Predictive maintenance

Anticipate asset failures to act early and reduce cost

There are three forms of maintenance.

  • Corrective maintenance: repairs made after a problem or failure occurs
  • Preventative maintenance: scheduled repairs made based on experience
  • Predictive maintenance: repairs made because data for an asset indicates that a failure is imminent

Predictive maintenance by definition requires regular condition monitoring of assets, to predict when maintenance is needed as opposed to performing such maintenance at fixed intervals. Assets under operational and environmental stress may start to fail earlier than expected. Early detection of deterioration in asset condition is key to predictive maintenance which is supposed to grow to USD 65 billion by the year 2030 at a CAGR of 27.4%, higher than most other technology areas. Predictive maintenance is only feasible with a well-established condition monitoring system which provides input into an AI/ML predictive maintenance software that determines when maintenance is needed. It is well-acknowledged that performing timely maintenance and repairs is cheaper than when assets have stopped functioning altogether, e.g. in railways it is lower cost to grind a rail when a surface defect develops, as opposed to replacing the rail when there is a complete fracture. In some cases predictive maintenance is also guided at understanding the root cause of the problem. If the underlying issue is not resolved, no matter how many times the asset is maintained, it will continue to fail. Multi-sensor based condition monitoring coupled with AI/ML algorithms is used for understanding such core issues. The development of all AI/ML models and algorithms requires a deep understanding of the properties of the asset involved and what condition monitoring systems provide as input.  

Predictive maintenance is very important to any business because:

  • The life of the assets can be extended reducing capital expense on buying new assets
  • The cost and complexity of repairs is reduced
  • Operational downtime is reduced increasing overall efficiency
  • Inventory management is simplified as all required spare parts and materials are stored
  • It is easier to comply with standards and meet compliances

Our expertise in the area of predictive maintenance includes, but is not limited to:

  • Design and development of predictive maintenance models, customised to managing specific industry segments and their assets using detailed domain knowledge
  • AI/ML based predictive maintenance software development in line with industry 4.0 practices
  • Development and interfacing of condition monitoring systems with an IT backbone to deliver data to predictive maintenance systems
  • Cloud based web-portals for clients delivering customised tools to review, prioritise and export predictive maintenance analysis
17 January 2023