A novel adaptive fault detection methodology for complex system using deep belief networks and multiple models: A case study on cryogenic propellant loading system

Abstract

Abstract A novel methodology based deep belief networks and multiple models (DBNs-MMs) is presented to accomplish fault detection for complex systems. And firstly, historical datasets are collected and processed to train the DBNs, so that DBNs can be constructed to learn the nonlinear dynamic characteristics of complex system, and so a model with a specific architecture and some initial intelligence will be built up. Secondly, the operation condition and real-time measurement data are employed in DBNs-MMs to get a series of network outputs. Finally, the residuals can be obtained by comparing the measurement output and each DBN output. Then, the fault detection can be achieved by employing a properly adaptive threshold for each residual. Some faulty cases of the complex cryogenic propellant loading system have been used to demonstrate the effectiveness of the proposed fault detection methodology, and the result has shown its excellent performance.