Maintenance Optimization

Maintenance optimization contribute to reduce cost and increase uptime, production and safety. Condition-based maintenance (CBM) is a maintenance strategy that monitors the actual condition of an asset to decide what maintenance needs to be done. A successful CBM schema depends on reliable condition monitoring and data collection.

Basing maintenance activities on actual condition rather than on a predetermined time schedule can progress maintenance work to a new level of efficiency. Fewer maintenance induced damages and failures results in improved equipment availability and more efficient operation for longer periods of time. Unexpected breakdowns can be substituted with planned repairs, allowing more time to be spent identifying areas for further improvement in terms of machine performance, energy efficiency or output.

CBM decisions are based on information collected through condition monitoring, maintenance events, inspections, design quality and reliability, sensor and data quality assessments, environmental conditions and expert knowledge. Remaining useful life (RUL) is the length of time a machine is likely to operate before it requires repair or replacement. By taking RUL into account, engineers can schedule maintenance, optimize operating efficiency, and avoid unplanned downtime.

IFE has long experience in condition monitoring and developing predictive models for RUL estimations from nuclear, oil and gas and other industries.  Prognostic models are developed using both data-driven and physical modelling from classical analytics and machine learning.

Benefits:

  • Increased uptime/reduced downtime
  • Improved system reliability
  • Reduced costs
  • Reduction/elimination of unplanned failures
  • Increased asset life

IFE can provide assistance in these areas:

  • On-line signal validation
  • Plant performance-monitoring
  • Condition-monitoring, prognostic model development and RUL estimations
  • Fault detection based on plant models and data

IFE offers research on assignments. Please contact:

Hoffmann, Mario

Intelligent Systems,

+47 936 25 826

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