By Andrea Watters
The Navy has avoided more than $4.8 million in repairs since May, improved fleet safety and increased H-1 flight line availability—all by using one of several new analytical tools to identify failing subcomponents in the H-1 main gearbox.
Our goal is to increase time on wing and increase mission readiness,” said Brig. Gen. Greg Masiello, Naval Air Systems Command (NAVAIR) assistant commander for Logistics and Industrial Operations.
The tool, a new algorithm detection capability developed by NAVAIR engineer Allen Jones, helps spot main gearbox system faults. By isolating the fault, the engineer can recommend removing components proactively, such as a failing quill gear.
“So instead of sending the aircraft gearbox to depot repair, the fleet is able to replace the subcomponent on the flight line, reducing fleet maintenance burden, reducing costs and improving safety,” Masiello said.
Crews are using conditioned-based maintenance (CBM) to perform maintenance based on need, rather than a set schedule, and have initially focused on rotorcraft platforms: H-53, H-60, H-1 and V-22. The data NAVAIR engineers and logisticians are analyzing is generated by smart aircraft equipped with sensors—similar to tire pressure sensors in today’s automobiles.
“We’re at the point where we can process massive amounts of data, take action and save millions of dollars on repairs as a result of CBM practices,” Masiello said.
To date, the CBM diagnostic strategy has not only saved millions in main gearbox repair costs, but avoided 14 mission aborts (and possible precautionary emergency landings) and reduced the number of drain-and-flush cycles associated with the main gearbox.
Deploying Proactive Tools
NAVAIR has also developed Vector, a readiness analysis toolset, with input from Naval Aviation Enterprise stakeholders. Vector went online in July 2016 as the Web-based successor to the powerful Integrated Logistics Support Management System.
“Our tool, Vector, whose name indicates direction and magnitude, is enabling proactive maintenance and supply,” Masiello said.
Serving as a data warehouse, Vector aggregates 10 years of historical readiness data from 19 disparate data systems into a single source to provide cost, inventory, maintenance, supply and operational flight-hour data in a standard format.
“It [Vector] automates what used to take us months to do and provides information in minutes. Instead of only focusing on the top 20 parts that are challenging the fleet today, we’re working to prevent the next 20 challenges from actually occurring. We want to get out ahead of problems, but we have to do both—fight the current readiness battle and enable the fleet to avoid the next one,” he said.
Vector produces more than 100 top-level metrics to identify components that perform outside their established parameters. With this information, Naval Aviation leaders can see early indicators of potential readiness issues and address them proactively before they impact the fleet, Masiello said.
For example, Vector could have helped prevent a recent fleet maintenance issue involving a potential shortage of brake replacement parts on F/A-18E-F Super Hornets two years before it happened, Masiello said.
By monitoring the databases in real time, Vector indicates when the demand and status of a given part is outside the norm by producing a heat chart—red indicates the part is three standard deviations outside its normal performance (outside of bounds); if orange, two standard deviations, and someone needs to take action; and yellow signals one standard deviation outside the norm, and we should “pay attention and find out why this item is no longer green,” Masiello said.
Vector would have shown an increase in demand for the brake part, which would have prompted the program office or logistician to question the change, Masiello said.
“Today, when Vector indicates a change in part usage, the program office or logisticians can investigate the cause and determine whether they need to order more parts (if the part is still available) or get the Fleet Readiness Center to manufacture the part,” he said.
Predicting the Future
The Logistics and Industrial Operations competency, in collaboration with NAVAIR’s Engineering Modeling Division, is also developing two government-owned forecast models designed to predict which components or parts may need to be replaced based on maintenance schedules.
Developed by the H-53 Program Office, the Readiness Forecast Model (RFM) uses existing Naval Aviation data—such as the current status of parts and aircraft and historical scheduled and unscheduled maintenance rates—to forecast future behavior and assess the near-term impact of specific actions, such as stocking up on a specific part or upgrading a component that requires frequent maintenance.
“RFM provides a one-year forecast of ready basic aircraft, non-mission capable aircraft and out-of-reporting aircraft, enabling a quick look on near-term readiness posture,” Masiello said. “This allows us to understand near-term impacts of top-level actions; essentially ‘what if’ scenarios of major changes/adjustments at system level.”
RFM output on CH-53E and MH-53E simulations are currently being analyzed for accuracy by comparing model output to actual historical data and will be followed by a strategy to deploy RFM rapidly for other key type/model/series (TMS) over the next year.
The Predictive Analytics Model (PAM), an RFM companion, takes a more strategic view at looking at the next 10 years, Masiello said.
According to its developers, PAM runs discrete event simulations using probabilistic decisions and business rules to model TMS flight operations and resulting maintenance and supply demand and effects. The model employs several discrete factors, processes and resources in areas of supply chain, maintenance, flight hour changes, component reliability, life limit increases, depot capacity and performance improvements. PAM output includes metrics on the numbers of aircraft in various states of mission capability readiness.
“PAM will allow us to run a simulation on a part, component or system to see how it would impact the number of [mission capable aircraft]. For example, what would the result be in X number of years if we upgraded the fuel control on a particular aircraft? PAM will predict the fleet readiness impact of an improvement by accounting for component reliability, impact to supply demand and impact to required maintenance. This way, we can see readiness advantages or disadvantages before we decide to invest in making a change,” Masiello explained.
“Bottom line, we have created a series of maintenance planning tools and initiatives that provide decision makers at all levels with the right information to make informed decisions,” Masiello said.
Andrea Watters is the editor of Naval Aviation News magazine.