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Maintenance Maturity Models Cheat Sheet (DRAFT) by [deleted]

Maintenance Maturity Models

This is a draft cheat sheet. It is a work in progress and is not finished yet.

Introd­uction

To gauge IIoT impact on mainte­nan­ce-­related activi­ties, it helps to revisit higher­-level concepts related to mainte­nance maturity and associated defini­tions. ARC's recent review of current maturity models uncovered many different versions of it as well as many internal incons­ist­encies. The industry lacks a true standard to build upon. This lack of clarity makes it difficult to compare solutions, leading to confusion among potential users and delaying the applic­ation of solutions.

While industry partic­ipants generally have a good unders­tanding of both reactive and preventive mainte­nance, we've encoun­tered a variety of interp­ret­ations for condit­ion­-based, predic­tiv­e-m­ain­tenance and prescr­ipt­ive­-ma­int­enance approa­ches, and where IIoT comes into play. A clearer definition of these upper mainte­nan­ce-­mat­urity levels is needed for users to be able to better assess the available altern­atives.

Reactive mainte­nance

Reactive, or run-to­-fa­ilure, mainte­nance is the most common approach for equipment, since most assets have a very low probab­ility of failure and are non-cr­itical. This approach helps control mainte­nance costs, but is only approp­riate for non-cr­itical assets.

Preventive mainte­nance

Here, mainte­nance is performed based on either time (analogous to replacing the batteries in your household smoke detectors once a year), or usage (changing your car's oil every 5,000 miles). Preventive mainte­nance applies to assets with an age-re­lated failur­e-p­attern, where the frequency of failure for the asset increases with age, run-time, or number of cycles.

Condit­ion­-based mainte­nance

CBM involves monitoring a specific asset parameter. The focus tends to be the amplitude of the value, with vibration monitoring being the most common. CBM typically applies to production (rotating equipment) and automation (instr­uments and the control system) equipment. For stationary plant equipment, such as steam boilers, piping and heat exchan­gers, periodic inspec­tions and condition evalua­tions are often used.
 

Asset Maturity Models

Predictive mainte­nance

PdM uses engineered algorithms and machine learning with multiple input parameters to provide higher accuracy (fewer false positives or missed issues) and more advanced warning before failure. It combines "­small data" from a device or system with algorithms that model that type of equipment (sometimes called virtual equipment or a "­digital twin") to monito­r-c­ond­ition and raise an alert when approp­riate. This provides the more advanced notice needed to schedule and execute the mainte­nance during planned shutdowns.

Prescr­iptive mainte­nance

Builds on PdM with alerts that provide diagno­stics and guidance for repair. Inform­ation for determ­ining the timing and impact of failure is also included to help assess priority and urgency.