Condition Monitoring and Fault Diagnosis for Marine Diesel Engines using Information Fusion Techniques
AbstractThe structural complexity of marine diesel engines and the failure transmission path significantly influence the quality of a measured vibration signal. A set of accelerometers have been involved in the condition monitoring and fault diagnosis (CMFD). To make full use of multi-channel sensor signals, a new information fusion method is proposed for the CMFD of marine diesel engines in this paper. For the signal fusion, the independent component analysis (ICA) was firstly adopted to separate useful source signal close to the engine vibration characteristics of the fault components from multi-channel sensors. Then the short time Fourier transform (STFT) was applied to the fault feature extraction and the principal component analysis (PCA) was used to fuse the feature space from a high dimension into a very low one. Followed, a Fuzzy neural network (FNN) classifier was employed to identify the engine faults. The real vibration data measured on a ship using four-channel sensors was used to evaluate the proposed method. The experimental diagnostic results demonstrate that the developed diagnostic method captures distinct time-frequency features of the vibration signals for monitoring the engine health condition with a fault detection rate of 90.5%. Moreover, the performance of the proposed method is superior to that without information fusion processing. Thus, the proposed method is feasible and available for the CMFD of marine diesel engines. Ill. 5, bibl. 11, tabl. 1 (in English; abstracts in English and Lithuanian).
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