-Cogging torque has a negative impact on the operation of permanent magnet machines by increasing torque ripple, speed ripple, acoustic noise and vibration. In this paper Magnet Shifting Method has been used as a tool to reduce the cogging torque in inset Line Start Permanent Magnet Synchronous Motor (LSPMSM). It has been shown that Magnet Shifting Method can effectively eliminate several lower-order harmonics of cogging torque. In order to implement the method, first the expression of cogging torque is studied based on the Fourier analysis. An analytical expression is then introduced based on Permanent Magnet Shifting to reduce cogging torque of LSPMS motors. The method is applied to some existing machine designs and their performances are obtained using Finite Element Analysis (FEA). The effect of magnet shifting on pole mmf (magneto motive force) distribution in air gap is discussed. The side effects of magnet shifting on back-EMF, core losses and torque profile distortion are taken into account in this investigation. Finally the experimental results on two prototypes 24 slot 4 pole inset LSPMS motors have been used to validate the theoretical analysis.
This paper is a review and evaluation of different on-line methods for diagnosing winding fault in induction motors presented in literature. Many methods can be found in literature; in some references, frequency analysis of motor signals such as current , speed, instantaneous power , Park's vector modulus and so on are introduced. Evaluation of negative sequence of current is also among the proposed methods. Supply voltage unbalance and some other phenomena may be confused with the winding fault. Therefore, the monitoring and fault detection of electrical machines have moved in recent years from traditional techniques to artificial intelligence based techniques. There are many other techniques that will be introduced and investigated in the detailed paper. A comparison of Different methods introduced in literature is the main objective of this paper.
Bearing is an important part of electric machines. In order to avoid unscheduled outputs, it is important to detect an upcoming fault as soon as possible. Since fault in a great number of bearings commences from a single point defect, research on this category of faults has shared a great deal in predictive diagnosis literature. Single point defects will cause certain characteristic frequencies to appear in machine vibration spectrum. Because of impulsive nature of fault strikes, and complex modulations present in vibration signal, a simple spectrum analysis may result in erroneous conclusions.When a shaft rotates at constant speed, strikes due to a single point defect repeat at constant intervals. Each strike shows a high energy distribution around it. The time intervals between successive impulses in auto-correlated vibration power signals are related to fault frequency and therefore show the defective part.According to the proposed method, an apparatus is designed. Introducing the structure of this apparatus, the circuits, hardware, and software is the main objective of this paper. The apparatus has shown its effectiveness in laboratory experiments and field tests.
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