February 11, 2013
Henry Canaday Washington
Airlines use predictive maintenance to slash costs, convert unscheduled maintenance to scheduled events and reduce asset downtime. They have been doing it on engines for some time, and now airlines can perform predictive maintenance much more effectively because new aircraft yield more data, and the tools for exploiting it have improved dramatically. Yet ways of thinking and business processes also must change, which often is more difficult than tapping sensors. And who will conduct the analyses upon which crucial maintenance decisions are made: OEMs, maintenance providers, airlines or consultants?
Most predictive maintenance today starts with OEM tools. Alan Epstein, vice president of environment and technology at Pratt & Whitney, describes predictive maintenance as using sensors and computers to do what human inspectors once did by disassembly and visual inspection. “Maintenance was always on condition; we just use computers to understand condition now,” he says.
Engine monitoring began when statistical correlations were observed and then traced to their physical causes.
Most Pratt customers use the company's Advanced Diagnostics and Engine Management (ADEM) to help plan engine maintenance. Epstein predicts Pratt and other engine OEMs will continue to improve prognostics, partly because their long-term support contracts offer huge incentives to keep costs down and availability up.
Auxiliary power units also are very mature in predictive techniques, notes Kristen Law, director of mechanical maintenance strategy and condition-based maintenance at Honeywell. The company maintains a website showing trend data for its air-transport APUs and estimates how many hours are left before major repairs. This Predictive Trend Monitoring and Diagnostics (PTMD) tool offers troubleshooting tips, with estimates of the probability of each tip's success.
Honeywell also makes central maintenance computers to collect data from many subsystems. Its CMC on the Boeing 777 collects data from more than 80 systems and sends it to airlines or vendors. This CMC can intelligently filter multiple faults that propagate from a single failure and focus attention on the root of the problem.
But predictive possibilities differ from component to component. For example, for engines and APUs, systems can suggest maintenance actions, but only trend data is offered and flagged green, yellow or red for many other components. Someone at the airline or elsewhere must then decide what to do.