Fault detection and optimal feature selection in automobile motor-head machining process

Reséndiz‐Flores, Edgar O.; Navarro-Acosta, Jesús Alejandro; Mota-Gutiérrez, Cecilia G.

doi:10.1007/s00170-017-1136-x

Cited by 7 publications

(2 citation statements)

References 19 publications

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“…If the normalized data have five samples with five values of the Mˆi, a total of 5 estimation output (M i ) is generated then for each level computation. Once the values of (M i ) and (M i ) are specified, Equation (11) until Equation ( 16) are used to compute the dynamic SNR of each level computation. For the final optimization result, if the average value of SNR for 1 (used) is significantly higher compared to 2 (excluded) in each variables analysis, it shows that the contribution of that variable is relatively significant.…”

Section: Mˆi=mentioning

confidence: 99%

“…Several other studies whether within MTS or beyond the MTS nature, also highlighted that Binary PSO converges faster compared to other algorithms. Up until recently, many BPSO algorithms have been progressively enhanced and proven to provide better accuracy [11][12][13][14][15]. In the area of the T-Method, BPSO has not been explored yet, and it serves as a motivation for this research study to be conducted.…”

Section: Introductionmentioning

confidence: 99%

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Binary Particle Swarm Optimization for Variables Selection Optimization in Taguchi’s T-Method

et al. 2020

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Prediction analysis has drawn significant interest in numerous field. Taguchi’s T-Method is a prediction tool that developed practically but not limited to small sample analysis. It was developed explicitly for multidimensional system prediction by relying on historical data as the baseline model and adapting the signal to noise ratio (SNR) as well as zero proportional concepts in strengthening its robustness. Orthogonal array (OA) in T-Method is a variable selection optimization technique in improving the prediction accuracy as well as help in eliminating variables that may deteriorate the overall performance. However, the limitation of OA in dealing with higher multidimensionality restraint the optimization accuracy. Binary particle swarm optimization used in this study helps to cater to the limitation of OA as well as optimizing the variable selection process to better prediction accuracy. The results show that if the historical data consist of samples with higher correlation of determination (R2) value for the model creation, the optimization process in reducing the number of variables would be much reliable and accurate. Comparing between T-Method+OA and T-Method+BPSO in four different case study, it shows that T-Method+BPSO performing better with greater R2 and means relative error (MRE) value compared to T-Method+OA.

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Section: Mˆi=mentioning

confidence: 99%