Instantaneous Angular Speed-Based Fault Diagnosis of Multicylinder Marine Diesel Engine Using Intrinsic Multiscale Dispersion Entropy

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 multicylinder marine diesel engines (MCMDEs). To solve this problem, a new framework named intrinsic multiscale dispersion entropy (IMDE) is proposed by calculating the MDE of intrinsically reconstructed instantaneous angular speed (IAS) signal. First, intrinsic characteristic scale decomposition has been utilized to decompose an IAS signal into principal components. Then, appropriate components are selected for reconstruction of the intrinsically denoised IAS signal. Finally, working state of the MCMDE is identified by quantifying the intrinsically reconstructed IAS signal with the help of 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 when compared to other existing techniques: MDE, multiscale sample entropy (MSE), and multiscale fuzzy entropy (MFE).