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Diesel engines, as power equipment, are widely used in the fields of the automobile industry, ship industry, and power equipment. Due to wear or faulty adjustment, the valve train clearance abnormal fault is a typical failure of diesel engines, which may result in the performance degradation, even valve fracture and cylinder hit fault. However, the failure mechanism features mainly in the time domain and angular domain, on which the current diagnosis methods are based, are easily affected by working conditions or are hard to extract accurate enough from, as the diesel engine keeps running in transient and non-stationary processes. This work aimed at diagnosing this fault mainly based on frequency band features, which would change when the valve clearance fault occurs. For the purpose of extracting a series of frequency band features adaptively, a decomposition technique based on improved variational mode decomposition was investigated in this work. As the connection between the features and the fault was fuzzy, the random forest algorithm was used to analyze the correspondence between features and faults. In addition, the feature dimension was reduced to improve the operation efficiency according to importance score. The experimental results under variable speed condition showed that the method based on variational mode decomposition and random forest was capable to detect the valve clearance fault effectively.

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 autoencoder (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.

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