Proactive Asset Management with IIoT and Analytics

Author photo: Larry O'Brien
ByLarry O'Brien
Category:
Industry Trends
ARC Research Director Ralph Rio just wrote an interesting report on proactive asset management with the Industrial Internet of Things and Analytics, which can be accessed from the ARC web site here. Excerpts from the report:

Preventive Maintenance: Appropriate for Just 18 Percent of Assets

Preventive maintenance schedules inspections and/or repairs are typically based on calendar time, run time, or cycle count. This approach assumes that the probability of equipment failure increases with use. However, data on failure patterns compiled by NASA and the US Navy (see chart on page 3) show that only 18 percent of failures are age related, and 82 percent have a random pattern. Based on these data, preventive maintenance (PM) provides a benefit for just 18 percent of assets. Doing a PM on the other 82 percent may well cause failures by placing the asset at the beginning of the Type B curve for early life failures.

Predictive Maintenance: Simple Math

PdM uses condition monitoring to provide advanced notice of a failure so appropriate maintenance can be scheduled and performed to prevent the unplanned downtime. The three common approaches for the condition monitoring include:

  1. Monitoring a process data value associated with the asset from the plant historian with trend charts and/or a mathematical calculation
  2. A plant asset management (PAM) system with sensors (commonly vibration) typically applied to rotating equipment
  3. Periodic inspections and evaluation involving infrared, ultrasonic, oil analysis or corrosion typically used for stationary plant equipment such as steam boilers, piping and heat exchangers
Approach Method Applicability Comparison
Reactive Run to failure and then repair Non-critical assets with low impact from failure 10X plus when failure occurs
Preventive Service in a fixed time or cycle interval Failures increase with age or usage 2X maintenance costs
Predictive (condition monitoring) Monitor process data, identify bad trends, & alert prior to failure Simple systems where single variable math predicts a failure 1X maintenance costs
Proactive (analytics & multiple variables) Equipment-specific data acquisition with algorithms, analytics, and/or a model Complex systems where multiple variables and analytics can predict failure Unscheduled downtime approaches zero
Proactive Asset Management with IIoT and Analytics  

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