A fault diagnosis model based on t-SNE-VNWOA-LSSVM was proposed,and the bench test was carried out.The test set up four working conditions:normal working condition,insufficient air supply,early combustion and single cylinder oil cut-off.The cylinder head vibration signals collected under various working conditions were processed by fast Fourier transform(FFT),and 13 time-do-main and frequency-domain features were extracted.t-SNE was used to reduce the dimension of the data and visualize the fault features.The initial parameters δ2 and γ of the classifier(LSSVM)were optimized by the whale optimization algorithm(VNWOA),and its fault identification model was es-tablished.The diagnosis results of genetic algorithm(GA)and particle swarm optimization(PSO)were compared with them.The results show that the accuracy of fault diagnosis model based on t-SNE-VNWOA-LSSVM is as high as 96.57％,and it has good stability and diagnosis speed.
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