TWI512501B - Calculation method of smoothness of ball screw operation - Google Patents
Calculation method of smoothness of ball screw operation Download PDFInfo
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本發明有關於一種滾珠螺桿運行順暢度的診斷方法，特別是指一種能夠將滾珠螺桿運行順暢度予以量化的診斷方法。The invention relates to a method for diagnosing the smooth running of a ball screw, in particular to a diagnostic method capable of quantifying the smooth running of the ball screw.
由於滾珠螺桿具有定位精度高、使用壽命長、摩擦係數低以及可進行高速正逆向傳動與變化傳動等特性，因此廣泛地應用於精密機械等相關產業的定位以及測量系統上。Because the ball screw has high positioning accuracy, long service life, low friction coefficient and high-speed forward and reverse transmission and variable transmission, it is widely used in the positioning and measurement systems of precision machinery and other related industries.
為了達到高定位精度，滾珠螺桿運行時的順暢與否就變得極為重要。進一步言之，就品質保證的觀點來看，針對新製得之滾珠螺桿運行時的順暢程度進行診斷，就能夠避免售出不良品，而就應用的觀點來看，診斷滾珠螺桿運行時的順暢度，能有助於判定滾珠螺桿是否出現異常以利即時替換。In order to achieve high positioning accuracy, the smoothness of the ball screw operation becomes extremely important. Furthermore, from the viewpoint of quality assurance, it is possible to avoid the sale of defective products by diagnosing the smoothness of the newly manufactured ball screw, and from the viewpoint of application, the smooth operation of the ball screw is diagnosed. Degree, can help to determine whether the ball screw is abnormal for instant replacement.
習知用於判定滾珠螺桿是否異常的方法有：例如台灣專利第I400438號(以下簡稱習知技術1)揭示利用霍爾IC元件偵測滾珠螺桿的球通頻率，並將其與理論值做比較，當量測所得之球通頻率低於理論值時，判定該滾珠螺桿發生異常(亦即有磨耗產生)；日本專利公開第JP2004347401號(以下簡稱習知技術2)揭示利用加速規感知軸承的振動訊號，再將該振動訊號轉換成頻譜訊號，藉由已經設定好的門檻值來判定軸承是否產生異常；美國專利第US7680565B2號(以 下簡稱習知技術3)揭示連續地擷取時域訊號並將其接續地轉換成頻譜，再將前次頻譜與下次頻譜進行比較，來決定滾珠螺桿的健康狀態。Conventional methods for determining whether a ball screw is abnormal or not include, for example, Taiwan Patent No. I400438 (hereinafter referred to as conventional technology 1), which discloses that a ball IC frequency of a ball screw is detected by a Hall IC component and compared with a theoretical value. When the ball pass frequency obtained by the equivalent measurement is lower than the theoretical value, it is determined that the ball screw is abnormal (that is, there is wear); Japanese Patent Publication No. JP2004347401 (hereinafter referred to as the conventional technology 2) discloses the use of the acceleration gauge to sense the bearing. Vibrating the signal, and then converting the vibration signal into a spectrum signal, and determining whether the bearing is abnormal by the threshold value that has been set; US Patent No. US7680565B2 Referred to as the prior art 3), the time domain signal is continuously extracted and converted into a spectrum, and the previous spectrum is compared with the next spectrum to determine the health of the ball screw.
由於滾珠螺桿之裝配與製造上的關係，球通頻率之量測 值實際上為變動量，且該值本身就會比理論值低，因此實務上難以用習知技術1的方法來判斷滾珠螺桿的磨耗情形；而習知技術2必須先建置資料庫來定義出門檻值，且其系統必須具有頻譜分析功能方能執行其診斷方法，導致成本較高；而習知技術3係基於「系統一開始就是正常」的假設狀態下來執行，因而無法應付「系統一開始就是不正常」的狀態，使其實際應用上受到限制。Due to the relationship between the assembly and manufacture of the ball screw, the measurement of the ball pass frequency The value is actually a variable amount, and the value itself is lower than the theoretical value. Therefore, it is practically difficult to judge the wear condition of the ball screw by the method of the prior art 1. However, the prior art 2 must first establish a database to define Devaluation, and its system must have a spectrum analysis function to perform its diagnostic method, resulting in higher costs; and the conventional technology 3 is based on the assumption that the system is normal at the beginning, and thus cannot cope with System One. The beginning is not normal, which limits its practical application.
有鑑於此，本發明之主要目的在於提供一種能夠解決前述問題之滾珠螺桿運行順暢度之診斷方法。In view of the above, it is a primary object of the present invention to provide a diagnostic method for smooth operation of a ball screw capable of solving the aforementioned problems.
為達成上述目的，本發明所提供之一種滾珠螺桿運行順暢度之診斷方法，其包括有：(a)擷取滾珠螺桿所產生之物理訊號資料；(b)依據該滾珠螺桿之幾何尺寸資訊以及操作條件資訊，將該物理訊號資料進行分割(division)，以產生複數已分割資料；(c)依據該滾珠螺桿運行時之可量化動態特性，經由淬取(extraction)該已分割資料裡的峰值訊號來產生一峰值特徵資料子序列；(d)利用複數峰值特徵評價模型與該峰值特徵資料子序列，計算出複數峰值特徵資料母序列；以及(e)依據各該峰值特徵資料母序列中之資料的變異性與分布狀態，產生複數順暢度值，藉由該等順暢度值的變化來診斷該滾珠螺桿運行之順暢度等步驟。In order to achieve the above object, the present invention provides a method for diagnosing the smooth running of a ball screw, which comprises: (a) drawing physical signal data generated by a ball screw; (b) according to geometrical information of the ball screw and Operating condition information, dividing the physical signal data to generate a plurality of divided data; (c) extracting peaks in the divided data according to quantifiable dynamic characteristics of the ball screw during operation a signal to generate a peak feature data subsequence; (d) using a complex peak feature evaluation model and the peak feature data subsequence to calculate a complex peak feature data parent sequence; and (e) according to each of the peak feature data parent sequences The variability and distribution state of the data, the complex smoothness value is generated, and the smoothness of the operation of the ball screw is diagnosed by the change of the smoothness values.
在本發明所提供之滾珠螺桿運行順暢度之診斷方法 中，步驟(a)之該物理訊號資料可為位移、速度、加速度、壓力、電壓或前述之組合；而步驟(b)之該幾何尺寸資訊可為外徑、牙長、總長、導程以及珠徑卷數，以及該操作條件資訊可為行程、轉速、循環週期、速度曲線以及負載曲線。Diagnostic method for smooth running of ball screw provided by the present invention The physical signal data of step (a) may be displacement, velocity, acceleration, pressure, voltage or a combination thereof; and the geometric information of step (b) may be outer diameter, tooth length, total length, lead, and The number of beads and the operating condition information can be stroke, speed, cycle, speed curve and load curve.
在本發明所提供之滾珠螺桿運行順暢度之診斷方法 中，該已分割資料係為將該物理訊號資料在時間域上面進行分割所產生者，此外，各該已分割資料所包含之時間長度係與轉速有關，以及各該已分割資料之數量係與導程以及行程有關。Diagnostic method for smooth running of ball screw provided by the present invention The split data is generated by dividing the physical signal data over the time domain. In addition, the length of time included in each divided data is related to the rotational speed, and the number of each divided data is The lead and the itinerary are related.
在本發明所提供之滾珠螺桿運行順暢度之診斷方法 中，步驟(c)之該可量化動態特性可為該滾珠螺桿中之滾動元件週期性地撞擊該滾珠螺桿中之其他元件所產生的特徵頻率。Diagnostic method for smooth running of ball screw provided by the present invention The quantifiable dynamic characteristic of step (c) may be a characteristic frequency generated by the rolling elements in the ball screw periodically striking other elements in the ball screw.
在本發明所提供之滾珠螺桿運行順暢度之診斷方法 中，步驟(c)之該峰值特徵資料子序列之資料總數不超過該已分割資料的分割數，且由各該峰值特徵資料子序列所組成之一資料空間與由各該已分割資料所組成之一資料空間互為映射關係。Diagnostic method for smooth running of ball screw provided by the present invention The total number of data of the peak feature data subsequence of step (c) does not exceed the number of divisions of the divided data, and one of the data spaces consisting of the peak feature data subsequences and the divided data One of the data spaces is a mapping relationship with each other.
在本發明所提供之滾珠螺桿運行順暢度之診斷方法 中；步驟(c)之該峰值特徵係指該峰值特徵資料子序列中局部最大(local maximum)之峰值起算，各自下降0.5倍能量範圍內(振幅下降0.707倍)所包含之峰值能量總和。Diagnostic method for smooth running of ball screw provided by the present invention The peak characteristic of the step (c) refers to the peak of the local maximum in the peak characteristic data subsequence, and each of the peak energy sums included in the energy range of 0.5 times (the amplitude is decreased by 0.707 times).
在本發明所提供之滾珠螺桿運行順暢度之診斷方法 中，步驟(d)之該等峰值特徵評價模型可為以該峰值特徵資料子序列作 為樣本空間來定義其最大值、方均根值、L2-Norm、中位數以及變異數的統計變化量相關量測模型，或者是可為以該峰值特徵資料子序列作為樣本空間來定義其誤差項平方和(sum of squares due to error,SSE)、回歸項平方和(sum of squares due to regression,SSR)以及總平方和(sum of squares total,SST)的回歸分析相關模型，或者是可為以該峰值特徵資料子序列作為樣本空間來定義其機率密度函數的相關模型。 此外，步驟(d)之各該峰值特徵資料母序列之資料總數不超過該已分割資料的分割數。Diagnostic method for smooth running of ball screw provided by the present invention The peak feature evaluation model of step (d) may be performed by using the peak feature data subsequence A statistical variation correlation model for the maximum value, the root mean square value, the L2-Norm, the median, and the variance is defined for the sample space, or the error term can be defined by using the peak feature data subsequence as the sample space. Sum of squares due to error (SSE), sum of squares due to regression (SSR), and sum of squares total (SST) regression analysis related models, or can be The peak feature data subsequence is used as a sample space to define a correlation model of the probability density function. In addition, the total number of pieces of the peak feature data parent sequence of the step (d) does not exceed the number of divisions of the divided data.
在本發明所提供之滾珠螺桿運行順暢度之診斷方法 中，該等峰值特徵資料母序列係為以該峰值特徵資料子序列作為資料空間，基於統計變化量相關量測模型、回歸分析相關模型或機率密度函數相關模型之計算結果所形成的資料集合。Diagnostic method for smooth running of ball screw provided by the present invention The peak feature data parent sequence is a data set formed by using the peak feature data subsequence as a data space, based on a statistical variation related measurement model, a regression analysis related model, or a probability density function correlation model.
在本發明所提供之滾珠螺桿運行順暢度之診斷方法中，在螺桿運行為順暢的情況下，步驟(e)之該等順暢度值不會超過1.4。In the method for diagnosing the smooth running of the ball screw provided by the present invention, in the case where the screw operation is smooth, the smoothness value of the step (e) does not exceed 1.4.
有關本發明所提供之滾珠螺桿運行順暢度之診斷方法的步驟與特徵，以下將列舉實施例並配合圖式進一步詳細說明。The steps and features of the method for diagnosing the smooth running of the ball screw provided by the present invention will be further described in detail below with reference to the embodiments.
S1~S5‧‧‧步驟S1~S5‧‧‧Steps
第1圖為範例1之滾珠螺桿的振動訊號圖。Fig. 1 is a vibration signal diagram of the ball screw of Example 1.
第2圖為範例2之滾珠螺桿的振動訊號圖。Figure 2 is a vibration signal diagram of the ball screw of Example 2.
第3圖為範例1之峰值特徵資料母序列柱狀圖。Figure 3 is a histogram of the peak characteristic data of the sample 1 of the example 1.
第4圖為範例2之峰值特徵資料母序列柱狀圖。Figure 4 is a histogram of the peak characteristic data of the example 2.
第5圖為範例1之順暢度值之線圖。Figure 5 is a line graph of the smoothness values of Example 1.
第6圖為範例2之順暢度值之線圖。Figure 6 is a line graph of the smoothness values of Example 2.
第7圖為本發明之診斷方法的流程圖。Figure 7 is a flow chart of the diagnostic method of the present invention.
首先，如第7圖所示，依據本發明一實施例所為之滾珠螺桿運行順暢度之診斷方法，主要包括有下述步驟。First, as shown in Fig. 7, a method for diagnosing the smoothness of the operation of the ball screw according to an embodiment of the present invention mainly includes the following steps.
首先，於步驟S1中，係擷取一滾珠螺桿所產生之一物理訊號資料；之後於步驟S2中，依據該滾珠螺桿之幾何尺寸資訊以及操作條件資訊，將該物理訊號資料進行分割，以產生複數已分割資料；於步驟S3中，依據該滾珠螺桿運行時之可量化動態特性，經由淬取該已分割資料裡的峰值訊號來產生一峰值特徵資料子序列；於步驟S4中，利用複數峰值特徵評價模型與該峰值特徵資料子序列，計算出複數峰值特徵資料母序列；以及於步驟S5中，依據各該峰值特徵資料母序列中之資料的變異性與分布狀態，產生複數順暢度值，藉由該等順暢度值的變化來診斷該滾珠螺桿運行時之順暢度。First, in step S1, a physical signal data generated by a ball screw is captured; then in step S2, the physical signal data is segmented according to the geometrical information of the ball screw and the operating condition information to generate a plurality of divided data; in step S3, generating a peak characteristic data sub-sequence by extracting a peak signal in the divided data according to the quantizable dynamic characteristic of the ball screw operation; and using the complex peak in step S4 a feature evaluation model and the peak feature data subsequence, calculating a complex peak feature data mother sequence; and in step S5, generating a complex smoothness value according to the variability and distribution state of the data in each of the peak feature data parent sequences The smoothness of the ball screw operation is diagnosed by the change in the smoothness values.
在步驟(a)中，利用加速規擷取滾珠螺桿於特定行程內的振動訊號。In step (a), the acceleration signal is used to extract the vibration signal of the ball screw within a specific stroke.
在步驟(b)中，依行程對應的牙長來定義量測訊號的分割段數N與每段訊號的時間長度T。根據轉速n(單位：rpm)、行程S(單位：mm)以及導程L(單位：mm)得知螺帽每轉一圈所需之時間T以及總行程中所轉總圈數N。前述總圈數N亦為量測訊號所需的分割段數，而螺帽每轉一圈所需之時間T亦為每段訊號的時間長度， N與T的關係可由下列式(1)予以表示。In step (b), the number N of segments of the measurement signal and the length of time T of each segment of the signal are defined according to the length of the tooth corresponding to the stroke. According to the rotational speed n (unit: rpm), the stroke S (unit: mm), and the lead L (unit: mm), the time T required for each revolution of the nut and the total number of turns N in the total stroke are known. The total number of turns N is also the number of segments required for the measurement signal, and the time T required for each revolution of the nut is also the length of time of each signal. The relationship between N and T can be expressed by the following formula (1).
[式1]N=S/L；T=1/(n/60) [Formula 1] N=S/L; T=1/(n/60)
在步驟(c)中，根據球通頻率Fc來決定每個分割段數N中所要擷取的訊號峰值數Np。利用習知技術或學術文獻所提供之下列式(2)即可計算出球通頻率Fc，而每個分割段數中所要擷取的訊號峰值數Np可由下列式(3)予以表示。In the step (c), the number of signal peaks Np to be extracted in each of the divided segments N is determined based on the ball pass frequency Fc. The ball pass frequency Fc can be calculated by the following formula (2) provided by a conventional technique or an academic literature, and the number Np of signal peaks to be extracted in each segment number can be expressed by the following formula (3).
[式3]Np=Fc*T [Formula 3] Np=Fc*T
上式(2)中，ψ 為兩滾珠相鄰所夾角度，其可由下列式(4)求得；而ω m 為滾珠公轉角速度，其可表示成下列式(5)。In the above formula (2), ψ is an angle between two balls adjacent to each other, which can be obtained by the following formula (4); and ω m is a ball revolution angular velocity, which can be expressed by the following formula (5).
上式(4)中，α為導程角，D b 為滾珠直徑，D s 為間格子厚度，以及r m 為節圓半徑(等於0.5d m ，d m 為節圓直徑)。而上式(5) 中，ω 為螺桿旋轉角速度，(其中r b 為滾珠半徑=0.5D b ，D b 為 滾珠直徑，r m 為節圓半徑(等於0.5d m ，d m 為節圓直徑))，α 0 為滾珠接觸螺帽的接觸角，α 1 為滾珠接觸螺桿的接觸角，β 為滾珠自旋角(一般為47度)。In the above formula (4), α is the lead angle, D b is the ball diameter, D s is the inter-grid thickness, and r m is the pitch circle radius (equal to 0.5 d m and d m is the pitch circle diameter). In the above formula (5), ω is the screw rotation angular velocity, (where r b is the ball radius = 0.5 D b , D b is the ball diameter, r m is the pitch circle radius (equal to 0.5 d m , d m is the pitch circle diameter)), and α 0 is the contact angle of the ball contact nut, α 1 is the contact angle of the ball contact screw, and β is the ball spin angle (generally 47 degrees).
在步驟(d)中，接著計算每個分割段數N中各峰值的能量分布狀態{Pn_max}&{Pn_rms}。首先，將量測訊號資料{Data}轉換 為分貝(decibel,dB)表示的方程式型態，並將轉換後的資料標示為{Data}dB ，其中{Data}與{Data}dB 之間的關係為{Data} dB =20log{{Data}/D ref } ，Dref 為量測訊號的物理基準量，如果量測訊號為加速度，則Dref =1μm/s2 。之後，計算每個分割段數N中各自峰值的能量大小Pnn，亦即Pnn=Σ Ph*Pw ，Σ Ph為介於max訊號峰值及其該峰值下降3dB以內所能囊括到之峰值的總高度；Pw為計算峰值高度的訊號寬度(等於1/(4*Fc)或3dB以內所能囊括到峰值的範圍)，需注意的是，計算Σ Ph時以{Data}而不以{Data}dB 較為方便，而Pnn共有N*Np個值。接著，標記出各分割段數N中的最大峰值能量{Pn_max}，亦即{Pn_max}={P1_max,P2_max,...,P N _max} ，其中P1_max=max{P11,P12,...,P1Np }，而P2_max=max{P21,P22,...,P2Np }，以此類推。最後，標記出各分割段數N中峰值能量的方均根值{Pn_rms}，亦即{Pn_rms}={P1_rms,P2_rms,...,PN_rms} ，其中，而，以此類推。需注意的是{Pn_max}與{Pn_rms}各自共有N個值。In the step (d), the energy distribution state {Pn_max} & {Pn_rms} of each peak in each of the divided segments N is calculated. First, convert the measurement signal data {Data} to the equation type represented by decibel (dB), and mark the converted data as {Data} dB , where the relationship between {Data} and {Data} dB It is {Data} dB = 20log{{Data}/D ref } , D ref is the physical reference quantity of the measurement signal, and if the measurement signal is acceleration, D ref =1 μm/s 2 . Then, the energy level Pnn of each peak value in each segment number N is calculated, that is, Pnn=Σ Ph*Pw , where Σ Ph is the total height of the peak that can be included within the peak of the max signal and the peak drop of 3 dB. Pw is the signal width for calculating the peak height (equal to 1/(4*Fc) or the range within 3dB that can be included in the peak). It should be noted that Σ Ph is calculated as {Data} instead of {Data} dB. It is convenient, and Pnn has a total of N*Np values. Next, the maximum peak energy {Pn_max} in the number N of segments is marked, that is, {Pn_max}={P1_max, P2_max, . . . , P N _max} , where P1_max=max{P11, P12,... , P1 Np }, and P2_max=max{P21, P22, ..., P2 Np }, and so on. Finally, the root mean square value {Pn_rms} of the peak energy in each segment number N is marked, that is, {Pn_rms}={P1_rms, P2_rms, ..., PN_rms} , where ,and And so on. It should be noted that {Pn_max} and {Pn_rms} each have N values.
在步驟(e)中，根據{Pn_max}與{Pn_rms}來判定滾珠螺桿的順暢度。首先，將{Pn_max}與{Pn_rms}升幂排列後依最小值將{Pn_max}與{Pn_rms}正歸化為{Pn_MAX} 與{Pn_RMS} 。接著，計算峰值差異變動量{Pn_diffA} ，亦即{Pn_diffA}={P1_diffA,P2_diffA,...PN-1_diffA} ，其中P1_diffA=P2_RMS-P1_RMS，P2_diffA=P3_RMS-P2_RMS，後面以此類推，而PN-1 _diffA=PN _RMS-PN-1 _RMS，需注意的是 {Pn_diffA}的定義方法不僅止於上述的方式，前後牙口數的差異判別方式均視為相同手法，如：相差值、變異量、數值斜率...等。In the step (e), the smoothness of the ball screw is determined based on {Pn_max} and {Pn_rms}. First, {Pn_max} and {Pn_rms} are arranged in power, and {Pn_max} and {Pn_rms} are normalized to {Pn_MAX} and {Pn_RMS} by the minimum value. Next, the peak difference variation amount {Pn_diffA} is calculated, that is, {Pn_diffA}={P1_diffA, P2_diffA, ... PN-1_diffA} , where P1_diffA=P2_RMS-P1_RMS, P2_diffA=P3_RMS-P2_RMS, and so on, and P N-1 _diffA=P N _RMS-P N-1 _RMS. It should be noted that the definition method of {Pn_diffA} is not limited to the above method, and the difference discrimination method of the number of teeth before and after is regarded as the same method, such as: difference value, Variation, numerical slope, etc.
最後，定義順暢度值SA(1<SA<N-1)，並利用下列數種方式判定順暢度。Finally, the smoothness value SA (1 < SA < N-1) is defined, and the smoothness is determined by the following several methods.
方式一：當序列{Pn_diff}中的值出現絕對值大於1.4以上數值2次、4次或6次以上，則S=2、4或6(此情況Pn_diff值出現絕對值大於1.4之情形會連續出現，並且會正負交替)，定義容忍值S_lim，當S>S_lim，則判定螺桿不順暢(適用於多段牙產生不順的情形)。Method 1: When the value in the sequence {Pn_diff} appears to be greater than the value of 1.4 or more, 2 times, 4 times or more, then S=2, 4 or 6 (in this case, the Pn_diff value has an absolute value greater than 1.4. Appears and will alternate between positive and negative), defining the tolerance value S_lim, and when S>S_lim, it is determined that the screw is not smooth (applicable to the situation where the multi-segment teeth are not smooth).
方式二：當序列{Pn_diff}中的值出現絕對值大於1.4以上數值2次、4次或6次以上，且任一次Pn_diff值出現絕對值大於1.4之情形非連續出現，則判定螺桿不順暢(適用於連續一段牙產生不順的情形)。Manner 2: When the value in the sequence {Pn_diff} is more than 1.4, the value is greater than 1.4, 2 times, 4 times or more, and if the absolute value of the Pn_diff value is greater than 1.4, the screw is not smooth. Applicable to the situation where a continuous segment of the tooth is not smooth).
方式三：計算最大峰值差異變動量{Pn_diffB} ，亦即{Pn_diffB}={P1_diffB,P2_diffB, ...P N-1 _diffB} ，其中P1_diffA=P2_MAX-P1_MAX，P2_diffA=P3_MAX-P2_MAX，後面以此類推，而PN-1 _diffA=PN _MAX-PN-1 _MAX，並定義順暢值SB(1<SB<N-1)，而後比照上述方式一與方式二判定順暢度。Three ways: calculating a maximum difference between peak variation amount {Pn_diffB}, i.e., {Pn_diffB} = {P1_diffB, P2_diffB , ... P N-1 _diffB}, where P1_diffA = P2_MAX-P1_MAX, P2_diffA = P3_MAX-P2_MAX, behind this Similarly, P N-1 _diffA=P N _MAX-P N-1 _MAX, and define a smooth value SB (1 < SB < N-1), and then determine the smoothness according to the above manners 1 and 2.
需注意的是{Pn_diffB}的定義方法不僅止於上述的方式，前後牙口數的差異判別方式均視為相同手法，如：相差值、變異量、數值斜率...等。It should be noted that the definition method of {Pn_diffB} is not limited to the above method, and the difference discrimination method of the number of front and back mouths is regarded as the same method, such as: difference value, variation amount, numerical slope, etc.
以下揭示依本發明之診斷方法實際檢測範例1與範例2之滾珠螺桿的實驗例。Experimental examples of actually detecting the ball screws of Examples 1 and 2 according to the diagnostic method of the present invention are disclosed below.
利用加速規擷取範例1與範例2之滾珠螺桿於特定行程內的振動訊號，如第1圖與第2圖所示。範例1與範例2之行程S為289mm，導程L為6mm，轉速n為1000rpm，因此求得總圈數N為48，時間T為0.06s。範例1與範例2之球通頻率Fc為151Hz，因此求得需擷取之訊號峰值數Np為9。接著計算每個分割段數中各峰值的能量分布狀態{Pn_max}&{Pn_rms}，得到下表1與表2所示之峰值特徵資料子序列。The acceleration signals are used to capture the vibration signals of the ball screws of Examples 1 and 2 in a specific stroke, as shown in Figs. 1 and 2. The stroke S of Example 1 and Example 2 is 289 mm, the lead L is 6 mm, and the rotational speed n is 1000 rpm. Therefore, the total number of turns N is 48, and the time T is 0.06 s. The ball pass frequency Fc of the example 1 and the example 2 is 151 Hz, so that the number of signal peaks Np to be extracted is 9 . Next, the energy distribution state {Pn_max} & {Pn_rms} of each peak in each segment number is calculated, and the peak feature data subsequences shown in Table 1 and Table 2 below are obtained.
經由所得到之子序列藉由順暢度判斷定義得到峰值特徵資料母序列，如下第3圖、第4圖。The peak feature data mother sequence is obtained by the smoothness judgment definition through the obtained subsequence, as shown in the third and fourth figures below.
最後再根據{Pn_max}&{Pn_rms}來判定螺桿的順暢度，如第5圖以及第6圖所示。並且，由第5圖與第6圖可以清楚 得知，範例1之滾珠螺桿被診斷為順暢，而範例2之滾珠螺桿被診斷為不順暢。Finally, the smoothness of the screw is determined according to {Pn_max} & {Pn_rms}, as shown in Fig. 5 and Fig. 6. And, it can be clearly seen from Fig. 5 and Fig. 6. It was found that the ball screw of Example 1 was diagnosed as smooth, and the ball screw of Example 2 was diagnosed as not smooth.
綜上所陳，由於本發明之診斷方法僅利用感測器感知滾珠螺桿的動態時域訊號資料，同時搭配滾珠螺桿的規格以及其動態特性，根據特定分割段裡的訊號峰值變化特性就能夠定義出量化的順暢度指標，因此相較於習知診斷方法，本發明之診斷方法不需執行頻譜分析，也不需先建置資料庫，因此成本較低，此外，本發明之診斷方法還能產出量化的順暢度指標，免除人為判斷可能導致之標準不一的問題。In summary, since the diagnostic method of the present invention uses only the sensor to sense the dynamic time domain signal data of the ball screw, and the specification of the ball screw and its dynamic characteristics, it can be defined according to the peak value of the signal in a specific segment. Quantitative smoothness index, therefore, compared with the conventional diagnostic method, the diagnostic method of the present invention does not need to perform spectrum analysis, and does not need to first build a database, so the cost is low, and in addition, the diagnostic method of the present invention can The smoothness of the output is quantified, exempting the problem of human standards that may lead to different standards.
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| US7680565B2 (en) * | 2006-03-23 | 2010-03-16 | Mitchell Gabriel Mircea Balasu | Systems for announcing the health of ball screw actuators and ball recirculation |
| TWI400438B (en) * | 2010-04-01 | 2013-07-01 | Hiwin Tech Corp | Detecting device of transmission element |
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| US7680565B2 (en) * | 2006-03-23 | 2010-03-16 | Mitchell Gabriel Mircea Balasu | Systems for announcing the health of ball screw actuators and ball recirculation |
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