IoT and machine learning keep aircraft mission-ready

Lockheed Martin transforms aircraft maintenance and fleet management with SAS^® Analytics and AI

From the highest landing strip in the Himalayas to the roughest dirt airstrip in hostile terrain, the C-130J Super Hercules goes where other transport aircraft can’t, don’t and won’t go. More than a proven workhorse, this aircraft of choice for 22 nations and 26 operators worldwide is a data-generating machine.

When aerospace company Lockheed Martin began making the C-130J in the late 1990s, a limited number of sensors collected about 30MB of data per flight hour. Today more than 600 sensors per aircraft generate 3GB every hour.

Lockheed Martin wrangles and exploits that data to revolutionize aircraft maintenance and performance using SAS advanced analytics and artificial intelligence (AI) solutions. The company can predict when parts will fail, keeping fleets airborne for vital missions worldwide.

Improving aircraft readiness for military and humanitarian missions

Countries around the world rely on the C-130 for search and rescue missions, peacekeeping, medical evacuations, scientific research, military operations, aerial refueling, special operations and humanitarian relief.

“The relief efforts are what I’m most proud of,” said Mike Isbill, a Lockheed Martin Fellow specializing in data analytics. “After tornadoes, hurricanes or earthquakes, C-130s quickly bring emergency supplies and aid. They can operate in the most austere conditions, landing in fields or on damaged runways.”

Isbill’s team uses analytics and machine learning to make their aircraft as reliable and cost-effective as possible. When a C-130 sits on the ground waiting for an ordered part to arrive, that downtime affects a customer’s mission readiness. By predicting failure before it occurs, the part can be readily available when and where it’s needed.

Sensors on the C-130J detect and record 6,000 data elements up to 10 times every second. Data elements range from vibrations and temperature readings to location of a part on the aircraft.

“By knowing everything happening on the airplane when a failure occurs, we can build stronger anomaly detection algorithms with SAS Viya,” Isbill said. “The machine learning and AI algorithms help us see into the future – to see when things start to degrade. We can say with better accuracy, this part is going to fail in the next 50, 30 or 20 hours. Customers can plan for maintenance downtime prior to failure, especially before a critical deployment.”

Enhancing efficiency and cost savings

Lockheed Martin began partnering with SAS in 2014 to develop better ways to manage and perform C-130 maintenance.

“We’ve gone from writing on a whiteboard, taking notes and trying to figure out in our heads what the information was telling us, to being able to draw so many insights that weren’t there before,” Isbill said. “Now I can take the data from 2.5 million flight hours for the C-130J and I can slice it, dice it and combine it in ways and at speeds never possible before.”

With the cloud-based SAS Viya analytics engine, Isbill and his team can expand and contract memory as needed, even when running huge simulations.

“We’ve seen some amazing benefits in using SAS Viya in all our processes, including time savings and productivity gains,” Isbill said. “The biggest for me was in cleaning and aggregating the data. The algorithms we’ve developed clean the data for us when it comes in, taking only about 30 seconds, and about 85% of that is done automatically. That alone saves us 40 hours a month.”

Analytics services developed with SAS Viya

Working with SAS, Isbill’s team developed a menu of analytics services for Lockheed Martin customers. A fleet management service uses in-depth analyses of maintenance activity, sensor data, flight profile and repair data to help operations and maintenance teams make faster, more reliable decisions to maximize aircraft availability.

“The customer can go into the fleet management dashboards prior to deployment to diagnose the aircraft and monitor the health of their fleet,” Isbill said. “With insights about which parts in an aircraft might fail, they can make better decisions about which aircraft to deploy and which spare parts to take on a deployment.”

Harnessing the Internet of Things (IoT), a new module built using SAS Event Stream Processing resides on and communicates with the aircraft. It streams and reads data in real time to alert the maintenance team on the ground if any issues exist. Tools and parts can be ready before the airplane lands.

Using a tablet, the flight crew can review the health of any system on the aircraft at any time. If they see anomalies in the data for a mission-critical system during flight, they can decide if they need to abort the mission.

The event-stream processing unit also feeds information to Lockheed Martin’s customer service center, which monitors the health of its fleet across the world. Data trends from flights add to the knowledge base so they can help other customers troubleshoot problems and prevent failures.

Isbill is especially pleased with the security of the IoT solution that offers a “disconnected edge” option for ground crews who work on secure laptops not connected to data in the cloud. “The crew can gather information, run SAS Event Stream Processing in real time, do the analytics as they’re flying, and then we can securely send that back through the military’s 5G communications to keep that data secure,” Isbill said.

Intelligent diagnostics via machine learning optimize troubleshooting

With aircraft data streaming in, Isbill and his team developed the Intelligent Diagnostics tool that applies machine learning and IoT analytics to C-130J flight data. The system combines that data with system knowledge from Lockheed Martin engineers and parts vendors to form a central repository on more than 300 aircraft parts.

The system tracks all the decisions and related events associated with repairing a specific part and learns from them. Intelligent Diagnostics can recommend troubleshooting steps, repairs or replacements in similar situations – creating real-time best practices for aircraft maintenance. For example, the Intelligent Diagnostics service lowers no-fault-found rates, a particular area of aircraft downtime and added expense.

Lockheed Martin further reduces downtime by applying survival analysis to a broad family of line replaceable units for spare parts optimization. Taking the correct set of spare parts (commonly known as a packup kit) on a deployment is crucial. For example, the company worked with one of its largest C-130J operators to track 20 aircraft and 50 parts over six months. Its predictive maintenance models forecasted a 2,000-hour reduction in downtime – 2,000 hours that could be used as flying time in support of mission requirements.

“That’s a 2.6% increase in mission-capability rate,” Isbill said. “That might not sound like a lot, but moving the needle on mission-capability rate is usually done in increments of 0.1 or 0.2%.”

Optimizing part supplies improves readiness of spares for existing customers as well as how the company sells spares to new customers and deploys parts around the world for faster distribution.

Redefining aircraft analytics

Isbill’s team plans to increase its use of event-stream processing and intelligent diagnostics to turn that data into more valuable insights.

“I’m excited about the future of our partnership with SAS,” Isbill said. “With analytics behind the data, we’re going to give the maintenance personnel on the ground information and insights they’ve never had before. They can keep pilots in the air longer, and aircraft won’t have to be on the ground any second longer than necessary.”

Isbill expects those improvements not just on the C-130J but also on other fleets as his team puts data from Lockheed Martin fighter jets into SAS Viya in the cloud.

“The underlying algorithms work for any aircraft,” Isbill said. “It’s the unique data and operating environments you have to adjust. We’ll take what we’ve developed for machine learning, AI and IoT, and apply that to other aircraft platforms.”