Kernel flexible and displaceable convex hull based tensor machine for gearbox fault intelligent diagnosis with multi-source signals

Measurement ◽  
2020 ◽  
Vol 163 ◽  
pp. 107965
Author(s):  
Zhiyi He ◽  
Haidong Shao ◽  
Junsheng Cheng ◽  
Yu Yang ◽  
Jiawei Xiang
1989 ◽  
Vol 136 (6) ◽  
pp. 530
Author(s):  
G.R. Wilson ◽  
B.G. Batchelor
Keyword(s):  

2020 ◽  
Vol 68 (5) ◽  
pp. 358-366
Author(s):  
H.E. Oh ◽  
W.B. Jeong ◽  
C. Hong

When multiple sources contribute competitively to the noise level, multi-channel control architecture is needed, leading to more cost and time for control computation. We, hence, are concerned with a single-channel control method with a single-reference signal obtained from a linear combination of the multiple source signals. First, we selected 3 source signal sensors for the reference signals and the error sensor, selected a proper actuator and designed the controllers: 3 cases of single-channel feedforward controllers with a single-reference signal respectively from the source signals, a multi-channel feedforward controller with the reference signals from the source signals, and the proposed controller with the reference signal from weighted sum of the source signals. The weighting factors and the filter coefficients of the controller were determined by the FxLMS algorithm. An experiment was then performed to confirm the effectiveness of the proposed method comparing the control performance with other methods for a tower air conditioner. The overall sound pressure level (SPL) detected by the error sensor is compared to evaluate their performance. The reduction in the overall SPL was obtained by 4.74 dB, 1.96 dB and 6.62 dB, respectively, when using each of the 3 reference signals. Also, the overall SPL was reduced by 7.12 dB when using the multi-reference controller and by 7.66 dB when using the proposed controller. Conclusively, under the multiple source contribution, a single-channel feed forward controller with the reference signal from a weighted sum of the source signals works well with lower cost than multi-channel feedforward controller.


2019 ◽  
Vol 31 (5) ◽  
pp. 761
Author(s):  
Xiao Lin ◽  
Zuxiang Liu ◽  
Xiaomei Zheng ◽  
Jifeng Huang ◽  
Lizhuang Ma

2000 ◽  
Author(s):  
Lisa A. Pflug ◽  
George B. Smith ◽  
Michael K. Broadhead

2019 ◽  
Vol 82 ◽  
pp. 16-31 ◽  
Author(s):  
Jie Xue ◽  
Yuan Li ◽  
Ravi Janardan
Keyword(s):  

Author(s):  
Bochang Zou ◽  
Huadong Qiu ◽  
Yufeng Lu

The detection of spherical targets in workpiece shape clustering and fruit classification tasks is challenging. Spherical targets produce low detection accuracy in complex fields, and single-feature processing cannot accurately recognize spheres. Therefore, a novel spherical descriptor (SD) for contour fitting and convex hull processing is proposed. The SD achieves image de-noising by combining flooding processing and morphological operations. The number of polygon-fitted edges is obtained by convex hull processing based on contour extraction and fitting, and two RGB images of the same group of objects are obtained from different directions. The two fitted edges of the same target object obtained at two RGB images are extracted to form a two-dimensional array. The target object is defined as a sphere if the two values of the array are greater than a custom threshold. The first classification result is obtained by an improved K-NN algorithm. Circle detection is then performed on the results using improved Hough circle detection. We abbreviate it as a new Hough transform sphere descriptor (HSD). Experiments demonstrate that recognition of spherical objects is obtained with 98.8% accuracy. Therefore, experimental results show that our method is compared with other latest methods, HSD has higher identification accuracy than other methods.


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