Mimic Monitoring and diagnostics signal processing for early detection of machinery fault situations.
The mimic condition monitoring algorithm allows for studying time-frequency patterns, such as the operation of turbochargers, study of fuel usage, of certain electronics for early prediction and classification of faulty conditions. These findings result in making maintenance decisions based on present condition and performance rather than on a timed basis. The present study was conducted with a view to incorporate the algorithm and data monitoring study for all aspects of the ship with specific focus on fuel consumption. JFM & Lancaster University, UK believe that the results of this study could be a huge cost- saving factor in terms of maintenance costs and would revolutionise fuel efficiency in vessels using Mimic technology. This technology has the flexibility to be adopted and installed into all sorts of vessels and could have a wide spectrum usage.
The research:
JFM provided the Lancaster University’s engineering department with live operational data from their own fleet of ships. The analysis was based on three main factors (a) fault classification using time-frequency patterns (b) early fault detection and the development of sensor systems (c) associated electronics for condition monitoring.
JFM’s Engineering Manager, Martin Briddon is quoted to have said: “This research has helped enormously with deeper signal processing than was currently undertaken and has resulted in a deeper understanding of machinery fault situations. We are now able to use the knowledge and information to develop new ways of searching for machinery faults amongst a clutter of normal operational data.”
Lancaster University, Engineering Department head Dr.Xiandong Ma is quoted to have said: “The team at JFM has been really helpful and provided particular requirements in investigating condition monitoring algorithms and systems within marine ships. The experience has greatly helped to confirm the viability of the proposed project. We are looking forward to our next collaboration with them.”
The £20,000 project was funded by Impact Acceleration Account of the UK’s Engineering & Physical Sciences Research Council, JFM and the University.
Source: The Motorship
