Sikorsky's S-92 helicopter had been in service for 12 years when it reached one million flight hours. Just six years later, the fleet is operating in 28 countries and its flight hours have doubled.
“The two million flight hours milestone is a testament to the reliability, availability and cost-effectiveness of the S-92 helicopter in some of the world's most demanding conditions and no-fail missions,” says interim vp of global commercial and military systems Leon Silva. “Sikorsky is committed to supporting these critical missions with continued innovation to ensure operators can respond safely and with confidence, in any scenario.”
Operators utilising the S-92 enjoy a better than 93 per cent availability rate, a best-in-class safety record, even in extreme conditions, and a proven record when it comes to reliability and adaptability.
Sikorsky has delivered 300 S-92s with about 86 per cent operated in the offshore oil and gas industry for personnel transport. Every major oil company relies on S-92s in its fleet thanks to the aircraft's unsurpassed capabilities and capacity that minimise per seat-mile costs while reducing needed trips and risk.
Fleet aircraft are also in service for civil search and rescue and have completed more than 91,000 search and rescue missions, as well as commercial airline transport, executive transport and other priority missions including coastal and border patrol, emergency response and disaster relief. The S-92 is used by 13 nations for head of state missions, and the aircraft is the baseline for the VH-92A helicopter to be used for the new US presidential helicopter fleet.
“We're grateful to our customers, our more than 150 suppliers worldwide and our employees who support our award-winning S-92 programme,” Silva adds. “They have all contributed to this major milestone, and we look forward to continued success.”
Sikorsky's advanced predictive maintenance capabilities are a key enabler to sustaining its commercial and military programmes. Data is captured and analysed across millions of flight hours to identify the biggest maintenance drivers to improve readiness and reduce costs.
By combining data sets, analytics, machine learning and prognostic algorithms, operators are equipped with the information and parts they need to perform maintenance actions.