CMServices Global Ltd. has been working as part of a consortium of companies to develop a condition-based monitoring system for ship structures, engine machinery and other auxiliary systems.
The two year project, which received funding from Innovate UK, saw CMServices Global Ltd. work alongside project partners, Vibtek Ltd, TWI Ltd and Brunel University, as well as gaining assistance from Condor Ferries on the South Coast of England, Grant’s Distillery in Scotland, the Royal Mail in Sheffield and Toyota in Derby.
The DiMOS system was designed to perform predictive maintenance procedures using an artificial intelligence system to allow for early fault detection. The aim was to be able to spot a fault as it developed, allowing maintenance to be undertaken in a timely manner to save on unnecessary maintenance costs and prevent damage to assets.
Using real-time sensor data along with AI-based models to prescribe maintenance, the system has been designed to take account of risk levels, maintenance schedules and the associated costs.
By using AI, it was possible to alleviate the time consuming manual interpretation of data. This is becoming increasingly important as systems generate greater quantities of both vibration and process data. The fully automated DiMOS software is able to evaluate an entire machine in a few seconds, although there is still the capability to assess and verify the data manually if required.
The data collection stages of the project were threatened by the COVID-19 pandemic, but assistance from Condor Ferries on the South Coast of England, Grant’s Distillery in Scotland, the Royal Mail in Sheffield and Toyota in Derby, allowed this important work to continue.
The data collection was completed both in real time and periodically, allowing the system to be assessed in different service conditions. These tests monitored assets that are typically found in the marine industries, such as pumps, fans, compressors and gearboxes.
This testing and data collection allowed the project partners to bridge the gap between condition monitoring systems and artificial intelligence to deliver prescriptive maintenance planning platform tools for machinery systems, using machinery reliability templates supported by local failure mode effects and criticality analysis data.
The project also saw the creation of a prototype platform, which underwent beta testing at various operational sites across the UK.
The DiMOS Project progress has created a system that can eliminate unnecessary tasks so that resources can be focused on those that improve the through-life cost of asset ownership and add value.
With a capability to provide an automated early warning system for impending machinery failure, the system is now ready to be rolled out to industries outside of marine too. Potential applications for the system include power generation, automotive and aerospace.
The system is currently installed at a shipping company and more businesses are being approached to further develop the system and ensure its rigidity in the first quarter of 2021.
You can find out more about this project at https://www.dimosproject.com/
The DiMOS Project has received funding from Innovate UK with Project Reference No.104505