Prognostic Diagnosis of Ball Screw Preload Loss for Machine Tool through the Hilbert-Huang Transform and Multiscale Entropy Measure

現代製造工程於診斷工具機滾珠導螺桿的預壓力失效故障有極大意義，本論文目的欲藉由希伯特黃轉換及多尺度熵方法診斷滾珠導螺桿預壓力失效，診斷方法為擷取工具機運作時的馬達電流訊號，而滾珠導螺桿之最大動態預壓2%、4%、6%已預先設計及製造，並進行實驗，訊號樣本利用經驗模態分解與希爾伯特頻譜來討論及展現，不同預壓特徵使用希伯特黃轉換擷取及分析，多尺度熵已可確認及分離不規則及複雜之滾珠導螺桿預壓力失效訊號，實驗數據成功地顯示，提出的方法已可預先了解滾珠導螺桿預壓力失效的狀況，此智慧型感測器可藉由訊號處理及EMD、HHT樣本匹配，再根據MES比對評估滾珠導螺桿健康，此診斷方法實現目的為方便預先了解滾珠導螺桿預壓力失效狀況。

關鍵字

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並列摘要

Detection of ball screw preload loss for machine tool has great significances in modern manufacturing processes for unexpected failures. This paper proposes a diagnostic method for ball screw preload loss through the Hilbert-Huang transform (HHT) and multiscale entropy(MSE) process. The method is used to diagnose a ball screw preload loss through the motor torque current signals when machine tool is in operation. Maximum dynamic preloads of 2%, 4%, and 6% ball screws were predesigned, manufactured, and conducted experimentally. Signal patterns are discussed and revealed by Empirical Mode Decomposition (EMD) with the Hilbert Spectrum. Different preload features are extracted and discriminated using HHT. The irregularity development of ball screw with preload loss is determined and abstracted via MSE based on complexity perception. The experiment results successfully show that the prognostic status of ball screw preload loss can be envisaged by the proposed methodology. The smart sensing for the health of the ball screw is available based on comparative evaluation of MSE by the signal processing and pattern matching of EMD/HHT. This diagnosis method realizes the purposes of prognostic effectiveness on knowing the preload loss and utilizing convenience.

並列關鍵字

Ball screw preload loss ； empirical mode decomposition ； Hilbert-Huang transform ； multiscale entropy

國際替代計量

Prognostic Diagnosis of Ball Screw Preload Loss for Machine Tool through the Hilbert-Huang Transform and Multiscale Entropy Measure

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