On the pros and cons of the remaining useful life KPI

AspenTech critique of RUL estimator advocates ‘low touch’ ML. MathWorks predictive maintenance toolbox uses physical modeling to assesses RUL.

AspenTech blogger Mike Brooks has provided a critique of ‘remaining useful life’ (RUL) as a metric for predictive maintenance applications. While RUL is a commonly accepted KPI, Brooks claims that it is a ‘flawed concept’. Traditional maintenance is organized around statistical assumptions of failure mechanisms, mostly related to normal wear and tear. However, studies by ARC Advisory Group have shown that over 80% of asset failures are caused by ‘errant process’ rather than age-related issues, making conventional preventive maintenance strategies ineffective. Examples of such errant processes include operator error, lightning strikes and voltage spikes or episodic cavitation in a pump. Most of these random events make the reliable prediction of RUL impossible. For more on AspenTech’s approach to prescriptive analytics and equipment failure read the white paper on ‘Low-touch’ machine learning for asset management.

In a MathWorks article, Aditya Baru has no qualms about using RUL as a KPI and suggests three ways of estimate RUL for predictive maintenance. MathWorks’ Predictive maintenance toolbox computes RUL depending on what data is available - lifetime machine data, run-to-failure histories and threshold values of know failure condition indicators. Computing RUL may leverage proportional hazard models and probability distributions of component failure times. For example, a battery’s discharge time may be estimated from past discharge rates and covariates, such as operating temperature and load. The MathWorks approach involves physics-based forward modeling and Kalman filtering. More RUL examples here.

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