It is of great significance to diagnose the fault of diesel engine, which is widely used in many important fields as key power equipment. The accuracy of fault diagnosis can be effectively improved by obtaining the complex and changeable operating conditions, which can result in the change of monitoring signals. This study proposes a variable operating conditions recognition method based on stacked auto-encoder (SAE) and feature transfer learning. In this method, the vibration in the firing angle domain collected from multi-sensor signals is reconstructed. Then a feature set sensitive to working conditions is extracted from the recombinant signals by a well-constructed stack auto-encoder. According to the dataset test, the softmax classifier can effectively get a high recognition accuracy. Considering that the fault may affect the condition identification, the misfire fault that has a great influence on firing angle domain signals is used to test the robustness of the proposed method. Besides, to enable a well-trained test rig with a large amount of data to be effectively applied to another unit that lacks data, the BDA transfer learning method is used to map the operating conditions of two different engines to the same feature space. The results of experiments conducted on two large power marine multi-cylinder diesel engines show that BDA is capable of transferring the sensitive features of operating conditions.

Vibration signals have the characteristics of multi-source strong shock coupling and strong noise interference owing to the complex structure of reciprocating machinery. Therefore, it is difficult to extract, analyze, and diagnose mechanical fault features. To accurately extract sensitive features from the strong noise interference and unsteady monitoring signals of reciprocating machinery, a study on the time-frequency feature extraction method of multi-source shock signals is conducted. Combining the characteristics of reciprocating mechanical vibration signals, a targeted optimization method considering the variational modal decomposition (VMD) mode number and second penalty factor is proposed, which completed the adaptive decomposition of coupled signals. Aiming at the bilateral asymmetric attenuation characteristics of reciprocating mechanical shock signals, a new bilateral adaptive Laplace wavelet (BALW) is established. A search strategy for wavelet local parameters of multi-shock signals is proposed using the harmony search (HS) method. A multi-source shock simulation signal is established, and actual data on the valve fault are obtained through diesel engine fault experiments. The fault recognition rate of the intake and exhaust valve clearance is above 90% and the extraction accuracy of the shock start position is improved by 10°.