause multiple external symptoms such as a shortened fuel injection duration; The failure of the oil valve, the cavitation of the plunger coupling, and the sticking (upper card) of the needle valve of the injector are the result of the joint action of the faults. The...

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Problems solved by technology

Its shortcoming is that the failure phenomena contained in the fuel system Bayesian network model established by this method are all engine global indicators, and the changes of these indicators will also be affected by the health of systems other than the fuel system (such as intake and exhaust systems). Therefore, these indicators cannot accurately describe the health status of the fuel system; in addition, this method can only diagnose the faults of the fuel system, but cannot make decisions on the maintenance operations of the fuel system, so it cannot effectively guide the staff on the maintenance of the fuel system. Condition-based maintenance

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