scholarly journals Sensorless Posture Detection of Reluctance Spherical Motor Based on Mutual Inductance Voltage

2021 ◽  
Vol 11 (8) ◽  
pp. 3515
Author(s):  
Jiazi Xu ◽  
Qunjing Wang ◽  
Guoli Li ◽  
Rui Zhou ◽  
Yan Wen ◽  
...  
Keyword(s):  
Detection Method ◽  
Position Angle ◽  
Mutual Inductance ◽  
Spherical Motor ◽  
Position Detection ◽  
Population Standard Deviation ◽  
Rotor Pole ◽  
On Line ◽  
Motor Structure ◽  
Detection Effect

In this paper, a sensorless rotor posture detection method based on the mutual inductance voltage of the stator coil is proposed to simplify the position detection element of a reluctance spherical motor. Firstly, the numerical relationship between the stator/rotor pole misalignment angle and the mutual inductance voltage of the stator coil is analyzed, which is used as the basis for judging the spatial position of the rotor. Secondly, an experimental platform is designed to verify the consistency between the calculated value and the experimental value of the mutual inductance voltage and to determine the appropriate excitation signal. Thirdly, based on the real-time voltages generated by the stator coil mutual inductance, an intelligent algorithm is used to invert the 3-DoF (degree-of-freedom) position angle of the spherical rotor combined with the motor structure constraints. The experimental results show that the detection method has a good on-line detection effect, and the population standard deviation is within 1.8° Therefore, the developed technique can be used for replacing the position detection method with sensors.

Research Situation Analysis of On-line Detection Technology for Power Transformer Winding Deformation

2020 ◽  
Vol 13 (6) ◽  
pp. 823-832
Author(s):  
Zhenhua Li ◽  
Weihui Jiang ◽  
Li Qiu ◽  
Zhenxing Li ◽  
Yanchun Xu
Keyword(s):  
Detection Method ◽  
Theoretical Research ◽  
Line Detection ◽  
Response Analysis ◽  
Advantages And Disadvantages ◽  
Detection Technology ◽  
The Future ◽  
Transformer Winding ◽  
On Line ◽  
Winding Deformation

Background: Winding deformation is one of the most common faults in power transformers, which seriously threatens the safe operation of transformers. In order to discover the hidden trouble of transformer in time, it is of great significance to actively carry out the research of transformer winding deformation detection technology. Methods: In this paper, several methods of winding deformation detection with on-line detection prospects are summarized. The principles and characteristics of each method are analyzed, and the advantages and disadvantages of each method as well as the future research directions are expounded. Finally, aiming at the existing problems, the development direction of detection method for winding deformation in the future is prospected. Results: The on-line frequency response analysis method is still immature, and the vibration detection method is still in the theoretical research stage. Conclusion: The ΔV − I1 locus method provides a new direction for on-line detection of transformer winding deformation faults, which has certain application prospects and practical engineering value.

Defect detection method for rail surface based on line-structured light

Measurement ◽  
2020 ◽  
Vol 159 ◽  
pp. 107771 ◽  
Author(s):  
Xiaohui Cao ◽  
Wen Xie ◽  
Siddiqui Muneeb Ahmed ◽  
Cun Rong Li
Keyword(s):  
Defect Detection ◽  
Detection Method ◽  
Structured Light ◽  
On Line

Outlier Detection for Soft-Sensor Modeling Data Based on k-Nearest Neighbor

2012 ◽  
Vol 468-471 ◽  
pp. 2504-2509
Author(s):  
Qiang Da Yang ◽  
Zhen Quan Liu
Keyword(s):  
Outlier Detection ◽  
Nearest Neighbor ◽  
Detection Method ◽  
Soft Sensor ◽  
K Nearest Neighbor ◽  
Sensor Model ◽  
Sensor Technique ◽  
On Line ◽  
Modeling Data ◽  
Sensor Modeling

The on-line estimation of some key hard-to-measure process variables by using soft-sensor technique has received extensive concern in industrial production process. The precision of on-line estimation is closely related to the accuracy of soft-sensor model, while the accuracy of soft-sensor model depends strongly on the accuracy of modeling data. Aiming at the special character of the definition for outliers in soft-sensor modeling process, an outlier detection method based on k-nearest neighbor (k-NN) is proposed in this paper. The proposed method can be realized conveniently from data without priori knowledge and assumption of the process. The simulation result and practical application show that the proposed outlier detection method based on k-NN has good detection effect and high application value.

A simple excitation position detection method for sensorless SRM drive

2007 ◽  
Author(s):  
Joanna Bekiesch ◽  
Gunter Schroder ◽  
Tae-Hyoung Kim ◽  
Jin-Woo Ahn
Keyword(s):  
Detection Method ◽  
Position Detection
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