Predictive Maintenance in Aerospace Industry |Turbofan Engine | Machine Learning
Think about all the machines you use during a year, all of them, from a toaster every morning to an airplane every summer holiday. Now imagine that, from now on, one of them would fail every day. What impact would that have? The truth is that we are surrounded by machines that make our life easier, but we also get more and more dependent on them. Therefore, the quality of a machine is not only based on how useful and efficient it is, but also on how reliable it is. And together with reliability comes maintenance.
What if an aircraft part could tell you when it needs to be repaired or replaced? With continuous data collection, monitoring and application of advanced analytics, it can.
In Aviation big data analytics has promoted the viability for performance optimization of aircrafts through predictive maintenance at a cheaper and effective manner for the airline industry, also providing operational and financial advantages over limited infrastructural or operational modifications.
Hence, the data analytic platform based on existing infrastructure in both aircraft and information technology had been discussed to provide significant support on real-time ground and air-borne decision making utilizing the conventional data analytics techniques such as regression, association rule, decision trees etc. in a machine learning environment approach on data acquired and stored on a virtualized data platform. Although, several real time analytical processing issues are identified and further research are suggested with improvements that would have significant effect on improving the sustainability and efficiency of aircrafts.
This will bring us closer to a world of no unplanned downtime, no maintenance related delays and cancellations, and no aircraft stranded on the ground for mechanical failures. It will dramatically improve capacity utilization, and reduce the time we lose today by performing preventive maintenance and servicing for lack of information on the actual status of the assets.
As the industry becomes more comfortable with intelligently monitoring and analyzing equipment in order to determine the need for repair or replacement, there is an opportunity to move away from traditional preventative maintenance and shift to predictive maintenance. With predictive maintenance, significant reductions in unplanned downtime can save millions, keep planes flying and keep customers happy.
#PdM #turbofanengine #ml #ai
Think about all the machines you use during a year, all of them, from a toaster every morning to an airplane every summer holiday. Now imagine that, from now on, one of them would fail every day. What impact would that have? The truth is that we are surrounded by machines that make our life easier, but we also get more and more dependent on them. Therefore, the quality of a machine is not only based on how useful and efficient it is, but also on how reliable it is. And together with reliability comes maintenance.
What if an aircraft part could tell you when it needs to be repaired or replaced? With continuous data collection, monitoring and application of advanced analytics, it can.
In Aviation big data analytics has promoted the viability for performance optimization of aircrafts through predictive maintenance at a cheaper and effective manner for the airline industry, also providing operational and financial advantages over limited infrastructural or operational modifications.
Hence, the data analytic platform based on existing infrastructure in both aircraft and information technology had been discussed to provide significant support on real-time ground and air-borne decision making utilizing the conventional data analytics techniques such as regression, association rule, decision trees etc. in a machine learning environment approach on data acquired and stored on a virtualized data platform. Although, several real time analytical processing issues are identified and further research are suggested with improvements that would have significant effect on improving the sustainability and efficiency of aircrafts.
This will bring us closer to a world of no unplanned downtime, no maintenance related delays and cancellations, and no aircraft stranded on the ground for mechanical failures. It will dramatically improve capacity utilization, and reduce the time we lose today by performing preventive maintenance and servicing for lack of information on the actual status of the assets.
As the industry becomes more comfortable with intelligently monitoring and analyzing equipment in order to determine the need for repair or replacement, there is an opportunity to move away from traditional preventative maintenance and shift to predictive maintenance. With predictive maintenance, significant reductions in unplanned downtime can save millions, keep planes flying and keep customers happy.
#PdM #turbofanengine #ml #ai
