本文關(guān)鍵詞：基于表征學(xué)習(xí)的滾珠絲杠副系統(tǒng)狀態(tài)監(jiān)測與性能評估技術(shù)研究　出處：《西南交通大學(xué)》2016年博士論文　論文類型：學(xué)位論文

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【摘要】：滾珠絲杠副系統(tǒng)是一類將旋轉(zhuǎn)運動轉(zhuǎn)換為直線運動的機械傳動裝置,廣泛應(yīng)用于精密機械定位與測量系統(tǒng)。其在運行過程中不可避免的會出現(xiàn)磨損,結(jié)構(gòu)松動等現(xiàn)象。而滾珠絲杠副系統(tǒng)發(fā)生故障或者出現(xiàn)性能退化會嚴(yán)重影響機械設(shè)備的精度與運行安全。因此研究滾珠絲杠副系統(tǒng)的狀態(tài)監(jiān)測與性能評估對于提高制造業(yè)水平,減少企業(yè)經(jīng)濟損失非常重要。滾珠絲杠副系統(tǒng)的振動信號包含了豐富的設(shè)備狀態(tài)與性能信息。但是,采集的振動信號中往往包含有大量的噪聲成分,這對有效信息提取造成了一定困擾。同時,如何有效提取與表達信號中的信息成分也是信號處理領(lǐng)域中的研究熱點與難點。論文結(jié)合表征學(xué)習(xí)在滾珠絲杠副系統(tǒng)狀態(tài)監(jiān)測與性能評估方面展開了深入的研究,具體內(nèi)容如下：(1)以字典學(xué)習(xí)和深度學(xué)習(xí)為代表系統(tǒng)研究了表征學(xué)習(xí)的基本算法與結(jié)構(gòu)。揭示了表征學(xué)習(xí)的內(nèi)涵屬性是不引入先驗知識的情況下從原始信號中學(xué)習(xí)得到其本質(zhì)特征表示。同時,通過圖像信號處理說明了表征學(xué)習(xí)可以學(xué)習(xí)得到信號的基本組成成分,并可廣泛應(yīng)用于信號去噪與分類。(2)研究了基于字典學(xué)習(xí)和稀疏編碼的機械振動信號去噪技術(shù)。針對滾珠絲杠副支撐軸承振動信號的特點,構(gòu)建了由固定字典函數(shù)和軸承振動信號構(gòu)成的復(fù)合異構(gòu)訓(xùn)練樣本集合。探索了振動信號在學(xué)習(xí)字典域內(nèi)稀釋表示進行去噪的方法。提出利用在線字典學(xué)習(xí)來增強算法計算實時性。并通過仿真信號和實驗數(shù)據(jù)驗證了算法的可行性和有效性。(3)設(shè)計了滾珠絲杠副系統(tǒng)絲杠變速工況下的故障定位算法。通過多項式調(diào)頻小波方法,計算出絲杠旋轉(zhuǎn)瞬時頻率。提出了基于幅值閥值和導(dǎo)數(shù)閥值的時域信號異常檢測方法定位絲杠故障時間點�；诮z杠瞬時旋轉(zhuǎn)頻率和故障時間點計算獲得絲杠在不同工況下的故障位置點。通過實驗平臺研究了兩種不同工況下的絲杠多故障點定位。實驗結(jié)果表明,提出的算法可有效判斷絲杠故障并計算出準(zhǔn)確位置,定位誤差小于兩個絲杠導(dǎo)程。(4)研制了滾珠絲杠副系統(tǒng)綜合加速性能退化實驗臺。根據(jù)絲杠壽命公式,分析了影響絲杠運行壽命的因素,設(shè)計了滾珠絲杠副系統(tǒng)絲杠性能退化實驗方案。通過改變運行速度,負(fù)載大小模擬滾珠絲杠副系統(tǒng)的不同運動工況,并采集了不同工況下的振動、扭矩等傳感器信號,為滾珠絲杠副系統(tǒng)的準(zhǔn)確性能評估提供了數(shù)據(jù)保障。(5)提出了基于深度神經(jīng)網(wǎng)絡(luò)的滾珠絲杠副系統(tǒng)性能評估方法。對實驗采集的滾珠絲杠副系統(tǒng)振動信號進行時域、頻域和時頻域特征提取,設(shè)計了頻域譜峭度相關(guān)系數(shù)這一新的頻域特征值,可分辨能力強。深度神經(jīng)網(wǎng)絡(luò)學(xué)習(xí)提取深層次的特征參數(shù)并降低特征維數(shù),提高可識別性。引入去噪深度學(xué)習(xí)算法,將輸入向量中某些位置的值用隨機噪聲代替,增強了模型的魯棒性。(6)基于以上理論和實驗,進行了滾珠絲杠副系統(tǒng)狀態(tài)監(jiān)測與性能評估系統(tǒng)的實用化研究�；赟ocket原理設(shè)計了數(shù)據(jù)采集端與上位機客戶端的數(shù)據(jù)通信協(xié)議,實現(xiàn)了系統(tǒng)的遠(yuǎn)程化操作。研究了基于圖形處理單元的算法并行化加速方案,縮短了模型計算時間�；赪xPython開發(fā)了圖像化顯示界面,便于可視化操作與顯示。本文在滾珠絲杠副系統(tǒng)性能退化實驗,數(shù)據(jù)表征學(xué)習(xí),狀態(tài)監(jiān)測與性能評估等方面進行了深入研究。探索了基于表征學(xué)習(xí)的振動信號處理新方法,提出了一種振動信號自適應(yīng)表達新思路。對滾珠絲杠副系統(tǒng)的狀態(tài)監(jiān)測與性能評估進行了實用化研究,對系統(tǒng)的工業(yè)化推廣起到了積極作用。
[Abstract]:The ball screw system is a kind of rotary motion into linear motion of the mechanical transmission device, is widely used in precision mechanical positioning and measuring system. It is inevitable in the process of operation will be worn, loose structure and so on. And the ball screw system failure or performance degradation will seriously affect the precision and safety operation mechanical equipment. So the research of ball screw system for condition monitoring and performance evaluation to improve the manufacturing level, reduce the economic loss is very important. The vibration signal of the ball screw system includes the equipment status and performance information rich. However, vibration signals often contain a lot of noise, which caused some problems for effective information extraction. At the same time, research how to extract information in the composition and expression of signal is in the field of signal processing With difficulty. Combined with the characterization of learning conducted in-depth research on the ball screw system for condition monitoring and performance evaluation, the specific contents are as follows: (1) the dictionary learning and deep learning of the basic algorithm and structure characterization of learning as the representative system. To reveal the connotation of the attribute Xi is not to introduce prior knowledge in the case of the essential characteristics of the original signal from the learned representation. At the same time, the image signal processing shows the characterization of learning can get basic signal components of learning, and can be widely used in the signal denoising and classification. (2) studied the mechanical vibration signal denoising technology based on dictionary learning and sparse encoding. According to the characteristics of the ball screw bearing vibration signal, to construct a complex heterogeneous training sample consists of fixed dictionary function and the bearing vibration signal collection of vibration signal in the study are discussed. The dictionary learning domain representation method for denoising dilution. Put forward to enhance the learning algorithm in real-time using the online dictionary. And the feasibility and effectiveness of the algorithm is verified by simulation signals and experimental data. (3) the fault location algorithm for variable speed screw ball screw system design. Through polynomial frequency modulation wavelet method. Calculate the screw rotation of instantaneous frequency is proposed. The anomaly detection method of positioning screw failure time point time domain signal amplitude threshold and threshold based on derivative calculation of the fault location point screw under different working conditions of the screw instantaneous rotation frequency and fault point. Based on the experimental platform is studied under the two different conditions of multi fault locating screw. The experimental results show that the proposed algorithm can effectively determine the screw fault and calculate the accurate position, the positioning error is less than two screw (4) is developed. The ball screw system accelerated performance degradation experiment. According to the screw life formula, analyzed the influence factors of service life of screw, the design of ball screw screw system performance degradation experiments. By changing the running speed, load different motion conditions of size simulation of ball screw system, and vibration under different conditions of the acquisition, torque the sensor signal, provides data protection for accurate performance evaluation of ball screw system. (5) performance evaluation method of ball screw system based on neural network is proposed. Time domain vibration signal of ball screw system experiment data extraction, frequency domain and time-frequency domain features, the design of frequency domain spectral kurtosis and correlation coefficient a new spectral feature, distinguishing ability. The depth of the neural network feature extraction parameters of deep and reduce the feature dimension and improve recognition The introduction of deep learning. Denoising algorithm, the input vector values in certain positions with random noise instead, enhances the robustness of the model. (6) based on the above theory and experiment, the practical research of ball screw system for condition monitoring and performance evaluation system. Based on the designing principle of Socket data communication protocol data collection terminal and PC client, realize the remote operating system. Research on parallel acceleration scheme algorithm based on graphics processing unit, shorten the calculation time. WxPython model is developed based on the image display interface, easy operation and visual display. The degradation experiment in the performance of ball screw system, data representation learning. In-depth research on condition monitoring and performance evaluation and so on. To explore a new method for characterizing vibration signal processing based on learning, the adaptive expression of a vibration signal A practical study on the state monitoring and performance evaluation of the ball screw system has been carried out, which has played an active role in the industrialization of the system.

【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TH132;TH17

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