PHM System with Comprehensive Data Analytics to Provide Localized Reasoning for a Failure Prediction

Aircraft system contains components to achieve system functionality, monitor its health and record the health status. The recording health status data provides useful information that will led to the root cause of a system failure. However, the maintenance activities are performed in isolation based on the routine procedure and manual approach. Hence critical system maintenance is ignored because of failure to identify the correct system component causing system health deterioration. Today data analytics are effectively used for predictive health management. With the large amount of data collected in the avionics systems it is difficult finalize the list of the parameters that might be critical for data analytics and prognostic health monitoring. This synopsis provide insight into identifying the parameters impacting the system failure using data analytics modelling based on the characteristic of a system and finalize the critical parameters for prognostic health monitoring based on the reasoning model. The goal of the paper is to provide a method to achieve prognostic health monitoring of the system using data analytics that includes model to identify the data set from a larger data set, define the prediction model to detect the health of system and identify the reasoning of the failure or failure prediction.