Biblio
This paper presents an analytical method for predicting the electromagnetic performance in permanent magnet (PM) machine with the spoke-type rotor (STR) and a proposed hybrid rotor structure (HRS), respectively. The key of this method is to combine magnetic field analysis model (MFAM) with the magnetic equivalent circuit model. The influence of the irregular PM shape is considered by the segmentation calculation. To obtain the boundary condition in the MFAM, respectively, two equivalent methods on the rotor side are proposed. In the STR, the average flux density of the rotor core outer-surface is calculated to solve the Laplace's equation with considering for the rotor core outer-surface eccentric. In the HRS, based on the Thevenin's theorem, the equivalent parameters of PM remanence BreB and thickness hpme are obtained as a given condition, which can be utilized to compute the air-gap flux density by conventional classic magnetic field analysis model of surface-mounted PMs with air-gap region. Finally, the proposed analytical models are verified by the finite element analysis (FEA) with comparisons of the air-gap flux density, flux linkage, back-EMF and electromagnetic torque, respectively. Furthermore, the performance that the machine with the proposed hybrid structure rotor can improve the torque density as explained.
When supporting commercial or defense systems, a perennial challenge is providing effective test and diagnosis strategies to minimize downtime, thereby maximizing system availability. Potentially one of the most effective ways to maximize downtime is to be able to detect and isolate as many faults in a system at one time as possible. This is referred to as the "multiple-fault diagnosis" problem. While several tools have been developed over the years to assist in performing multiple-fault diagnosis, considerable work remains to provide the best diagnosis possible. Recently, a new model for evolutionary computation has been developed called the "Factored Evolutionary Algorithm" (FEA). In this paper, we combine our prior work in deriving diagnostic Bayesian networks from static fault isolation manuals and fault trees with the FEA strategy to perform abductive inference as a way of addressing the multiple-fault diagnosis problem. We demonstrate the effectiveness of this approach on several networks derived from existing, real-world FIMs.