A modelling approach for predicting marine engines shaft dynamics

For making decisions on maintenance and operations of ship systems in a timely and cost effective way, intelligent approaches for continuously assessing the critical ship systems condition are required. This study aims to provide a framework for large marine two-stroke diesel engines performance assessment, by mapping the relationship of specific malfunctioning engine conditions on the Instantaneous Crankshaft Torque (ICT). This is accomplished by the development of a thermodynamics model, which is coupled with a lumped mass crankshaft dynamics model, in order to predict the engine shaft dynamics and torsional response. Subsequently, by employing the coupled engine models, a number of case studies are simulated for investigating the influence on the engine ICT, which include: (a) change in the Start of Injection (SOI), (b) change in the Rate of Heat Release (RHR), (c) change in the scavenge air pressure, and (d) leaking exhaust valve. By investigating the predicted ICT from the coupled model in both the time and frequency domains, distinct frequencies are identified, which correspond to specific engine malfunctioning conditions. Based on the derived results, these engine malfunctioning conditions are mapped with the frequencies most affected in the engine’s instantaneous torque, which demonstrate the usefulness of implementing the the ICT measurement for diagnostic purposes

On the Use of Wavelet Transform for Practical Condition Monitoring Issues

The aim of this study is to assess the effectiveness of the Wavelet Transform (WT) for machine condition monitoring purposes. In this chapter the WT is set up specifically for vibration signals captured from real life complex case studies having a poor extent in literature: marine couplings and i.c. engines tested in cold conditions. Both Continuous (CWT) and Discrete Wavelet Transform (DWT) are applied. The former has been used for faulty event identification and impulse event characterization through the analysis of the three-dimensional representation of the CWT coefficients. The latter has been applied for filtering and feature extraction purposes and for detecting impulsive events strongly masked by noise. Comparing the results from both the CWT and DWT analyses it has been clearly demonstrated the ability of the WT in satisfying both the condition monitoring and fault detection requirements for all tested cases. This means that the application of the WT not only permit to recognize the change of the state of the tested machine but it is also able to localise the source of the alteration

Fault detection using transfer function techniques

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