Enhancing PHM Functionality by Using Data Sharing Standards

Abstract

Nearly everything related to Prognostics and Health Management (PHM) in aviation is dependent on data: its generation, its transmission, its storage and its access and use. In each of these areas, many standards exist related to the underlying technologies, but this is not the case when it comes to standards for data governance. In this brief paper, we address the gaps in the landscape on data governance, especially those related to data sharing. This will be critical when artificial intelligence and in particular machine learning becomes more widespread in developing and maintaining PHM algorithms. We describe how aerospace consortia may be able to help fill some of these gaps. The path forward in this highly connected world will only be possible if strict rules exist for all entities within the ecosystem to be able to share data and thereby achieve common benefits no single organization can achieve on its own. In particular, we describe the role of the Independent Data Consortium for Aviation (IDCA) that has been established to do just this. Some examples of areas within aviation where governance standards can be critical are parts data tracking, AOG (aircraft on ground) situations, diagnostics and prognostics for aircraft systems, supporting the use of artificial intelligence (AI) and machine learning (ML). We show how issues can be resolved faster and more efficiently if data governance standards exist that are agreed to by all stakeholders. Additionally, mechanisms for ensuring trustworthiness would be critical in allowing automated enforcement and accounting. Here we are reporting on the progress we are making in developing these standards.