Towards predictive maintenance of hydrogen pressure vessels based on multi-sensor data

Abstract

In this paper, we report on a sensor network for structural health monitoring (SHM) of Type IV composite overwrapped pressure vessels (COPVs) designed for hydrogen storage. The sensor network consists of three different SHM sensing technologies: ultrasonic guided waves (GW), acoustic emission (AE) testing, and distributed fiber optic sensors (DFOS). We present an experimental setup for a lifetime test, where a COPV is subjected to cyclic loading. Data from all sensors are collected and centrally evaluated. The COPV failed after approximately 60,000 load cycles, and the sensor network proved capable of detecting and localizing the damage even before the failure of the COPV. This multi-sensor approach offers significantly more channels of information and could therefore enable a transition from costly and time-consuming periodic inspections to more efficient and modern predictive maintenance strategies, including artificial intelligence (AI)-based evaluation. This not only has a positive effect on operational costs but enhances safety through the early identification of critical conditions in the overall system in real-time. In the future, we aim to integrate the measurement setup into a hydrogen refueling station with the data stream implemented into a digital signal processing chain and a digital twin.