CN105930602A

CN105930602A - Optimal weighted wavelet package entropy-based chattering detection method

Info

Publication number: CN105930602A
Application number: CN201610278121.1A
Authority: CN
Inventors: 熊振华; 孙宇昕; 庄春刚; 朱向阳
Original assignee: Shanghai Jiao Tong University
Current assignee: Shanghai Jiao Tong University
Priority date: 2016-04-28
Filing date: 2016-04-28
Publication date: 2016-09-07

Abstract

The invention discloses an optimal weighted wavelet package entropy-based chattering detection method. By modeling entropy values in chattering and stable states, an optimal weight interval can be obtained; a reasonable threshold is determined in combination with a visual algorithm by applying an extreme value statistics theory, so that the dependence on artificial experience is reduced; and finally, the chattering is detected in a non-occurrence stage, so that the damage of the chattering to workpieces and a machine tool is reduced.

Description

A Flutter Detection Method Based on Optimal Weighted Wavelet Entropy

技术领域technical field

本发明涉及车削颤振检测领域，尤其涉及一种基于最优加权小波包熵的颤振检测方法。The invention relates to the field of turning chatter detection, in particular to a chatter detection method based on optimal weighted wavelet packet entropy.

背景技术Background technique

切削颤振是一种不稳定现象，它几乎发生在所有切削过程中，表现为刀具与工件之间的剧烈振动。尤其是在航空薄壁件车削中，工件最薄处只有1到2毫米，工件的动态性能很差，极易引起颤振。颤振的发生会影响生产效率以及加工质量，同时还可引起过度噪音，刀具损坏等，对产品质量、刀具及机床设备等的危害已毋庸质疑。日益发展的制造业对加工效率、加工质量、加工成本提出了更高的要求，为了更大限度的降低颤振造成的不利影响，必须在颤振孕育阶段就将颤振及早地检测出来，随后选择稳定的切削参数，或者采取行的控制策略，避免颤振对工件和机床部件的损害。Cutting chatter is an unstable phenomenon that occurs in almost all cutting processes and manifests as severe vibration between the tool and the workpiece. Especially in the turning of aerospace thin-walled parts, the thinnest part of the workpiece is only 1 to 2 mm, and the dynamic performance of the workpiece is very poor, which can easily cause chatter. The occurrence of chatter will affect production efficiency and processing quality, and it can also cause excessive noise, tool damage, etc. The harm to product quality, tool and machine tool equipment is beyond doubt. The ever-growing manufacturing industry puts forward higher requirements on processing efficiency, processing quality and processing cost. In order to minimize the adverse effects caused by chatter, chatter must be detected early in the incubation stage of chatter, and then Choose stable cutting parameters, or adopt advanced control strategies to avoid damage to workpieces and machine tool components caused by chatter.

很多学者做过颤振检测方面的研究，有基于加速度、切削力和声信号的，主要可分为以下三类：第一类是信号频率域的分析，如小波变换，S函数变换，希尔伯特黄变换，自适应滤波和相干函数等。根据Heisenberg-Gabor不等式，小波变换不可能在时频域同时获得高分辨率。S函数变换和希尔伯特黄变换的计算量很大，无法应用于在线颤振检测。第二类是模式识别方法，主要有人工神经网络、支持向量机、案例推理、模糊逻辑表等，但是在前期需要做大量的实验来训练模型。第三类是熵值方法，如排列熵，粗粒度熵率，近似熵，这类方法通过提取过程的随机特征来检测颤振，并广泛运用于铣削、车削和镗削。Many scholars have done research on chatter detection, some of which are based on acceleration, cutting force and acoustic signals, which can be mainly divided into the following three categories: The first category is the analysis of signal frequency domain, such as wavelet transform, S-function transform, Hill Bert-Huang transform, adaptive filtering and coherence functions, etc. According to the Heisenberg-Gabor inequality, it is impossible for wavelet transform to obtain high resolution simultaneously in the time-frequency domain. S-function transform and Hilbert-Huang transform are computationally intensive and cannot be applied to online flutter detection. The second category is pattern recognition methods, which mainly include artificial neural networks, support vector machines, case reasoning, fuzzy logic tables, etc., but a lot of experiments are required to train the models in the early stage. The third category is entropy methods, such as permutation entropy, coarse-grained entropy rate, and approximate entropy. These methods detect chatter by extracting random features of the process, and are widely used in milling, turning, and boring.

因此，本领域的技术人员致力于开发一种基于最优加权小波包熵的颤振检测方法，不仅计算速度快，还能比现有的车削颤振检测方法更早地的检测出颤振，即在颤振孕育阶段检测出颤振。Therefore, those skilled in the art are committed to developing a chatter detection method based on optimal weighted wavelet packet entropy, which not only has a fast calculation speed, but also detects chatter earlier than the existing turning chatter detection method, That is, flutter is detected during the flutter incubation phase.

发明内容Contents of the invention

有鉴于现有技术的上述缺陷，本发明所要解决的技术问题是如何更早地检测出颤振，如何在颤振的孕育阶段检测出颤振。In view of the above-mentioned defects in the prior art, the technical problem to be solved by the present invention is how to detect the chatter earlier and how to detect the chatter in the incubation stage of the chatter.

为了克服上述现有技术的不足，本发明提供了一种快速有效的车削颤振检测方法，该方法是基于加权小波包熵值(Weighted Wavelet Packet Entropy,WWPE)的，能够在颤振孕育阶段就将颤振检测出来。整个颤振检测流程见图1，该方法主要分为以下几步：In order to overcome the deficiencies of the prior art above, the present invention provides a fast and effective turning chatter detection method, which is based on weighted wavelet packet entropy (Weighted Wavelet Packet Entropy, WWPE). Flutter is detected. The entire flutter detection process is shown in Figure 1. This method is mainly divided into the following steps:

(1)确定小波包分解层数L(1) Determine the number of wavelet packet decomposition layers L

如果分解层L过大，小波包变换生成的频带会很窄，即更精细的频率分辨率。然而，频带如果很窄，将放大稳定状态下的WWPE波动。关于这种波动的来源解释如下：由于切削过程的复杂性和随机性，在稳定状态下，每个频带的能量比值会在2^-L的波动。具体而言，能量分布的波动主要源于切屑和受迫振动导致的测量误差，以及材料，温度和切削力的不连续性所引起的时变动态特性。第L层第j频带的小波包系数定义为：If the decomposition layer L is too large, the frequency band generated by wavelet packet transform will be very narrow, that is, finer frequency resolution. However, if the frequency band is narrow, it will amplify WWPE fluctuations in steady state. The source of this fluctuation is explained as follows: due to the complexity and randomness of the cutting process, in a steady state, the energy ratio of each frequency band will fluctuate in 2- ^L . Specifically, the fluctuations in energy distribution mainly originate from measurement errors caused by chips and forced vibrations, as well as time-varying dynamic characteristics caused by material, temperature and cutting force discontinuities. The wavelet packet coefficient of the jth frequency band in the L layer is defined as:

$\begin{matrix} {x x}_{L L}^{j j} = = {{{c c}_{j j,, i i},, i i = = 11,, 22,, ... ...,, K K}},, & j j = = 11,, 22,, ... ...,, 22^{L L} \end{matrix}$

(2)确定加权频带(2) Determine the weighted frequency band

加权频带的确定，首先通过模态实验获得工艺系统固有频率，根据固有频率所属频带即为加权频带。To determine the weighted frequency band, the natural frequency of the process system is first obtained through modal experiments, and the frequency band to which the natural frequency belongs is the weighted frequency band.

(3)确定最优权值(3) Determine the optimal weight

最优权值的确定。通过理论分析和设计实验获得。分别建立加速度信号在频域的能量分布在稳定状态和颤振状态下的模型。在稳定状态下，每个频带所占总能量的比值相同：Determination of optimal weights. Obtained through theoretical analysis and design experiments. The models of the energy distribution of the acceleration signal in the frequency domain in the steady state and flutter state are respectively established. At steady state, each frequency band contributes the same amount to the total energy:

E_L,j＝2^-L，j＝1,2,...,2^L E _L,j ＝2 ^-L ，j＝1,2,...,2 ^L

假设颤振主频率位于第p频带，将这个频带加权后，得到稳定状态下的WWPE值：Assuming that the main frequency of flutter is in the pth frequency band, after weighting this frequency band, the WWPE value in the steady state is obtained:

${ρ ρ}_{s the s t t e e a a d d y the y} = = - - \frac{k k}{22^{L L} + + k k - - 11} l l n no ((\frac{k k}{22^{L L} + + k k - - 11})) - - \frac{22^{L L} - - 11}{22^{L L} + + k k - - 11} l l n no ((\frac{11}{22^{L L} + + k k - - 11}))$

当颤振发生时，第p频带的能量比值增加为：When flutter occurs, the energy ratio of the pth frequency band increases as:

E_L,p＝2^-L+d,d>0E _L,p ＝2 ^-L +d,d>0

其中d是被总能量归一化后的能量增加量。于此，颤振状态下的WWPE为：where d is the energy increase normalized by the total energy. Here, the WWPE in the flutter state is:

$\begin{matrix} {ρ ρ}_{c c h h a a t t t t e e r r} = = - - \frac{k k ((11 + + 22^{L L} d d))}{k k ((11 + + 22^{L L} d d)) + + 22^{L L} - - 11} ln ln ((\frac{k k ((11 + + 22^{L L} d d))}{k k ((11 + + 22^{L L} d d)) + + 22^{L L} - - 11})) \\ - - \frac{22^{L L} - - 11}{k k ((11 + + 22^{L L} d d)) + + 22^{L L} - - 11} ln ln ((\frac{11}{k k ((11 + + 22^{L L} d d)) + + 22^{L L} - - 11})) \end{matrix}$

那么，颤振引起的WWPE减少量为：Then, the amount of WWPE reduction caused by flutter is:

Δρ＝ρ_steady-ρ_chatter Δρ=ρ _steady _{-ρ chatter}

Δρ值越大，稳定状态和颤振状态的差异也就越大。因此，最大化Δρ是一种可以直接提升WWPE值的颤振检测性能。Δρ是k、L、d的函数，其中k、L、d分布代表权值，小波分解层数，归一化的能量增加量。从图2可以看出，Δρ首先随着k的增加而快速增加，当k达到极值点时，Δρ随着k的增加开始缓慢减小。根据理论分析，对每组L和d，都存在最优权值，使得稳定状态和颤振状最大化。以此为基础，设计实验获得最优权值。The larger the value of Δρ, the greater the difference between steady state and chatter state. Therefore, maximizing Δρ is a flutter detection performance that can directly improve the WWPE value. Δρ is a function of k, L, and d, where the distribution of k, L, and d represents the weight, the number of wavelet decomposition layers, and the normalized energy increase. It can be seen from Figure 2 that Δρ first increases rapidly with the increase of k, and when k reaches the extreme point, Δρ begins to decrease slowly with the increase of k. According to theoretical analysis, for each group of L and d, there is an optimal weight to maximize the steady state and flutter state. On this basis, design experiments to obtain the optimal weights.

(4)计算WWPE(4) Calculate WWPE

第L层每个频带的能量为：The energy of each frequency band in layer L is:

${E E.}_{L L,, j j} = = {Σ Σ}_{i i = = 11}^{K K} {| | {c c}_{j j,, i i} | |}^{22}$

其中E_L,j代表第L层第j频带的能量，所有频带的总能量为Where E _L,j represents the energy of the jth frequency band in the L layer, and the total energy of all frequency bands is

$E E. = = {Σ Σ}_{j j = = 11}^{22^{L L}} {E E.}_{L L,, j j}$

为了简便，能量向量归一化为For simplicity, the energy vector normalized to

$\begin{matrix} {V V}^{n no} = = [\begin{matrix} {E E.}_{L L,, 11}^{n no} & {E E.}_{L L,, 22}^{n no} & ... ... & {E E.}_{L L,, 22^{L L}}^{n no} \end{matrix}] \\ = = \frac{11}{E E.} [\begin{matrix} {E E.}_{L L,, 11} & {E E.}_{L L,, 22} & ... ... & {E E.}_{L L,, 22^{L L}} \end{matrix}] \end{matrix}$

其中Vⁿ是归一化能量向量，是E_L,j的归一化形式。不是一般性地，令第p频带作为被加权频带：where ^Vn is the normalized energy vector, is the normalized form of E _L,j . Not generally, let the p-th frequency band be the weighted frequency band:

${E E.}_{L L,, p p}^{n no w w} = = {kE k}_{L L,, p p}^{n no}$

其中k是权值，满足k＞1。现在加权能量向量为Where k is the weight, satisfying k>1. Now the weighted energy vector is

${V V}^{n no w w} = = [\begin{matrix} {E E.}_{L L,, 11}^{n no} & ... ... & {E E.}_{L L,, p p - - 11}^{n no} & {E E.}_{L L,, p p}^{n no w w} & {E E.}_{L L,, p p + + 11}^{n no} & ... ... & {E E.}_{L L,, 22^{L L}}^{n no} \end{matrix}]$

从而得到WWPEand thus get WWPE

$ρ ρ = = - - {E E.}_{L L,, p p}^{n no w w} l l n no {E E.}_{L L,, p p}^{n no w w} - - {Σ Σ}_{j j = = 1. 1. j j &NotEqual; &NotEqual; p p}^{22^{L L}} {E E.}_{L L,, j j}^{n no} l l n no {E E.}_{L L,, j j}^{n no}$

(5)确定颤振发生阈值和颤振判定(5) Determine chatter occurrence threshold and chatter judgment

阈值确定方法如下：The threshold determination method is as follows:

(a)选择合适切削参数，进行稳定切削。(a) Select appropriate cutting parameters for stable cutting.

(b)计算稳定切削的WWPE(b) Calculation of WWPE for stable cutting

(c)获得样本{X₁,X₂,...,X_n}，从而样本中每10个样本值取一个最大值，构成最大值子集(c) Obtain samples {X ₁ ,X ₂ ,...,X _n }, so that every 10 sample values in the sample take a maximum value to form a maximum value subset

(d)通过视化算法确定最大值分布类型(d) Determine the type of maximum distribution by visual algorithm

(e)用最大值子集拟合所确定的分布类型，最后根据置信度水平确定阈值。(e) Fitting the determined distribution type with the maximum subset, and finally determining the threshold according to the confidence level.

最后，将步骤4计算出的WWPE与步骤5计算出的阈值比较，当WWPE小于阈值则判定为颤振，否则为稳定。Finally, compare the WWPE calculated in step 4 with the threshold calculated in step 5. When the WWPE is smaller than the threshold, it is determined to be chattering, otherwise it is stable.

本发明所述的基于最优加权小波包熵的颤振检测方法，不仅计算速度快，还能比现有的车削颤振检测方法更早地的检测出颤振，即在颤振孕育阶段检测出颤振。The chatter detection method based on the optimal weighted wavelet packet entropy of the present invention not only has a fast calculation speed, but also detects chatter earlier than the existing turning chatter detection method, that is, detects chatter in the chatter breeding stage out chatter.

以下将结合附图对本发明的构思、具体结构及产生的技术效果作进一步说明，以充分地了解本发明的目的、特征和效果。The idea, specific structure and technical effects of the present invention will be further described below in conjunction with the accompanying drawings, so as to fully understand the purpose, features and effects of the present invention.

附图说明Description of drawings

图1是本发明的一个较佳实施例的颤振检测流程图；Fig. 1 is a flutter detection flowchart of a preferred embodiment of the present invention;

图2是Δρ与层数L，权值k，加权频带能量变化d的关系图；Fig. 2 is a relationship diagram between Δρ and the number of layers L, the weight k, and the weighted frequency band energy change d;

图3是本发明的一个较佳实施例的检测到颤振的时刻与权值k关系图；Fig. 3 is a diagram of the relationship between the time when flutter is detected and the weight k in a preferred embodiment of the present invention;

图4是本发明的一个较佳实施例的WWPE随时间变化关系图。Fig. 4 is a graph showing the relationship of WWPE with time in a preferred embodiment of the present invention.

具体实施方式detailed description

下面根据一个较佳实施例进一步阐述本发明所述的基于最优加权小波包熵的颤振检测方法，包括如下步骤：The chatter detection method based on optimal weighted wavelet packet entropy of the present invention is further described below according to a preferred embodiment, including the following steps:

(1)确定小波包分解层(1) Determine the wavelet packet decomposition layer

在小波包变换的实施中，使用八阶Daubechies小波，将加速度信号分解到第4层。第4层的小波包变换系数是In the implementation of wavelet packet transform, the acceleration signal is decomposed into the fourth layer by using the eighth-order Daubechies wavelet. The wavelet packet transform coefficients of layer 4 are

${(\begin{matrix} {x x}_{44}^{11} & {x x}_{44}^{22} & ... ... & {x x}_{44}^{1616} \end{matrix})}^{T T},,$

其中是第4层第j频带的小波包系数。构造能量向量V＝[E_4,1 E_4,2 … E_4,16]，归一化后：in is the wavelet packet coefficient of the jth frequency band in the fourth layer. Construct energy vector V=[E _4,1 E _4,2 ... E _4,16 ], after normalization:

${V V}^{n no} = = [\begin{matrix} {E E.}_{44,, 11}^{n no} & {E E.}_{44,, 22}^{n no} & ... ... & {E E.}_{44,, 1616}^{n no} \end{matrix}] . .$

(2)选择应该加权的小波频带。(2) Select the wavelet frequency bands that should be weighted.

并计算WWPE。对于一个给定的机床工件系统，颤振频率或颤动频带可以通过机床工件系统的频率响应函数实验来预测。颤振频率通常比刀架(或工件)最低固有频率稍大0-15％。刀架(或工件)的固有频率可以通过模态实验得到。在实例中，根据模态实验获得的频率响应函数，主颤振频率位于第四层第一频带。为了提高WWPE对于颤振的敏感度，第一频带的能量比加权后为：And calculate the WWPE. For a given machine tool workpiece system, the chatter frequency or chatter frequency band can be predicted by experimenting with the frequency response function of the machine tool workpiece system. The chatter frequency is usually 0-15% greater than the lowest natural frequency of the tool holder (or workpiece). The natural frequency of the tool holder (or workpiece) can be obtained through modal experiments. In the example, according to the frequency response function obtained by the modal experiment, the main flutter frequency is located in the first frequency band of the fourth layer. In order to improve the sensitivity of WWPE to flutter, the weighted energy ratio of the first frequency band is:

${E E.}_{44,, 11}^{n no w w} = = {kE k}_{44,, 11}^{n no}$

其中k表示权值，并最终算出WWPE。图1给出了所提颤振检测方法的整个流程。Among them, k represents the weight value, and finally calculates WWPE. Figure 1 shows the entire flow of the proposed flutter detection method.

(3)阈值确定(3) Threshold determination

一旦，计算出WWPE，将其余阈值比较，如果熵值低于阈值则表示颤振发生。值得注意的是，阈值是根据稳定状态下的WWPE获得的，而且不同权值下的阈值也是不一样的。下面用一个实验例子，来更好的诠释阈值确定方法：Once, the WWPE is calculated, the rest of the thresholds are compared, and if the entropy value is lower than the threshold, chattering occurs. It is worth noting that the threshold is obtained according to the WWPE in the steady state, and the threshold under different weights is also different. Let's use an experimental example to better explain the threshold determination method:

(a)选择合适切削参数，进行稳定切削，采集加速度信号，计算得到500个WWPE值。(a) Select appropriate cutting parameters, perform stable cutting, collect acceleration signals, and calculate 500 WWPE values.

(b)从每10个WWPE提取出一个最大值，因此得到50个最大值构成的最大值子集Ω。(b) A maximum value is extracted from every 10 WWPEs, so a maximum value subset Ω composed of 50 maximum values is obtained.

(c)利用视化算法确定最大值子集所服从的分布，(c) Use a visualization algorithm to determine the distribution that the maximum subset obeys,

(4)确定最优权值。(4) Determine the optimal weight.

为了研究k对于WWPE的影响，我们进行了一组试验，k从1，2，3到50。注意到，当k＝1，WWPE退化为WPE。对于每个k值，计算出WWPE和阈值。图3给出了检测到颤振的时刻(检测时刻)与k的关系，最优权值取为k∈[7,16]，使用最优权值区间的权值时，WWPE能够比使用其他权值更早地检测出颤振。In order to study the influence of k on WWPE, we conducted a set of experiments with k ranging from 1, 2, 3 to 50. Note that when k=1, WWPE degenerates to WPE. For each value of k, WWPE and threshold are calculated. Figure 3 shows the relationship between the time when flutter is detected (detection time) and k, the optimal weight is k∈[7,16], when using the weight of the optimal weight interval, WWPE can compare with other Weights detect flutter earlier.

(5)用最优权值进行颤振检测(5) Flutter detection with optimal weights

根据步骤(4)中的实验，获得k的最优区间为k∈[7,16]，从而得到最优WWPE。为了验证最优WWPE的有效性，图4比较了三种权值下k＝1，8，30，WWPE的颤振检测性能。可以看出，使用位于最优区间的权值，比非最优区间的权值要更早地检测出颤振。详细地，权值为8的WWPE在t＝6.78秒时检测出颤振，而权值为1和30的WWPE分别在t＝7.77秒和t＝7.04秒检测出颤振。换言之，权值为8的WWPE比权值为1和30两种情况分别提前0.99秒和0.26秒检测出颤振，这验证了实验中得到的最优权值区间。According to the experiment in step (4), the optimal interval for obtaining k is k ∈ [7,16], thus obtaining the optimal WWPE. In order to verify the effectiveness of the optimal WWPE, Fig. 4 compares the flutter detection performance of WWPE under three weights k=1, 8, 30. It can be seen that chattering is detected earlier with weights in the optimal interval than with weights in the non-optimal interval. In detail, the WWPE with a weight value of 8 detects chattering at t=6.78 seconds, while the WWPEs with weights of 1 and 30 detect chattering at t=7.77 seconds and t=7.04 seconds, respectively. In other words, the WWPE with a weight of 8 detects flutter 0.99 seconds earlier and 0.26 seconds earlier than the two cases with a weight of 1 and 30, respectively, which verifies the optimal weight range obtained in the experiment.

以上详细描述了本发明的较佳具体实施例。应当理解，本领域的普通技术无需创造性劳动就可以根据本发明的构思作出诸多修改和变化。因此，凡本技术领域中技术人员依本发明的构思在现有技术的基础上通过逻辑分析、推理或者有限的实验可以得到的技术方案，皆应在由权利要求书所确定的保护范围内。The preferred specific embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make many modifications and changes according to the concept of the present invention without creative efforts. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning or limited experiments on the basis of the prior art shall be within the scope of protection defined by the claims.

Claims

1. a tremor detection method based on optimal weighting Wavelet Packet Entropy, it is characterised in that comprise the following steps:

Step 1, determining WAVELET PACKET DECOMPOSITION number of plies L, wherein the wavelet packet coefficient of L layer jth frequency band is defined as:

x_{L}^{j} = {c_{j, i}, i = 1, 2, ..., K}, j = 1, 2, ..., 2^{L}

Step 2, determine weighting frequency band；

Step 3, determine best initial weights；

Step 4, calculating weighted wavelet bag entropy；

Step 5, determine that tremor generation threshold value and tremor judge.

2. tremor detection method based on optimal weighting Wavelet Packet Entropy as claimed in claim 1, it is characterised in that In step 2, the determination method of described weighting frequency band is:

Step 21, by mode experiment obtain process system natural frequency；

Step 22, determine weighting frequency band according to frequency band belonging to natural frequency.

3. tremor detection method based on optimal weighting Wavelet Packet Entropy as claimed in claim 1, it is characterised in that In step 3, the determination method of described best initial weights is:

Step 31, set up the processing signal Energy distribution at frequency domain mould under steady statue and chatter state respectively Type；At steady state, the ratio of gross energy shared by each frequency band is identical:

E_L,j=2^-L, j=1,2 ..., 2^L

Step 32, assume that tremor basic frequency is positioned at pth frequency band, after being weighted by this frequency band, obtain steady statue Under weighted wavelet bag entropy value:

ρ_{s t e a d y} = - \frac{k}{2^{L} + k - 1} l n (\frac{k}{2^{L} + k - 1}) - \frac{2^{L} - 1}{2^{L} + k - 1} l n (\frac{1}{2^{L} + k - 1})

Step 33, when tremor occurs, the energy ratio of pth frequency band increases to:

E_L,p=2^-L+d,d>0

Wherein d is by the energy increments after gross energy normalization；

Weighted wavelet bag entropy under step 34, chatter state is:

\begin{matrix} ρ_{c h a t t e r} = - \frac{k (1 + 2^{L} d)}{k (1 + 2^{L} d) + 2^{L} - 1} \ln (\frac{k (1 + 2^{L} d)}{k (1 + 2^{L} d) + 2^{L} - 1}) \\ - \frac{2^{L} - 1}{k (1 + 2^{L} d) + 2^{L} - 1} \ln (\frac{1}{k (1 + 2^{L} d) + 2^{L} - 1}) \end{matrix}

The weighted wavelet bag entropy decrement that step 35, tremor cause is Δ ρ, and described Δ ρ is the letter of k, L, d Number, wherein k, L, d represent weights, the wavelet decomposition number of plies, normalized energy increments respectively；To often organizing L With there is best initial weights in d, Δ ρ so that steady statue and tremor shape maximize.

4. tremor detection method based on optimal weighting Wavelet Packet Entropy as claimed in claim 3, it is characterised in that institute Stating processing signal is acceleration signal.

5. tremor detection method based on optimal weighting Wavelet Packet Entropy as claimed in claim 1, it is characterised in that In step 4, the computational methods of described weighted wavelet bag entropy are:

Step 41, the energy of each frequency band of L layer be:

E_{L, j} = Σ_{i = 1}^{K} {| c_{j, i} |}^{2}

Wherein E_L,jRepresenting the energy of L layer jth frequency band, the gross energy of all frequency bands is

E = Σ_{j = 1}^{2^{L}} E_{L, j}

Wherein VⁿIt is normalized energy vector,It is E_L,jNormalized form；

Step 43, make pth frequency band as being weighted frequency band:

E_{L, p}^{n w} = {kE}_{L, p}^{n}

Wherein k is weights, meets k ＞ 1；

Step 44, weighted energy vector are

V^{n w} = [E_{L, 1}^{n} ... E_{L, p - 1}^{n} E_{L, p}^{n w} E_{L, p + 1}^{n} ... E_{L, 2^{L}}^{n}]

Then weighted wavelet bag entropy is

ρ = - E_{L, p}^{n w} \ln E_{L, p}^{n w} - Σ_{j = 1. j &NotEqual; p}^{2^{L}} E_{L, j}^{n} \ln E_{L, j}^{n} .

6. tremor detection method based on optimal weighting Wavelet Packet Entropy as claimed in claim 1, it is characterised in that In step 5, described threshold value determination method comprises the steps:

Step 51, select suitable cutting parameter, carry out stable cutting；

Step 52, the weighted wavelet bag entropy of the stable cutting of calculating；

Step 53, acquisition sample { X₁,X₂,...,X_n, thus in sample, every 10 sample values take a maximum, Constitute maximum subset；

Step 54, determined maximum distribution pattern by visualized algorithm；

Distribution pattern determined by step 55, use maximum subset matching, determines threshold value according to level of confidence.

7. tremor detection method based on optimal weighting Wavelet Packet Entropy as claimed in claim 1, it is characterised in that In step 5, the decision method of described tremor is that weighted wavelet bag entropy step 4 calculated calculates with step 5 The threshold ratio gone out relatively, when weighted wavelet bag entropy is then judged to tremor less than threshold value, is otherwise stable.