Fusion and decision making techniques for structural prognostic health management

There is a growing interest in fusing the wealth of data generated by aircraft sensors and airborne systems into prognostic information and automated decisions that lead to significant improvements in aircraft management, affordability, availability, airworthiness and performance (MAAAP). Over the past five years, Smiths and BAE SYSTEMS have launched collaborative work to evolve a certifiable practical system that addresses this interest. The collaborative work has built on the BAE SYSTEMS vast advanced technology experiences and on the Smiths unique experience that has produced prognostic and decision support algorithms combining model-based and artificial intelligence (AI) techniques. This paper reports on certifiable techniques that fuse flight data into prognostic information. The techniques were blind tested on legacy data covering 15 years of military operations. The paper presents a fusion/decision support technique targeted at identifying sensor/system faults. The paper also presents preliminary collaborative work on a structural damage detection technology where AI and fusion methods have been used to mitigate technology risks