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

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.