THE MAINTENANCE FORECAST


understanding of an aircraft’s health, much like radar and weather stations define conditions in the sky. Prognostic systems forecast the future health of an aircraft, just like weather forecast models predict daily conditions. Such informa- tion about current and future health will inform our decisions on aircraft readi- ness, what maintenance needs to be done today, and which in the future, to best meet upcoming operational demands. Planning maintenance for the future based on the current health of the aircraft and its forecast state is known as predic- tive maintenance. Te advantage of PPMx is that maintainers gain new, quantifiable knowledge of impending failures. Tey can decide when to conduct a repair by weighing the risk to the aircraft against operational demands. Te efficient control of maintenance will increase operational availability, decrease lifecycle costs and reduce mission disruption.


QUANTIFYING RISK Predictive maintenance is built on a series of decisions following a framework that scientifically evaluates the likelihood of component failure and its effect on the system. Te approach is not much different than the Army’s composite risk manage- ment. Risk is defined as the product of probability (likelihood of component fail- ure) and severity.


Risk = Probability × Severity


Severity is measured by the criticality of a fault and is denoted by the threat of injury to crew and damage to systems, repair time, repair cost and mission disruption. Te weighting and combination of these factors, from criticality to mission disrup- tion, are defined in reliability engineering as the “loss function.” Risk is formally calculated using a Bayesian framework with loss functions. Te Bayesian frame- work is a mathematical means to update


78 Army AL&T Magazine Spring 2022


the probability of failure given measured evidence (data). Te likelihood of failure is updated by diagnostics that provide the data (measured evidence) to evalu- ate system health. Te likelihood is fused with the criticality to give an estimate of the risk to the platform. Te risk for each component is then synthesized into an overall risk to the aircraft.


Predictive


maintenance allows logistical ordering to lead the maintenance, thereby reducing logistical downtime and increasing availability.


A deep dive into this topic, along with the data requirements and calculations, is best reserved for a technical article; however, it is important to know that minimization of the composite risk drives the decision about whether to conduct maintenance now or defer to the next window, ensur- ing there is no unscheduled maintenance in between. Te framework accurately assesses and properly bounds predic- tions and conveys that information to commanders, maintainers and logisticians.


USING RISK TO MODERNIZE Making predictions about the future is inherently a probabilistic task. Uncer- tainty grows the further into the future a prediction is made. We have experi- enced this. Weather predictions become less reliable the further in the future they


forecast. Te same is true for components that experience wear and fatigue in the rotor, flight controls, airframe, engine, drive and weapon systems. Figure 1 (see Page 79) illustrates the probability of failure increasing with accumulation of wear during flight hours. In practice, an onboard diagnostic system collects usage and environment data to make an esti- mate of the current health state. Next, expected usage and prognostic models forecast failure times where the spread of possible outcomes increases the further in the future the prediction is made.


Figure 1 illustrates PPMx supporting a maintenance-free operating period. Te operating period, shaded green, provides an extended period without disruption by maintenance. Te recovery period, shaded orange, consolidates maintenance tasks between the operating period and provides discrete windows for maintenance. Te key question when entering a recovery period is: “What is the likelihood each compo- nent survives to the next recovery period?” To answer this question, a measurement is performed that assesses the health state while in the maintenance window. Tis can be done through a combination of embedded solutions and nondestructive inspections. Te estimated health state is then combined with expected usage to predict the remaining useful life.


Because of the uncertainty in predict- ing the future, a range of possibilities is computed. Tis is shown as a blue normal distribution. Te dotted lines that lead from the current health estimate to the future represent the probability that the component survives at least that many hours. The most likely time of fail- ure (highest point in the distribution) is called the expected remaining useful life, but the component may fail at any of the times under the distribution of possible outcomes.


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