“There has to be a better way.” This was a common phrase among my Sailors on USS Laboon, a guided missile destroyer in the US Navy. Every week the 23 Sailors in my division completed hundreds of hours of maintenance. This maintenance, however, was based on an antiquated system that relied upon time-based maintenance rather than the conditions-based maintenance (CBM) increasingly used in the commercial sector. When combined with internet of things (IOT) sensors and artificial intelligence (AI), CBM has the potential to better predict and avoid machine failure, dramatically reducing equipment downtime and maintenance costs .
The Navy’s current maintenance system relies heavily on time-based maintenance. While there are some systems, like propulsion, that use a primitive version of CBM, this is the exception rather than the norm. The current time-based system might require a technician to replace an air conditioner filter every 90-days. This process has two primary issues. First, the filter could fail in less than 90-days, causing damage to the equipment. Second, the filter may not need to be changed after 90-days, in which case the technician has wasted an hour of time that could have been spent elsewhere. This is particularly problematic on ships like the Littoral Combat Ship (LCS) and the Zumwalt Destroyer Class that rely on minimally-manned crews augmented by automated systems.
CBM, on the other hand, is performed after one or more indicators show that equipment is going to fail or that equipment performance is deteriorating. CBM is particularly impactful when combined with IOT and AI applications. For example, a remote sensor could be placed behind the filter to detect flow rate. This sensor could feed the flow rate data into a central repository where it could be aggregated with other sensor data and routed through an AI algorithm. The algorithm could determine a) if the filter needs to be replaced and b) if the combination of the flow rate data and other sensor data from the AC indicates a more significant issue with the equipment as a whole. Over time, as the algorithm digests greater amounts of data it can, through reinforced learning, create more accurate predictions.
The military is not blind to the importance of smarter maintenance. The Department of Defense has invested heavily in Defense Innovation Unit Experimental (DIUx) , a DOD-funded venture capital unit, which has made machine learning for predictive maintenance a priority . In June the group awarded $1mm to Uptake, a Chicago-based startup that has partnered with companies including Boeing and Caterpillar . The contract is to pilot a CBM program for the Army’s Bradley Fighting Vehicle. In addition, Sea Systems Command (NAVSEA), the Navy command responsible for engineering and building Navy ships, has begun to incorporate a broader array of equipment sensors that will inform future CBM systems .
While these are certainly steps in the right direction, the Navy cannot proceed at the glacial pace that often characterizes change in large bureaucracies. To that end, the Navy should do three things. First, the private sector is successfully utilizing AI-enhanced maintenance programs and the Navy should take advantage of the work that has already been done. Specifically, it must dramatically expand the number of pilot programs with tech organizations like Uptake. Second, it must eliminate (or atleast reduce) the red tape that often prevents private sector organizations from partnering with the military. Finally, the Navy must ensure that lessons learned in the other branches inform its own progress. For example, any successes or failures associated with the Bradley Fighting Vehicle program must influence future Navy projects. Time is too short to repeat failures.
What Comes Next?
As the Navy moves toward an AI-enhanced CBM model there are critical questions that must be answered. Will information security and cyber security concerns outweigh the benefits of this new system? Are there other elements of private-sector maintenance systems that should be incorporated? Will recent military-related protests at Google  lead other private sector organizations (which are vital to the future of this model) to abandon their work with the DOD?
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 Smartening Up With Artificial Intelligence (AI)- What’s in it for Germany and its Industrial Sector. (2017). Retrieved November 11, 2018, from www.mckinsey.com
 Cornillie, Chris. “Rump Embraces Obama’s ‘Venture Capital Firm’ for Pentagon Tech.” Bloomberg Government, 21 Feb. 2018, about.bgov.com.
 Defense Innovation Unit Experimental (DIUx) Annual Report. (2017). Retrieved November 11, 2018, from https://diux.mil/download
 Greg, A. (2018, June 26). Army to use artificial intelligence to predict which vehicles will break down. Washington Post. Retrieved November 11, 2018, from www.washingtonpost.com
 Graffius, R., & Gaffney, R. (2016, Fall). Changing How the Navy Schedules Maintenance. Surface Warfare Magazine. Retrieved November 11, 2018, from https://www.public.navy.mil/surfor/swmag
 Shane, Scott, and Daisuke Wakabayashi. “’The Business of War’: Google Employees Protest Work for the Pentagon.” New York Times, 4 Apr. 2018, www.nytimes.com/2018/04/04/technology/google-letter-ceo-pentagon-project.html.