Finnair had Airbus's Airman but had been working with Frankfurt Consulting Engineers (FCE) on optimizing assets and reducing turn-times. FCE consultants suggested applying their Anomaly Detection Software to the bleed system. “We were quite skeptical,” Skytta remembers. “They didn't know anything about the technology. All they had was parameters.”
Finnair sent flight parameters for one year. The carrier knew what had happened to the bleed system, but did not tell the consultants. Skytta just asked FCE to tell him if something went wrong and whether he had changed components. “They saw the times we had changed a component that had failed. They totally surprised us. They got the right answers,” he says.
So Finnair sent FCE data for the entire fleet for six months and did no bleed-system repairs so as not to ruin the experiment. FCE software spotted problems in the pneumatic system that Airman missed. “We changed it and the problem vanished,” says Skytta.
Often the real challenge in doing predictive maintenance, Skytta notes, is spotting real problems without too many false alerts. “The problem with OEM systems at the moment is you get too many warnings. So the troubleshooter does not trust them. And a solution is no good if no one uses it.”
Lack of data is usually not a problem: “We have thousands of parameters on Airbuses, and airlines have found ways to collect it,” Skytta notes. Carriers already use this data for flight-monitoring, as required by regulations, and some data also could be used for predictive maintenance. However, “pilot agreements might prevent sharing data for other purposes,” Skytta acknowledges. “We do not have a culture of sharing data.” That is unfortunate because the best predictions would be based on multiple-airline data.
Skytta says different data formatting is usually not a big issue, noting that Airbus manages to collect the data it needs mostly in standard format. Airman and other OEM tools can be expensive, especially for small operators, and Skytta believes consultants such as FCE might help them.
Skytta says FCE's statistical approach can look for abnormalities in any system for which there is data, including mechanical, hydro-mechanical and electro-mechanical systems, even generators of electrical power. “It would not work for electronic systems. We do not have metrics on the circuit board,” he says.
FCE has been using its Anomaly Detection Software since 2004, mostly in the power and rail industries. Aviation Project Manager Daniel Jaroszewski says the technique requires no knowledge of underlying physical processes but lots of data as it looks at the correlations among all signals from the system under study.
The aim is to minimize both false alarms and missed critical events. The software can “learn” without supervision by spotting data patterns in a mostly healthy operation of a component or system, as it did for Finnair. The longer an anomaly persists, the more likely it will turn into a critical event. If data on malfunctions is available, the software can “learn” with supervision.