Towards Autonomous PHM: An Application to Turboshaft Engine Torque Prediction

Autonomous Prognostics and Health Management (Autonomous PHM) refers to the capability of a system to independently monitor, diagnose, predict, and manage its own health status without human intervention. It combines traditional PHM functions with autonomy and intelligent decision-making to enable self-sustaining operation, especially in complex or remote environments. The key characteristics of an autonomous PHM system include: (1) self-monitoring: continuous collection and analysis of sensor data to assess system health in real time; (2) self-diagnosis: identification of faults, anomalies, or degradations using AI, machine learning, or model-based reasoning; (3) self-prognosis: prediction of remaining useful life (RUL) or time to failure based on current and historical data; (4) autonomous decision-making: autonomous selection and execution of maintenance or mitigation actions (e.g., reconfiguration, load reduction); (5) adaptability: adapt pre-trained models (e.g., for fault detection or RUL estimation) from one system or component to another with limited new data; (6) minimal human oversight: designed to function reliably with little to no manual input, particularly useful in inaccessible or high-risk settings (e.g., space missions, underwater robotics, military systems). A few challenges remain for developing an effective autonomous PHM system: (1) learning with limited labeled data: limited availability of failure data for training ML models; (2) cross-platform autonomy: autonomous PHM systems often operate in varied conditions or on different equipment types. PHM functions should be adapted from one system or component to another to reduce the need to retrain models from scratch in every new setting. (3) scalability: autonomous PHM systems should scale to large, complex systems (e.g., fleets of aircraft or satellites). A model trained on one unit can be transferred to other units in the fleet to scale autonomous PHM capabilities efficiently. In this paper, the development of an autonomous PHM system by integrating self-supervised learning and large language models (LLMs) is presented. The effectiveness of the autonomous PHM system is demonstrated with an application to turboshaft engine torque prediction.