Condition Monitoring & Predictive Maintenance

Condition Monitoring & Predictive Maintenance

Objective Provide personnel with access to real-time assessments of critical asset performance so they can better track utilization and anticipate maintenance issues to prevent asset failure, reduce risk, increase productivity, and lower costs.

Technologies Condition-monitoring sensor & I/O devices, edge compute/gateways, analytical data modeling and data conversion & visualization

Partners CBT, SparkCognition, NI, HPE, PTC, Allied Reliability, OSIsoft


cbt   sparkcognition                 NI, Condition Monitoring     



In large, complex environments such as factories and refineries, converting raw materials into useful products demands constant monitoring and fine tuning. Traditional manual inspections are often time-consuming, labor-intensive, and carry safety risks for employees. Utilizing innovative technologies such as IoT, advanced analytics, edge computing, and artificial intelligence can enable a better, more modern and efficient way to gain and retain insights, manage operations, anticipate issues, and keep employees safe.

Deloitte brought together a team of leading technology vendors to deploy a secure, scalable, turnkey predictive maintenance and condition monitoring solution. Sensors installed on machinery collect operational data in real-time and feed into dashboards designed around user personas, allowing workers to efficiently – and safely – gather insights, make decisions, and improve operational effectiveness. By applying preconfigured analytics, software, infrastructure, and security, the team created a holistic machine-learning solution for monitoring equipment performance and anticipating failures.


Use Cases – Asset Health, Condition Monitoring, Predictive Maintenance/Analytics


Potential Benefits & Value

  • Improve return on assets by reducing spare equipment inventory
  • Anticipate machine failures and reduce unplanned downtime by scheduling repairs during planned outages
  • Reduce worker safety incidents by automating data collection



  • 20% expected reduction in downtime
  • 50% estimated reduction in planned maintenance costs
  • Target 0 safety incidents and unplanned outages