Abnormal state detection of diesel engine with flexible monitoring thresholds

• An enhanced multiscale fuzzy entropy and support vector data description-based method is proposed. • The novelty method is applied to detection health state of diesel engine under different operating conditions. • The proposed approach is validated with experimental data of a diesel engine under the normal and abnormal states. • Feature analysis shows the better representation of the proposed method. • The proposed method shows high accuracy and recall rate for diesel engine state detection. 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.