By Michael Weigand, Co-Founder and Chief Growth Officer, Shift5
In its annual threat assessment, the Office of the Director of National Intelligence (ODNI) warned that China is “almost certainly capable” of – and would consider – undertaking aggressive cyber operations against U.S. military assets worldwide if a major conflict were imminent. And recent data suggests that the results could be catastrophic as our defense resources remain vulnerable to operational failures and cybersecurity risks. In order for the U.S. to maintain its military edge, we need to proactively ensure the reliability, security and efficacy of our critical assets especially as it relates to the Department of Defense (DoD).
The DoD uses mission capable (MC) rates to assess the health and readiness of its military fleets – and just last year, the GAO reported that the MC rates of F-22s dropped nearly 17 percentage points over a six-year period. It also found that the DoD did not meet its MC goals for FY 2021 for 47 of the 49 aircraft in its review, with most aircraft more than 10 percentage points below the goal.
With national security depending on the safety and reliability of critical defense assets – predictive maintenance is key to ensuring operational readiness. Predictive maintenance refers to the use of hardware, software, and service components to provide predictive analytics for mechanical assets, infrastructure maintenance, and reliability objectives. Used to monitor emerging failures, predictive maintenance uses real-time condition-based monitoring and artificial intelligence (AI)/machine learning (ML) inferencing to identify expected failure points and determine remaining asset life. This intelligence enables military organizations to enhance operational readiness, enable cyber survivability, lower costs, shorten sustainment cycles, and increase platform availability [i.e., MC rates].
The DoD issued an interim predictive maintenance policy back in 2002. But in a December 2022 report on improving military readiness, the GAO found that in the 20 years since, military services have made limited progress in implementing it – despite pilot programs and evidence of improved maintenance outcomes. In a recent survey, 73 percent of DoD operations, maintenance, and IT leaders said they feel the lack of predictive maintenance across the DoD directly correlates to a low platform readiness/availability. However, if implemented and utilized correctly, the DoD can use predictive maintenance to its full potential, boosting the resilience and readiness of critical defense assets.
From Reactive to Proactive
In the absence of predictive maintenance, unplanned or reactive maintenance leads to more operational downtime and higher costs in the long run. DoD officials report that reactive maintenance often requires more materials and a higher level of effort than planned maintenance. In fact, by waiting until things break to fix them, the DoD spends $90 billion a year to keep ground systems, ships, and aircraft combat-ready, according to the GAO.
Downtime of critical defense assets puts personnel and national security at risk – but it is preventable. Minimizing downtime is key to improving operational readiness. In the same survey, 62 percent said they experienced weapons systems or aircraft downtime that could have been prevented with the use of predictive maintenance in the past year.
Turning Data into Intelligence
DoD operations, maintenance, and IT leaders need real-time knowledge of what is happening across all equipment and systems, unfortunately 73 percent say their current tooling fails to provide the data access and observability needed for effective predictive maintenance. Often, that’s because sensors, log files, and recording devices capture data at prescribed intervals, rather than in real time, because of storage limitations. As a result, when an anomaly pops at a millisecond, it often escapes detection by a sensor.
The problem underpinning these challenges, however, is access to this onboard data when digital anomalies arise. Weapon systems today lack an onboard sensor capable of capturing, storing, and analyzing these massive amounts of data on the edge in real time. Consider this: if you don’t have a complete dataset to begin with, the output of whatever you are trying to do with the data – in this case, take action for cyber or maintenance purposes – will only be as beneficial or accurate as the input.
In one of its pilot programs, the U.S. Air Force used AI and ML algorithms with data collected from condition-monitoring technology to predict when B-1 bombers will break. The Air Force estimated saving $5 million in two years – just by reducing unscheduled maintenance on ten B-1 bombers.
However, success is fragmented because the DoD has not consistently adopted and tracked implementation of predictive maintenance. In the survey of DoD operations, maintenance, and IT leaders, 84 percent said their organization must improve its ability to predict and prevent equipment failure.
Observability Builds Resiliency
Operational readiness hinges on real-time knowledge of what’s happening onboard critical defense assets. But 60 percent of DoD leaders say that when an issue arises, their organization struggles to determine if the root cause is a cyberattack – or an equipment maintenance issue.
This stems from a lack of full system observability – a full view of system performance. With onboard observability, maintenance teams have the knowledge to evaluate operational readiness and differentiate between cyber and maintenance issues. The best way to visualize the full system is to capture all data communications on the operational technology (OT) and from every component on the platform. To capture real-time onboard data from legacy OT, the DoD needs full-take data capture, which records every frame down to the millisecond – across the entire fleet.
Armed with onboard observability, the DoD can confidently act on data-driven insights to optimize the availability and resiliency of defense assets and boost MC rates across the services. On average, DoD leaders expect a 35 percent increase in their department’s MC rate from the successful implementation of predictive maintenance, according to recent research.
The growing complexity of technology and critical defense assets, paired with the increasing threats to national security, requires an urgent reform of the DoD’s approach to maintenance. Predictive maintenance offers solutions to some of the DoD’s greatest challenges: shifting to a proactive approach to maintenance by predicting and preventing cyberattacks and critical failures.
About the Author
Michael Weigand is the Co-Founder and Chief Growth Officer at Shift5. He is responsible for defining and overseeing execution of Shift5’s long-term growth objectives. Prior to Shift5, Michael served eight years in the U.S. Army as an Airborne, Ranger-qualified Infantry officer and was selected as one of the first cyber operations officers. While at a Department of Defense support agency, he served as an engineering and operations officer conducting both applied research and development (R&D), development, and field operations.
Michael has established and commanded multiple cyber organizations and skunkworks-style teams across the Army and DoD. Notably, he was instrumental in the establishment of the Army’s platform mission assurance program, the Army’s expeditionary cyber forces, the Army’s first cyber capability development unit, and multiple high-profile projects in conjunction with the Defense Digital Service. Michael holds a BS in Computer Science from the United States Military Academy. Other than defending OT, Michael’s secret superpower is flying small airplanes into small places.
Michael can be reached online at linkedin.com/in/michael-weigand/ and at our company website shift5.io/.
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