Aircraft Maintenance Technology

AUG-SEP 2016

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AIRLINE SUNDEEP SANGHAVI is the co-founder and CEO of DataRPM, an industry pioneer in automating predictive maintenance. For more information visit datarpm.com. 10 AUGUST/SEPTEMBER 2016 AIRCRAFT MAINTENANCE TECHNOLOGY ven though big data may seem like a buzzword that has permeated almost every industry on the planet, this near limitless source of actionable information can drive unprecedented company growth and effi- ciency. Unfortunately, a large part of this potentially useful data is generated by industrial sensors and dumped — largely untapped — into vast data lakes. The noise created by the sheer amount of data we accrue everyday from thousands of sources makes it almost impossible for a human team to analyze. It's a catch-22. These hurdles can be easily applied to predictive maintenance in the aviation sector, which like many other industries around the world has not reached its full, data-enabled potential. However, with 10 times return on investment for aviation companies and a potential 70 to 75 percent reduction in airplane break- downs, there is a clear case for automated predictive maintenance in the aviation industry. ONE PIECE AT A TIME If we look at a solitary part of an aeroplane we can start to gauge the challenges that a manufacturer, a company, or MRO provider can have when aiming to gain effective equipment performance insights by using data generated by sensors. Take an average jet engine from a commercial airliner, which can be equipped with over 5,000 individual sensors, as an example. According to SAP business software, these engines can generate around 20 terabytes of data per hour. Taking a few steps back, an entire plane — such as the A380 superjumbo — can generate information on 200,000 different aspects of every flight, allowing it to create immense pools of data. While the connectivity of planes is increasing, the data generated has already gone beyond what a human team of data scientists can handle and dipped into the realm of automated big data solutions. The key for companies and providers within the industry is knowing how to fine tune the ear of data teams and implement rapidly evolving cognitive models to effectively analyze and harness this data. But how does aviation leverage these data insights to reduce downtime of planes, decrease bottom line costs, and increase component life and efficiency? Is it down to finding the right team or the right solution? Beyond these, however, and as traditional models of data sci- ence continue to become outdated, it is important for companies to seek out the successful trends that will enable optimal use of their sensor data. DIAMOND IN THE ROUGH, IDENTIFYING THE RIGHT DATA SIGNAL The 2015 McKinsey Global Institute report points out the economic impact generated by the Industrial Internet of Things (IIoT) market, which incorporates machine learning and big data technology, to be $11.1 trillion by 2025. For aviation, this economic impact can be felt directly in the predictive maintenance sector, as leveraging this information and conversing with it to get actionable answers remains one of its biggest challenges. According to SAP, 42 percent of delayed flights are caused primarily by airline processes, such as main- tenance. When you take into account that a grounded plane can cost an airline $10,000 per hour, an efficient predictive maintenance process to reduce downtime for an aircraft can save millions of dollars every year. The expanding connectivity of planes and the wave MAN VS. MACHINE: AVIATION'S PREDICTIVE MAINTENANCE CHALLENGE Unlike human teams that can take weeks to analyze just one segment of a dataset in order to accurately install predictive maintenance models, automated solutions can discover critical points in the data in mere seconds By Sundeep Sanghavi

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