fault detection analytics

In a previous article we have seen how Condition Based Maintenance (CBM) helps to identify problems before they arise. In this article we will see how fault detection analytics on vibration data can help in saving millions for the aviation industry.         

Vibration sensors, commonly used in aircraft vibration analyzers by ground crew, can be installed at multiple important locations on an aircraft to provide continuous and real time data. Telematics or wireless technologies will allow transmitting this data to a central data management portal to enable accumulating real time operational data. This data then becomes a gold mine of information for a variety of applications. After analyzing this vibration data, a trend analysis of vibration patterns can provide alerts and enable

  • Fault detection analytics or predicting part failure with good reliability estimates
  • Preventing unwanted maintenance schedules
  • Prevent component replacement by sending it for service etc.
fault detection analytics on vibration data

Vibration analyzers will necessarily generate “Big Data”, but by applying various filters at multiple threshold points, this data can be made usable and insightful. This vibration data exhibits recongnizable patterns or signatures and can spot trends resulting in potential fault detection and enable preventive maintenance actions.

Let us consider an example, a flight is getting ready for long distance event and a vibration sensor available at one of the bearing is exhibiting somewhat abnormal trends. Machine learning algorithms, would allow us to quickly classify such trends into appropriate categories: needs immediate replacement, needs replacement in xxx hours, or needs maintenance. Depending upon which category this signal falls into, a decision can be made. For example, if the classification was needs replacement in xxx hours, the cost of replacing the bearing before the start of flight, could be weighed against the risks of delayed maintenane. Or taking a conservative approach replace the part before the take-off to completely avoid risks of component failure.    

Following are various factors which contribute to saving and increasing the life of aircraft.

  1. Detect and prevent Engine over-speed or over-torque
  2. Increased readiness of flights by predicting maintenance beforehand
  3. Preventing accidents
  4. Component not replaced but sent to service centers before failure
  5. Doing maintenance ahead of schedule based on condition based monitoring

All these factors would increase the life of aircraft, prevent losses and most importantly, reduce the chances of equipment failures during operation. These objectives can be easily realized by analyzing real time and historical data from the aircraft sensors and applying fault detection analytics to enable preventive maintenance. 

Originally posted on Thu, Jan 16, 2014 @ 07:24 AM

No responses yet

Leave a Reply

This site uses Akismet to reduce spam. Learn how your comment data is processed.

simafore.ai