Knowing in advance when an asset is likely to fail is advantageous in order to avoid unplanned downtime and asset damage. Predictive maintenance has been demonstrated to reduce maintenance costs by 25% and reduce asset breakdowns by 70%. In some cases, automotive assembly line manufacturing stops can cost £10,000 per minute, so there is a real incentive to cultivate predictive capabilities to detect developing faults in industrial machinery. The development of AI models for early fault detection has widespread applications across industry, including shipping, food and beverage, automotive, and logistics.
TWI has been working alongside a consortium comprising Vibtek Ltd, CMServices Global Ltd and Brunel University to develop a prescriptive maintenance system as part of the DiMOS Project, which has also gained input from Condor Ferries on the South Coast of England, William Grant and Sons in Scotland, Royal Mail in Sheffield, and Toyota Manufacturing UK in Derby.
The system was designed to perform prescriptive maintenance using artificial intelligence models to allow for early fault detection. TWI led the development of the application of artificial intelligence-based models to identify and locate developing faults within industrial machinery such as motors, fans and pumps at the case study sites. The system has been designed to take into account risk levels and likely failure modes into the prescriptive action.