Abstract
Due to coupling of multiple cylinders during operation and association of high environmental noise, the existing waveform analysis is inefficient to identify the working status of large multi-cylinder marine diesel engines (MCMDE). In order to solve this problem, a new framework named intrinsic multiscale dispersion entropy (IMDE) is proposed by calculating the multiscale dispersion entropy (MDE) of intrinsically reconstructed instantaneous angular speed (IAS) signal. Firstly, intrinsic characteristic scale decomposition (ICD) has been utilized to decompose an IAS signal into principal components. Then, appropriate components are selected for reconstruction of the intrinsically denoised IAS signal. Lastly, working state of the MCMDE is identified by quantifying the intrinsically reconstructed IAS signal with the help of multiscale dispersion entropy (MDE) leading to concept of IMDE. Simulation model corresponding to a v-16 cylinder engine and experimental data collected from a real life v-16-cylinder marine diesel engine are utilized for validation. Results show that the proposed IMDE can extract the effective fault features under different working conditions and has the highest classification accuracy in compare to other existing techniques: MDE, MSE and MFE.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call