Multi-Type Diesel Engines Operating Condition Recognition Method Based on Stacked Auto-Encoder and Feature Transfer Learning

Abstract

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.