About the Project
Nowadays, the smart system becomes more and more complex both in structure and functional logics. It has been shown that the reliability analysis and fault diagnosis are challenging. The dependent states and the events make the fault isolation more difficult and the cost increases dramatically dealing with the weak component in the system. The optimisation can be achieved effectively once the reliability and diagnosis can be analysed in real-time. Note that most of the current methods gave the probability for the reliability and diagnosis which means that the causing reasons hide behind.
Motivated by explainable AI (XAI), the fault diagnosis and reliability for dynamic smart system can be designed where the AI mechanism would explain the fault at multi-operational point. In particular, random forest and multi-layer CNN can be adopted for reliability analysis and fault diagnosis, and the optimisation can be implemented with probability density function shaping since the reliability can be described as a distribution model. In addition, due the limited data, the measurement distribution must be non-Gaussian which results in difficulty to optimise the performance as most of the existing optimisation results are based on Gaussian assumption.
The following 3-year schedule has been made for completing the project as a PhD research programme. In year one, the literature reviews for the existing explainable AI methods and reliability analysis. Developing basic fault diagnosis methods for dynamics systems. In the second year, the novel XAI framework considering the uncertainties and randomness will be developed in the second year in order to achieve the fault diagnosis and the stochastic distribution optimisation will be developed for the system optimisation in terms of reliability. In the third year, the presented AI hybrid model and algorithms will be validated by simulation and experiment at automotive pilot system at University of Bradford.
Došilović, Filip Karlo, Mario Brčić, and Nikica Hlupić. "Explainable artificial intelligence: A survey." 2018 41st International convention on information and communication technology, electronics and microelectronics (MIPRO). IEEE, 2018.
Xing, Liudong, Gregory Levitin, and Chaonan Wang. Dynamic System Reliability: Modeling and Analysis of Dynamic and Dependent Behaviors. John Wiley & Sons, 2019.
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