Diesel engine fuel oil system fault diagnosis method based on least square support vector machine

Abstract

A diesel engine fuel oil system fault diagnosis method based on a least square support vector machine comprises the steps of: collecting vibration acceleration signals of a diesel engine under conditions of normal work and various kinds of faults; utilizing an inherent time scale decomposition algorithm to decompose the vibration acceleration signals, and generating a plurality of rotation components and residual error signals; calculating typical frequency domain characteristics of first N-order rotation components, and using the typical frequency domain characteristics as fault characteristics; dividing training samples and test samples; utilizing a hybrid algorithm of a difference evolution algorithm and a particle swarm algorithm to optimize a punishment factor and a kernel function parameter of the least square support vector machine, and obtaining an optimal punishment factor and an optimal kernel function parameter; and utilizing the obtained optimal punishment factor and optimal kernel function parameter to train the least square support vector machine for carrying out fault diagnosis. By adopting the method provided by the invention, the operation state of the fault diagnosis can be rapidly and accurately judged, and the method is applicable to online diagnosis of the diesel engine.

Description

technical field [0001] The invention relates to a fault diagnosis method for a fuel system of a diesel engine. In particular, it relates to a fault diagnosis method of diesel engine fuel system based on least square support vector machine. Background technique [0002] As one of the most common power devices, diesel engine plays an important role in people's daily life and production. However, the structure of the diesel engine is complex, the working conditions are harsh, and the probability of failure is high. Therefore, in order to improve the safety and reliability of diesel engines and reduce the economic losses caused by faults, it is necessary to carry out research on fault diagnosis methods for diesel engines. [0003] Pattern recognition is the core of fault diagnosis, and the quality of the algorithm directly determines the accuracy and speed of fault diagnosis. Widely used pattern recognition methods include fault trees, rough sets and neural networks, etc., bu...

AI Technical Summary

Problems solved by technology

The particle swarm optimization algorithm is simple and has few adjustable parameters, but it is easy to fall into a local optimal solution

The differential evolution algorithm can improve the global search ability of the algorithm by setting a relatively reliable mutation strategy, but this mutation strategy will make the algorithm difficult to converge

In short, various algorithms have their own advantages and disadvantages and all have the commonality of iterative optimization.

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