Abstract: A diesel engine fault diagnosis method based on SAE(Stacked Auto-Encoder) with SSA(Sparrow Search Algorithm) is developed to improve the fault identification rate. The engine fault is detected in real time by monitoring reconstruction error of the auto-encoder. The abnormal engine condition samples are sent to the auto-encoder and categorized and identified. Setting of hyper parameters in the auto-encoder are usually done according to experience, therefore, more or less subjective. In order to overcome this problem, a sparrow search algorithm is introduced for optimizing the hyper parameters. Tests are performed with the simulation data produced by AVL BOOST. The achieved fault identification rate is as high as 96.71 %, much higher than that with SAE alone or support vector machine.