Abnormal state detection of diesel engine with flexible monitoring thresholds

In this paper, a condition monitoring method based on enhanced hierarchical fuzzy entropy and support vector data description (SVDD) is proposed to detect the abnormal state of diesel engine. Aiming at the lack of characterization of traditional statistical parameters, entropy algorithm is introduced to extract the fault mode information rich in vibration signal. Combined with the enhanced hierarchical analysis process, the enhanced hierarchical fuzzy entropy algorithm is proposed to enhance the extraction of high and low frequency fault related information in vibration signals. SVDD is used to construct anomaly detection model and set the threshold automatically. And the parrot optimization algorithm is used to optimize the parameters of the support vector data model to improve the adaptability and discrimination of the model. In order to verify the effectiveness of the proposed method, an experiment of a six-cylinder diesel engine under multiple working conditions was carried out to obtain the diesel cylinder head vibration data set under multiple conditions. Through feature analysis and discriminant analysis, the results show that compared with the existing entropy algorithm and intelligent search algorithm, the proposed method shows better representation and discriminant ability, and the average precision and recall rate under multiple loads are 99.32 % and 92 %, respectively.