CN102226740A - Bearing fault detection method based on manner of controlling stochastic resonance by external periodic signal - Google Patents

Bearing fault detection method based on manner of controlling stochastic resonance by external periodic signal Download PDF

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CN102226740A
CN102226740A CN2011100961077A CN201110096107A CN102226740A CN 102226740 A CN102226740 A CN 102226740A CN 2011100961077 A CN2011100961077 A CN 2011100961077A CN 201110096107 A CN201110096107 A CN 201110096107A CN 102226740 A CN102226740 A CN 102226740A
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bearing
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bearing fault
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CN102226740B (en
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张美丽
林敏�
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中国计量学院
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Abstract

The invention discloses a bearing fault detection method based on a manner of controlling stochastic resonance by an external periodic signal. According to the method provided in the invention, after a bearing fault signal is converted by a variable metric method, the converted signal is input in a bistable system; meanwhile, an external single frequency periodic signal is taken as a control signal to act directly on the system; contact barrier height of the bistable system and an escape rate of Kramers are changed by continuously adjusting an amplitude of the control signal. Therefore, stochastic resonance can be generated or increased artificially; a spectral value of an output power spectrum at the position of an input signal frequency can be effectively improved; and thus a characteristic signal of a bearing fault can be detected accurately at last. The detection method provided in the invention enables the effective control of the stochastic resonance to be realized, thereby providing a novel method for early detection of equipment faults.

Description

基于外加周期信号控制随机共振的轴承故障检测方法 Based on the periodic signal applied to the control stochastic resonance bearing failure detection method

技术领域 FIELD

[0001] 本发明涉及一种故障信号检测方法,尤其涉及一种在轴承故障诊断中使用的故障信号检测方法。 [0001] The present invention relates to a method for the failure signal detection, fault signal detection method in particular, relates to a fault diagnosis of the bearing.

背景技术 Background technique

[0002] 轴承是机器最常用却易磨损的部件。 [0002] bearing machine is the most common but easily worn parts. 据不完全统计,旋转机器大约30%的故障由轴承故障引起的。 According to incomplete statistics, about 30 percent of the rotary machine bearing failure caused by a fault. 产生轴承故障的原因有疲劳剥落,磨损,塑性变形,诱蚀,断裂,胶合,保持架损坏等。 There bearing failure cause spalling fatigue, wear, plastic deformation, induced corrosion, fracture, gluing, cage damage. 如果不能及时诊断轴承早期故障,将使机器设备产生严重故障,从而造成巨大的经济损失。 If not diagnosed early bearing failure, it will have serious equipment failure, causing huge economic losses. 因此,诊断出轴承的早期故障特征对避免严重故障的发生,保证机器设备的正常运行有着重大的现实意义。 Therefore, the diagnosis of early bearing fault characteristic of avoiding serious failures to ensure the normal operation of machinery and equipment of great practical significance. 在轴承故障诊断领域,利用现代信号处理方法对轴承故障进行处理,从含有噪声的信号中准确提取故障特征信号,是当前故障诊断的研究热点之一。 Bearing fault diagnosis using modern signal processing method for processing a bearing fault, extracting characteristic signal from a signal containing noise accurately, it is a hot current fault diagnosis. 采用的方法大多利用信号与噪声特性上的差异,通过数学变换方法来削弱噪声,提取有用信号, 不存在噪声与信号能量转换的物理机制,因而难以放大强噪声中的弱信号。 Methods of using most of the difference signal and noise characteristics, to weaken the noise by a mathematical transform, to extract useful signal, the physical mechanism of noise and signal energy conversion do not exist, it is difficult to amplify weak signals in strong noise. 其次,轴承故障信号由故障特征信号和背景噪声组成。 Secondly, bearing fault signal generated by the fault signal and the background noise characteristic components. 大量的背景噪声会引起现场测量信号信噪比降低, 当干扰严重时,甚至无法检测出轴承故障的早期特征,影响了旋转机器的正常运行。 Background noise can cause a large number of field measurement signal to noise ratio decreases, when the interference is serious, or even can not be detected early bearing failure characteristics, affecting the normal operation of the rotating machine.

发明内容 SUMMARY

[0003] 本发明的目的在于针对现有技术的不足,提供一种基于外加周期信号控制随机共振的轴承故障检测方法。 [0003] The object of the present invention is for the deficiencies of the prior art, there is provided a bearing failure detection method based on the periodic signal applied to the control of stochastic resonance.

[0004] 本发明的目的是通过以下技术方案来实现的:一种基于外加周期信号控制随机共振的轴承故障检测方法,其特征在于,具体步骤如下: [0004] The object of the present invention is achieved by the following technical solutions: A method for detecting bearing failure based on the control signal applied periodic stochastic resonance, wherein the following steps:

(1)利用采集系统采集振动加速度信号; (1) using the acquired vibration acceleration signal acquisition system;

(2)将轴承故障信号经变尺度方法变换为小频率信号; (2) the bearing fault signal becomes small scale method of converting a frequency signal;

(3)将变尺度后的轴承故障信号作用到双稳系统,分析双稳系统输出的功率谱,按频率压缩尺度比恢复实测轴承故障信号的采集尺度; (3) bearing the fault signal is applied to the variable metric bistable system, power spectrum analysis of the output bistable system, the frequency compression recovery ratio Found scale bearing scale fault signal acquisition;

(4)外加单一频率周期信号作为控制信号作用于双稳系统,调节控制信号的幅值,从而人为地产生或加强随机共振,检测出轴承故障特征信号。 (4) a single frequency periodic signal is applied as a control signal applied to the bistable system, adjusting the amplitude of the control signal to artificially generate or enhance resonance, bearing fault detected characteristic signal.

[0005] 进一步地,所述步骤(1)具体为:将加速度传感器固定在振动台上,利用采集系统采集轴承的振动加速度信号即轴承故障信号; [0005] Further, the step (1) specifically includes: the acceleration sensor is fixed on a vibration table using vibration acceleration signal acquisition system bearing, i.e. a bearing fault signal;

进一步地,所述步骤(2)具体为:根据频率压缩尺度比、定义压缩采样频率/5, = /5/及, Further, the step (2) specifically comprises: a compression ratio of the frequency scale, the sampling frequency defined compression / 5, = / 5 / and,

为故障信号的实际采样频率;由压缩采样频率得到数值计算步长为& =〗//„,使得轴承 Actual sampling frequency fault signal; sampling frequency obtained by a compression step size & numerical〗 = // ", such that the bearing

故障信号的每一频率成分(故障信号特征频率为J0 )按频率压缩尺度比i?线性压缩,从而轴 Each frequency component of the fault signal (fault signal of characteristic frequency J0) according to the frequency scale compression ratio I? Linear compression, so that the shaft

承故障信号的特征频率压缩为^=Ji0使之满足随机共振已有的绝热近似理论中小频率信号的条件。 Bearing fault signal characteristic frequency ^ = Ji0 compressed so as to satisfy the resonance condition existing random theoretical adiabatic small frequency signal.

[0006] 进一步地,所述步骤(3)具体为:将变尺度后的轴承故障信号作用到双稳系统,通过分析双稳系统输出的功率谱,捕捉故障信号的特征频率,最好按频率压缩尺度比R恢复故障信号特征频率为= ^ 4。 [0006] Further, the step (3) is specifically: the fault signal as a bearing to the variable metric bistable system, by analyzing the power spectrum of the output bistable system, the capture fault signal characteristic frequency, the frequency is preferably scale compression recovery ratio R of the fault signal characteristic frequency ^ = 4.

[0007] 进一步地,所述步骤(4)具体为:外加单一频率周期信号Sc。 [0007] Further, the step (4) specifically includes: a single frequency periodic signal applied Sc. S(ft)作为控制信号作用于双稳系统,通过调节控制信号的幅值S,双稳系统势垒高及Kramers逃逸速率发生改变,从而能够人为地产生或增强随机共振,有效地增强双稳系统输出功率谱在输入信号频率处的谱值,实现随机共振的控制,检测出轴承故障特征信号。 S (. Ft) acting as a control signal to a bistable system, by adjusting the amplitude of the control signal S, bistable system escape barrier height and Kramers rate changes, can be artificially generated or enhanced resonance, effective to enhance bistability system output spectrum of the power spectrum of the input signal frequency, to achieve control of stochastic resonance, bearing fault detected characteristic signal.

[0008] 本发明的有益效果是,本发明通过连续调节控制信号的幅值,双稳系统势垒高及Kramers逃逸速率发生改变,从而能够人为地产生或增强随机共振,有效地增强了双稳系统输出功率谱在输入信号频率处的谱值,最终可以准确地检测出轴承故障特征信号。 [0008] Advantageous effects of the present invention is that, by continuously adjusting the amplitude of the control signal of the invention, bistable system escape barrier height and Kramers rate changes, can be artificially generated or enhanced resonance, effectively enhancing the bistable system output spectrum of the power spectrum of the input signal frequency, eventually bearing fault accurately detected characteristic signal. 轴承故障信号通过随机共振能有效放大故障特征信号,提高故障特征信号的信噪比,准确地获取故障特征信号频率,该方法实现了随机共振的有效控制,为设备故障早期检测提供了一种新的方法。 Bearing fault signal can be effectively amplified by stochastic resonance characteristic fault signal, the fault characteristics of the signal-noise ratio accurately, a fault signal frequency characteristic, which enables efficient control of stochastic resonance, there is provided a novel apparatus for the early detection of faults Methods. 该方法也适用于其它领域涉及强噪声中的微弱信号检测,可拓宽随机共振的应用,具有良好的应用前景。 This method is also applicable to other fields of weak signal detection in strong noise, can broaden the application of the stochastic resonance, it has good application prospect.

附图说明 BRIEF DESCRIPTION

[0009] 图1为外加周期信号控制随机共振的频率检测原理框图。 [0009] FIG. 1 is applied to a random cycle control block diagram resonance frequency detection signal.

[0010] 图2为轴承振动信号功率谱图。 [0010] FIG 2 is a signal power spectrum of bearing vibration.

[0011] 图3为含噪声的轴承振动信号功率谱图。 [0011] FIG. 3 is a noisy signal power spectrum of bearing vibration.

[0012] 图4为5 = O时随机共振功率谱图。 [0012] FIG. 4 is a stochastic resonance power spectrum when 5 = O.

[0013] 图5为5 = 31时随机共振功率谱图。 [0013] FIG. 5 is 5 = 31 stochastic resonance power spectrum.

具体实施方式 Detailed ways

[0014] 本发明基于外加周期信号控制随机共振的轴承故障检测方法,具体步骤如下: 1、利用采集系统采集振动加速度信号; [0014] The present invention is based on a random control signal applied to the resonance period of bearing failure detection method, the following steps: 1, using the vibration acceleration signal acquisition system;

将加速度传感器固定在振动台上,利用采集系统采集轴承的振动加速度信号即轴承故 The acceleration sensor is fixed to the vibrating table, using the acquisition system that is bearing bearing vibration acceleration signal so

障信号。 Fault signal.

[0015] 2、将轴承故障信号经变尺度方法变换为小频率信号; [0015] 2, the bearing fault signal becomes small scale method of converting a frequency signal;

根据频率压缩尺度比P定义压缩采样频率J;· = ,其中为故障信号的实际采样频率,£为频率压缩尺度比。 The frequency compression sampling frequency scale compression J P ratio defined above; * =, wherein the sampling frequency of the actual fault signal, the frequency £ scale compression ratio. 由压缩采样频率得到数值计算步长为Δ£ = 1//5,,使得轴承故障信号的每一频率成分(故障信号特征频率为/o )按频率压缩尺度比i?线性压缩,从而轴承故障信号的特征频率压缩为使之满足随机共振已有的绝热近似理论中小频率信号的条件。 A sampling frequency obtained by the numerical compression step is Δ £ = 1 // 5 ,, such that each frequency component of the bearing fault signal (fault signal wherein a frequency of / o) the frequency scale of the compression ratio I? Linear compression to bearing failure characteristic frequency of the signal is compressed so as to satisfy the resonance condition existing random theoretical adiabatic small frequency signal.

[0016] 3、将变尺度后的轴承故障信号作用到双稳系统,分析双稳系统输出的功率谱,按频率压缩尺度比恢复实测轴承故障信号的采集尺度; [0016] 3, the bearing fault signal is applied to the variable metric bistable system, power spectrum analysis of the output bistable system, the frequency compression recovery ratio Found scale bearing scale fault signal acquisition;

将变尺度后的轴承故障信号作用到双稳系统,通过分析双稳系统输出的功率谱,捕捉 The bearing fault signal is applied to the variable metric bistable system, by analyzing the power spectrum of the output bistable system, captures

故障信号的特征频率,最好按频率压缩尺度比A恢复故障信号特征频率为/。 Fault signal characteristic frequency, the frequency is preferably compressed scale ratio A fault recovery signal characteristic frequency /. =^ 4。 = ^ 4. [0017] 4、外加单一频率周期信号作为控制信号作用于双稳系统,调节控制信号的幅值, 从而人为地产生或加强随机共振,检测出轴承故障特征信号; [0017] 4 plus single frequency periodic signal as the control signal applied to the bistable system, adjusting the amplitude of the control signal to artificially generate or enhance resonance, bearing fault detected characteristic signal;

外加单一频率周期信号作为控制信号作用于双稳系统,通过调节控制信号的幅值£ ,双稳系统势垒高及Kramers逃逸速率发生改变,从而能够人为地产生或增强随机共振,有效地增强双稳系统输出功率谱在输入信号频率处的谱值,实现随机共振的控制, 检测出轴承故障特征信号。 Single frequency periodic signal is applied as a control signal applied to the bistable system, £, bistable system and Kramers escape barrier height by adjusting the rate of change of the amplitude control signal, can be artificially generated or enhanced resonance, effective to enhance bis stable system output spectrum of the power spectrum of the input signal frequency, to achieve control of stochastic resonance, bearing fault detected characteristic signal.

[0018] 以下通过实施例对本发明内容做进一步解释。 [0018] The following further explanation of the present invention through examples. 用该方法对轴承故障信号进行处理。 Bearing fault signal processed by this method. 实验数据由Case Western Reserve University (CWRU)提供。 Experimental data is provided by Case Western Reserve University (CWRU). 图2是在实验室理想环境中测得的轴承振动信号的功率谱图,采样频率为1200ID/&,转速为1797rpm。 FIG 2 is a power spectrum of the vibration signal over the bearing in a laboratory environment measured, sampling frequency 1200ID / &, speed of 1797rpm. 由于实际现场环境中往往存在大量噪声并且随机共振的产生也需适当的噪声,因此,以噪声强度ΰ = 2的高斯白噪声作为背景噪声增加到轴承振动信号中得到混合信号,功率谱图如图3 所示。 As the actual field environment and there is often a large amount of noise generated stochastic resonance also need appropriate noise, therefore, the noise intensity ΰ = 2 to Gaussian white noise as the background noise is increased bearing vibration signal obtained in the mixed signal, the power spectrum of FIG. 3 shown in FIG. 从图3可知,并无明显的故障特征信息。 Seen from FIG. 3, there is no obvious fault features. 设定系统结构参数a = 4 , A = 1且不存在外加周期信号即5 = 0,频率压缩尺度比i? = 1000 ,压缩采样频率为Jsr = i; / i? = 12。 Setting system configuration parameters a = 4, A = 1 without the presence of a periodic signal that is applied to 5 = 0, the frequency scale compression ratio i = 1000, the sampling frequency is compressed Jsr = i;? / I = 12?. 混合信号经压缩尺度比Λ = 1000线性压缩之后作用到双稳系统,双稳系统输出功率谱如图4所示。 The compressed mixed signal scales linearly ratio Λ = 1000 after the compression to the bistable system, bistable system output power spectrum as shown in FIG. 当采用图1所示的方式外加参数为5 = 31,Ω = 10/τ的单一频率信号作为控制信号作用于双稳系统,则双稳系统输出功率谱如图5所示。 When using the embodiment shown in FIG. 1 plus parameters 5 = 31, Ω = single frequency signals 10 / τ as a control signal applied to the bistable system, the power spectrum of the output bistable system shown in Fig. 与图4比较可知,图5的=0.1613处有一明显的谱峰值,经频率尺度还原有/ = /, · R = 161-3/fe,该频率即为故障特征信号的频率, 其频率理论值为。 4 and FIG comparison, FIG. 5 = 0.1613 at a distinct spectral peaks, the frequency scale with a reduction / = /, · R = 161-3 / fe, which is the frequency of the fault frequency characteristic signal, the frequency of theory for.

Claims (5)

1. 一种基于外加周期信号控制随机共振的轴承故障检测方法,其特征在于,具体步骤如下:(1)利用采集系统采集振动加速度信号;(2)将轴承故障信号经变尺度方法变换为小频率信号;(3)将变尺度后的轴承故障信号作用到双稳系统,分析双稳系统输出的功率谱,按频率压缩尺度比恢复实测轴承故障信号的采集尺度;(4)外加单一频率周期信号作为控制信号作用于双稳系统,调节控制信号的幅值,从而人为地产生或加强随机共振,检测出轴承故障特征信号。 1. A bearing fault detection method applied to the control based on the periodic signal of stochastic resonance, wherein the following steps: (1) using the acquired vibration acceleration signal acquisition system; (2) the bearing fault signal is converted into a small variable metric method frequency signal; (3) bearing fault signal is applied to the variable metric to a bistable system, analysis of the power spectrum of the bistable system output, the frequency compression scale ratio recovery acquisition scale Found bearing fault signal; and (4) plus a single clock cycle signal as the control signal applied to the bistable system, adjusting the amplitude of the control signal to artificially generate or enhance resonance, bearing fault detected characteristic signal.
2.根据权利要求1所述的基于外加周期信号控制随机共振的轴承故障检测方法,其特征是,所述步骤(1)具体为:将加速度传感器固定在振动台上,利用采集系统采集轴承的振动加速度信号即轴承故障信号。 The method for detecting bearing failure based on a random control signal applied to the resonance period of the claim 1, wherein said step (1) specifically includes: the acceleration sensor is fixed to the vibrating table, using the acquisition system bearing i.e., the vibration acceleration signal bearing fault signal.
3.根据权利要求1所述的基于外加周期信号控制随机共振的轴承故障检测方法,其特征是,所述步骤(2)具体为:根据频率压缩尺度比ί定义压缩采样频率厶=i;/S,/3为故障信号的实际采样频率;由压缩采样频率得到数值计算步长为Al = 1//^■,使得轴承故障信号的每一频率成分(故障信号特征频率为Jq )按频率压缩尺度比i?线性压缩,从而轴承故障信号的特征频率压缩为/, /S,使之满足随机共振已有的绝热近似理论中小频率信号的条件。 According to claim 1, applied to the periodic signals controlling the bearing fault detection method based on stochastic resonance, wherein said step (2) specifically includes: a compression ratio of the sampling frequency ί Si = i is defined according to the frequency scale compression; / S, / 3 is the actual sampling frequency fault signal; sampling frequency obtained by a compression step of numerical Al = 1 // ^ ■, such that each frequency component of the bearing fault signal (fault signal frequency characteristic Jq) by frequency compression scale than I? linear compression so that the characteristic frequency of the bearing fault signal compression /, / S, so as to satisfy the resonance condition theory of small random frequency signal existing adiabatic approximation.
4.根据权利要求1所述的一种基于外加周期信号控制随机共振的轴承故障检测方法, 其特征是,所述步骤(3)具体为:将变尺度后的轴承故障信号作用到双稳系统,通过分析双稳系统输出的功率谱,捕捉故障信号的特征频率,最好按频率压缩尺度比R恢复故障信号特征频率为/。 1 according to one of the bearing fault detection method based on Stochastic Resonance periodic signal applied to a control, wherein said step (3) as claimed in claim particular: the role of bearing failure signal to the variable metric bistable system by analyzing the power spectrum of the output bistable system, the capture fault signal characteristic frequency, the frequency is preferably compressed scale fault signal characteristic frequency recovery ratio R /. =/. · Λ。 = /. · Λ.
5.根据权利要求1所述的一种基于外加周期信号控制随机共振的轴承故障检测方法, 其特征是,所述步骤(4)具体为:外加单一频率周期信号作为控制信号作用于双稳系统,通过调节控制信号的幅值ί ,双稳系统势垒高及Kramers逃逸速率发生改变,从而能够人为地产生或增强随机共振,有效地增强双稳系统输出功率谱在输入信号频率处的谱值,实现随机共振的控制,检测出轴承故障特征信号。 The bearing of one of the fault detection method based on Stochastic Resonance periodic signal applied to a control, wherein said step (4) as claimed in claim particular: single-frequency periodic signal is applied as a control signal applied to the bistable system by adjusting the amplitude of the control signal ί, bistable system escape barrier height and Kramers rate changes, can be artificially generated or enhanced resonance, effectively enhancing the spectral values ​​of the bistable system outputs a power spectrum of the input signal at a frequency , to achieve control of stochastic resonance, bearing fault detected characteristic signal.
CN 201110096107 2011-04-18 2011-04-18 Bearing fault detection method based on manner of controlling stochastic resonance by external periodic signal CN102226740B (en)

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