@article{Huang_Zheng_2018, title={Ball Nut Preload Diagnosis of the Hollow Ball Screw through Support Vector Machine}, volume={3}, url={https://ojs.imeti.org/index.php/AITI/article/view/1031}, abstractNote={<p class="Abstract"><strong><span lang="EN-US">T</span></strong><span lang="EN-US">his paper studies the diagnostic results of hollow ball screws with different ball nut preload through the support vector machine (SVM) process. The method is testified by considering the use of ball screw pretension</span><span lang="EN-US"> and different ball nut preload</span><span lang="EN-US">. SVM was used to discriminate the hollow ball screw preload status through the </span><span lang="EN-US">vibration signals and </span><span lang="EN-US">servo motor current signals. Maximum dynamic preloads of 2%, 4%, and 6% ball screws were predesigned, manufactured, and conducted experimentally. Signal patterns </span><span lang="EN-US">with d</span><span lang="EN-US">ifferent preload features are </span><span lang="EN-US">separated</span><span lang="EN-US">by </span><span lang="EN-US">SVM. The irregularity development of the ball screw driving motion current </span><span lang="EN-US">and rolling balls vibration of the ball screw </span><span lang="EN-US">can be discriminated via SVM based on complexity perception. The experimental results successfully show that the prognostic status of ball nut preload can be envisaged by the proposed methodology. The smart reasoning for the health of the ball screw is available based on classification of SVM. This diagnostic method satisfies the purposes of prognostic effectiveness on knowing the ball nut preload status</span></p>}, number={2}, journal={Advances in Technology Innovation}, author={Huang, Yi-Cheng and Zheng, Jing-Hong}, year={2018}, month={Feb.}, pages={94–99} }