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Relevance Vector Machine (RVM) with the uncertainty expressing ability has spawned broad applications in Prognostic and Health Management (PHM). However computationally intensive intrinsic nature of RVM greatly limits its usage. This paper presents a software and hardware co-design approach based on HMPSoC technology, which efficiently exploited sequential and parallel nature of RVM. Multi-channel and pipelined hardware architecture for the acceleration of kernel formulation and intermediate values calculation is proposed. The hardware that wrapped with AXI-Stream interface is integrated into HMPSoC as an acceleration engine. We implement the design on an on-board PHM prototype platform with a Xilinx Zynq XC7Z020 AP SoC. The experiment results show 5.3× and 46.8× speed up in terms of the time cost than the RVM running on PC with a Xeon 5620 processor and ARM Cortex A9 processor. The energy consumption is reduced by 153.0× and 37.3×, respectively. 978-1-4799-6245-7/14/$31.00 ©2014 IEEE

The paper first introduces the principles of artificial intelligence and expert systems, and makes a detailed analysis of the application of expert systems in power systems. Secondly, the paper uses VC++ language to establish an expert system for judging power system fault areas and fault types. The system can identify power system bus faults, line faults, circuit breakers and relay malfunctions and refusal faults. Finally, the system simulation test shows that the fault diagnosis expert system established in this paper has certain theoretical and practical significance.