Health Management Control Strategy of Tank Storage Based on Artificial Intelligence

Abstract

For the safe usage of missile fuel tank in a long-term preservation process, the health status of the storage tank must be evaluated and managed accurately. Currently, the health management strategy has gradually evolved from Time-based maintenance (TBM) to Preventive maintenance (PM). With artificial intelligence (AI) applied to process the big data, the strategy of tank storage health management is now able to make precis predictions and guidance. The basic data is acquired from various databases, and the prediction of the structural performance of the storage tank system is accomplished by a series of simulation models intelligently. The modules include data fused long storage evaluation module, corrosion depth prediction module, elastic modulus drop prediction module, and creep damage analysis module. With on-site monitoring of data, a decision tree model based on artificial intelligence is constructed to provide decision support for the use of the missile propellant tank, leading to a more effective, time-saving, and accurate control strategy.