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Use Case: Machine Learning for Predictive Maintenance in Aviation
Machine learning has become a critical element of aviation predictive maintenance systems. Its intelligent algorithms can process large volumes of disparate data, filtering out unnecessary data points to create an accurate snapshot of individual aircraft components. This capability provides an efficiency to multiple condition based monitoring and predictive maintenance processes.
Machine Learning Learns from Aircraft Maintenance Data
Machine learning for predictive maintenance in aviation uses data from various sources, such as flight data recorders and logbooks. It also relies heavily on Big Data gathered through condition-based monitoring and predictive maintenance solutions like ScoutCam’s image-based AI platform.
For example, the use of ScoutCam’s Camera-as-a Sensor™ solution facilitates the regular inspections mandated by governing authorities. The health of aircraft engines can be assessed using camera modules that are stabilized for the effective monitoring of rotating components while the engine is in operation. Also, within the engine environment, the condition of belts can be closely inspected for indications of wear or impending failure. For crankshaft monitoring, ScoutCam’s technology uses machine learning models to determine the thread quantity and area size. The micro-visualization solution is also used to help ensure that critical landing gear components are operating properly, providing the visibility needed to determine if there are shock absorber leaks.
The machine learning models are able to efficiently identify anomalies that would otherwise be difficult or impossible to detect by humans. This capability makes machine learning a necessity for multiple applications in aviation predictive maintenance. These machine learning use cases can improve aircraft uptime and safety, maximizing the quantity of aircraft flights aircraft can take before they have to undergo repairs. They can also increase aircraft equipment liability while lessening the workload of maintenance engineers.
Substitution for Human Visual Inspection
When performed manually, the visual inspection of aircraft can be time-consuming, extremely labor-intensive and prone to error. It can also be an extremely hazardous task, with maintenance engineers having to access parts of an aircraft that are in extreme conditions. However, one of the most important advantages of solutions that use machine learning is that they are able to make human-oriented processes much more efficient.
ScoutCam’s image-based AI platform is able to execute the visual inspection tasks required during pre-flight and post-flight checks and damage assessments with visualization capabilities impossible with the human eye. It provides industry-changing advantages, such as a significant reduction in inspection times, improved human safety, a reduction in human error, more efficient use of human resources and more efficient and accurate aircraft maintenance processes. It helps ensure that maintenance engineers are able to obtain the visual data necessary to accurately determine the condition of aircraft.
Immediate, Real-Time Diagnostics and Component Failure Predictions
Machine learning’s deep learning ability enables two very important capabilities: immediate diagnostics and the prediction of component failure.
Immediate, real-time diagnosis is rooted in condition-based monitoring, whose ultimate goal is to examine the functional health of the equipment being monitored. Machine learning’s intelligent algorithms can be programmed to detect unusual patterns in aircraft data that point to operational anomalies, analyzing inconsistencies between the expected and actual behaviors of aircraft components and systems to reveal where discrepancies in aircraft systems occur. The datasets that are analyzed include historical and real-time data, whether the data was gathered from flight data recorders or during condition-based monitoring (such as with ScoutCam’s Camera-as-a-Sensor solution) or from another source. It is important to note that the data also includes contextual data, such as information about weather conditions or interior operating environments. It is with this type of nuanced information that machine learning can not only conduct immediate diagnosis but can also determine the remaining working life of operation-critical components of aircraft.
Automation of Aviation Maintenance Processes
When data analysis indicates that anomalies are present or components are approaching the end of their working life, machine learning algorithms can automate certain aviation maintenance processes, such as the ordering of replacement components to have on hand when needed, the scheduling of specific maintenance tasks and the scheduling of aircraft technicians. Alerts, notifications and reports can be automatically generated when certain conditions arise. The automation of these aviation maintenance-related processes improves maintenance efficiency and resource use.
Better Prioritization of Maintenance Backlog
Aviation maintenance engineers usually have a roster of numerous repairs, inspections and checks that have to be completed to avoid safety issues. However, prioritizing tasks can be difficult. There can be uncertainty and confusion about which tasks should be executed first, resulting in non-urgent tasks being given precedence. This approach contributes to unexpected aircraft breakdowns—and adds even more tasks to the maintenance roster. Machine learning algorithms can prioritize maintenance tasks based on urgency and potential of impact, ensuring that aviation maintenance engineers address the most critical tasks first.
ScoutCam Helps Gather Predictive Maintenance Data for Aviation
Predictive maintenance is the data-driven approach to preventing aircraft failure, utilizing machine learning to improve maintenance tasks. Using visualization technology like ScoutCam’s image-based AI platform to monitor and assess aircraft areas that are difficult to reach or that are in extreme environmental conditions helps maintenance engineers to obtain the data machine learning requires to provide powerful capabilities and actionable insights.
Contact us to learn how our micro-visualization technology can be used for visual maintenance processes to improve the reliability of aircraft components and save money.