International Journal of Intelligent Engineering and Systems

8

Authors: Jie Li, Hexu Sun, Zhaoming Lei

SRM has become an attractive alternative in adjustable speed drive due to its simplicity in both motor construction and power converter, and vibration that causes acoustic noise in SRM have generated intense interest from the time it became commercially competitive with dc and ac drives. Basically, there are two conventional ways to reduce the noise: optimize the physical structure of the motor, or, choose appropriate control strategies. In this paper, a new method is proposed to combine these two optimization ways together: harmony optimization of SRM. Physical design is mainly aiming at the back iron thickness and the air gap length of the motor. Control optimization is about the control mode, turn-on and the turn-off angle which affects the power factor strongly. Based on the same cooper losses, the mathematics analysis and finite element simulation are carried out. The results show that the harmony optimization will reduce the vibration and torque ripple in a large extent without extra power losses.

8

Authors: Peng Yang, Shujuan Zhu

The electric care bed control system is divided into three modules, which are physiologic parameter detection module, human-computer interaction module and motor drive module (MDM). Those modules communicate with each other by controller area network (CAN) bus. Under the idea of modular design, a plug and play (PnP) multifunctional motor drive module (MDM) is developed. This MDM has a CAN interface which can make it possible to communicate with human-computer interaction module, and then control the position closed-loop and speed closedloop. The MDM based on CAN bus will be more efficient in communication and system expansion and make the electric care bed function perfectly.

8

Authors: Yang Yu, Chao Han

Shield is a typical mechanical, electrical, hydraulic integration of equipment. Its faults species are complex and diverse. To prevent because machine failure causes economic losses and casualties by shield, this article will introduces rough set theory to the subway shield machine fault diagnosis, propose a method which is based on rough set theory combined with neural network of Metro shield machine fault diagnosis. Use the strong advantage of rough sets theory in attribute reduction, and remove the data redundancy of information which is not effective for decisionmaking. Application of neural network algorithm to reduce date for diagnosis, the method can effectively improve the speed and accuracy of the diagnosis. Then use BP neural network combined with least square method to forecast fault, the least-square can reflect the trend of linear sequence, Neural network can seize the variation of nonlinear time series, therefore the combination of two methods could well predict the future of unit operating conditions.