CN103162710B - Based on MEMS gyro fault detection system and the detection method thereof of Wavelet Entropy - Google Patents

Based on MEMS gyro fault detection system and the detection method thereof of Wavelet Entropy Download PDF

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CN103162710B
CN103162710B CN201110420343.XA CN201110420343A CN103162710B CN 103162710 B CN103162710 B CN 103162710B CN 201110420343 A CN201110420343 A CN 201110420343A CN 103162710 B CN103162710 B CN 103162710B
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任亚飞
葛运旺
白旭灿
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Luoyang Institute of Science and Technology
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Abstract

本发明公开一种基于小波熵的MEMS陀螺故障检测系统,所述装置包括直流稳压电源模块、MEMS陀螺模块、运动载体模块、数据采集模块、小波多尺度分析模块、小波熵故障检测模块、小波多尺度重构模块和数据输出模块。本发明可以保证系统的可靠性和准确性。基于小波熵的MEMS陀螺故障检测系统性能稳定、工作可靠、体积小、性价比高,可以为各种载体设备提供准确的故障检测。

The invention discloses a MEMS gyroscope fault detection system based on wavelet entropy. Multi-scale reconstruction module and data output module. The invention can guarantee the reliability and accuracy of the system. The MEMS gyroscope fault detection system based on wavelet entropy has stable performance, reliable operation, small size and high cost performance, and can provide accurate fault detection for various carrier devices.

Description

基于小波熵的MEMS陀螺故障检测系统及其检测方法MEMS Gyroscope Fault Detection System and Its Detection Method Based on Wavelet Entropy

技术领域 technical field

本发明属于信号处理的技术领域,具体涉及一种具体涉及一种基于小波熵的微机电MEMS陀螺的故障检测系统。 The invention belongs to the technical field of signal processing, and in particular relates to a fault detection system for a micro-electromechanical MEMS gyroscope based on wavelet entropy.

背景技术 Background technique

MEMS(Micro-ElectroMechanicalSystem)陀螺是随着微电子和微机械加工技术的发展而研制出的一类新型角速率传感器,具有体积小,重量轻以及成本低,可靠性高,易于批量生产等传统陀螺无法比拟的优点。随着MEMS技术的飞速发展,其故障检测技术也一直备受关注。MEMS陀螺用于敏感模拟坐标系相对理想坐标系的偏角或角速度,是各类惯性系统中的核心部件。由于陀螺的广阔应用前景,国内外都对MEMS陀螺进行了大量的研究工作,但在MEMS陀螺的故障检测方面,迄今尚未有稳定可靠的产品。 MEMS (Micro-ElectroMechanicalSystem) gyro is a new type of angular rate sensor developed with the development of microelectronics and micromachining technology. It has the advantages of small size, light weight, low cost, high reliability, and easy mass production. Incomparable advantages. With the rapid development of MEMS technology, its fault detection technology has also attracted much attention. MEMS gyroscopes are used to sensitively simulate the declination or angular velocity of the coordinate system relative to the ideal coordinate system, and are the core components of various inertial systems. Due to the broad application prospects of gyroscopes, a lot of research work has been done on MEMS gyroscopes at home and abroad, but in terms of fault detection of MEMS gyroscopes, there has not been a stable and reliable product so far.

据故障检测所依据的信息源,故障检测技术一般可分为:来自传感器本身的检测和对传感器设置的滤波器信息进行检测。前者是内置检验具有在最低的级别上实现检测和隔离故障的优点;后者检测计算方法比较复杂,但是由于基于系统的统计学特性,因此更为敏感。 According to the information source on which the fault detection is based, the fault detection technology can generally be divided into: the detection from the sensor itself and the detection of the filter information set by the sensor. The former is that the built-in inspection has the advantage of detecting and isolating faults at the lowest level; the latter has a more complex detection calculation method, but is more sensitive because it is based on the statistical characteristics of the system.

现在大多MEMS陀螺的产品都是直接对陀螺的输出数据进行一些滤波处理,这种方法对精度的提高并不大,可靠性也不高。 Most MEMS gyroscope products now directly perform some filtering processing on the output data of the gyroscope. This method does not greatly improve the accuracy and the reliability is not high.

发明内容 Contents of the invention

本发明要解决的技术问题是MEMS陀螺产品检测时可靠性不高、精度不高,提供一种精度高、可靠性好的基于小波熵的MEMS陀螺故障检测系统,还介绍了该检测系统的检测方法,解决了现有检测方法敏感度低或是计算方法复杂的问题。 The technical problem to be solved by the present invention is that MEMS gyroscope products are detected with low reliability and low precision, and a MEMS gyroscope fault detection system based on wavelet entropy with high precision and good reliability is provided, and the detection system of the detection system is also introduced. The method solves the problems of low sensitivity or complex calculation methods of the existing detection methods.

本发明的技术方案是以下述方式实现的:一种基于小波熵的MEMS陀螺故障检测系统,包括直流稳压电源模块,用于向MEMS陀螺供电;MEMS陀螺模块,用于测量运动载体的运动信息;运动载体模块,通过测试夹具固定MEMS陀螺;数据采集模块,用于采集MEMS陀螺的信号,并传输至小波多尺度分析模块;小波多尺度分析模块,接收数据采集模块的信息,并将MEMS陀螺的信号进行多尺度小波分解后传输至小波熵故障检测模块;小波熵故障检测模块,接收小波多尺度分析模块的信息,检测MEMS陀螺的信号,把检测正常的信号输至小波多尺度重构模块,把检测故障的信号传输至MEMS陀螺模块,使得MEMS陀螺模块重置;小波多尺度重构模块,接收小波熵波故障检测模块的信息之后将该信息重构到原始尺度上,并传输至数据输出模块;数据输出模块,接收小波多尺度重构模块的输出信号,并输出经过故障检测和隔离后的数据。 The technical solution of the present invention is realized in the following manner: a MEMS gyro fault detection system based on wavelet entropy, including a DC stabilized power supply module, used to supply power to the MEMS gyro; MEMS gyro module, used to measure the motion information of the moving carrier ; The motion carrier module fixes the MEMS gyroscope through the test fixture; the data acquisition module is used to collect the signal of the MEMS gyroscope and transmits it to the wavelet multi-scale analysis module; the wavelet multi-scale analysis module receives the information of the data acquisition module and sends the MEMS gyroscope After multi-scale wavelet decomposition, the signal is transmitted to the wavelet entropy fault detection module; the wavelet entropy fault detection module receives the information from the wavelet multi-scale analysis module, detects the signal of the MEMS gyroscope, and transmits the detected normal signal to the wavelet multi-scale reconstruction module , transmit the fault detection signal to the MEMS gyro module, so that the MEMS gyro module resets; the wavelet multi-scale reconstruction module receives the information of the wavelet entropy wave fault detection module, reconstructs the information to the original scale, and transmits it to the data The output module; the data output module receives the output signal of the wavelet multi-scale reconstruction module, and outputs the data after fault detection and isolation.

一种基于小波熵的MEMS陀螺故障检测方法,是按照下述步骤进行的:MEMS陀螺模块测量运动载体模块的状态,把测量信息传递给数据采集模块;数据采集模块把采集到的信息输送至小波多尺度分析模块;小波多尺度分析模块对MEMS陀螺的信号进行小波多尺度分析,然后将分析结果传输至小波熵故障检测模块;小波熵故障检测模块接收小波多尺度分析的数据之后计算小波熵,并用小波熵对多尺度上的信号进行故障检测,若检测结果为故障,则将该检测故障的信号返回重置MEMS陀螺模块的工作状态,若检测结果为正常,则将检测后的信号传递给多尺度重构模块;多尺度重构模块将小波熵故障检测模块得到的正常信号进行小波多尺度重构后传输给数据输出模块,数据输出模块将多尺度重构模块的输出信息转换形式后输出。 A MEMS gyroscope fault detection method based on wavelet entropy is carried out according to the following steps: the MEMS gyroscope module measures the state of the motion carrier module, and transmits the measurement information to the data acquisition module; the data acquisition module transmits the collected information to the wavelet Multi-scale analysis module; the wavelet multi-scale analysis module performs wavelet multi-scale analysis on the signal of the MEMS gyroscope, and then transmits the analysis result to the wavelet entropy fault detection module; the wavelet entropy fault detection module calculates the wavelet entropy after receiving the wavelet multi-scale analysis data, And use wavelet entropy to detect faults on multi-scale signals. If the detection result is a fault, then return the detected fault signal to reset the working status of the MEMS gyro module. If the detection result is normal, pass the detected signal to Multi-scale reconstruction module; the multi-scale reconstruction module performs wavelet multi-scale reconstruction on the normal signal obtained by the wavelet entropy fault detection module and then transmits it to the data output module, and the data output module converts the output information of the multi-scale reconstruction module to output .

与现有的故障检测系统和检测方法相比,本发明具有下述优点:1、小波多尺度分析可以有效检测出信号的发展趋势,利用统计学上的熵值理论在信号的多尺度上对故障进行检测和处理,即对MEMS陀螺的数据源进行多层故障检测,以保证系统的可靠性和准确性。 Compared with existing fault detection systems and detection methods, the present invention has the following advantages: 1. The wavelet multi-scale analysis can effectively detect the development trend of the signal, and utilize the entropy value theory in statistics to analyze the fault on the multi-scale of the signal. Fault detection and processing, that is, multi-layer fault detection on the data source of the MEMS gyroscope to ensure the reliability and accuracy of the system.

2、基于小波熵的MEMS陀螺故障检测系统性能稳定、工作可靠、体积小、性价比高,可以为各种载体设备提供准确的故障检测。 2. The MEMS gyroscope fault detection system based on wavelet entropy has stable performance, reliable operation, small size and high cost performance, and can provide accurate fault detection for various carrier devices.

附图说明 Description of drawings

图1是本发明的原理框图。 Fig. 1 is a functional block diagram of the present invention.

图2是本发明中小波尺度分析流程图。 Fig. 2 is a flowchart of wavelet scale analysis in the present invention.

图3是本发明中小波重构流程图。 Fig. 3 is a flowchart of wavelet reconstruction in the present invention.

具体实施方式 Detailed ways

如图1所示,一种基于小波熵的MEMS陀螺故障检测系统,包括直流稳压电源模块1,用于向MEMS陀螺供电;MEMS陀螺模块2,用于测量运动载体的运动信息;运动载体模块3,通过测试夹具固定MEMS陀螺;数据采集模块4,用于采集MEMS陀螺的信号,并传输至小波多尺度分析模块5;小波多尺度分析模块5,接收数据采集模块4的信息,并将MEMS陀螺的信号进行多尺度小波分解后传输至小波熵故障检测模块6;小波熵故障检测模块6,接收小波多尺度分析模块5的信息,检测MEMS陀螺的信号,把检测正常的信号输至小波多尺度重构模块7,把检测故障的信号传输至MEMS陀螺模块2,使得MEMS陀螺模块2重置;小波多尺度重构模块7,接收小波熵波故障检测模块6的信息之后将该信息重构到原始尺度上,并传输至数据输出模块8;数据输出模块8,接收小波多尺度重构模块7的输出信号,并输出经过故障检测和隔离后的数据。 As shown in Figure 1, a MEMS gyroscope fault detection system based on wavelet entropy includes a DC stabilized power supply module 1, which is used to supply power to the MEMS gyroscope; a MEMS gyroscope module 2, which is used to measure the motion information of the moving carrier; the moving carrier module 3. Fix the MEMS gyroscope through the test fixture; the data acquisition module 4 is used to collect the signal of the MEMS gyroscope and transmit it to the wavelet multi-scale analysis module 5; the wavelet multi-scale analysis module 5 receives the information of the data acquisition module 4 and sends the MEMS The signal of the gyro is decomposed by multi-scale wavelet and then transmitted to the wavelet entropy fault detection module 6; the wavelet entropy fault detection module 6 receives the information of the wavelet multi-scale analysis module 5, detects the signal of the MEMS gyro, and transmits the detected normal signal to the wavelet multi-scale The scale reconstruction module 7 transmits the fault detection signal to the MEMS gyro module 2, so that the MEMS gyro module 2 is reset; the wavelet multi-scale reconstruction module 7 reconstructs the information after receiving the information of the wavelet entropy wave fault detection module 6 to the original scale, and transmitted to the data output module 8; the data output module 8 receives the output signal of the wavelet multi-scale reconstruction module 7, and outputs the data after fault detection and isolation.

本发明所述的基于小波熵的MEMS陀螺故障检测方法,是按照下述步骤进行的: The MEMS gyroscope fault detection method based on wavelet entropy of the present invention is carried out according to the following steps:

1.MEMS陀螺模块2测量运动载体模块3的状态,把测量信息传递给数据采集模块4;数据采集模块4把采集到的信息输送至小波多尺度分析模块5,小波多尺度分析模块5对MEMS陀螺的信号进行小波多尺度分析,然后将分析结果传输至小波熵故障检测模块6。 1. The MEMS gyroscope module 2 measures the state of the motion carrier module 3, and transmits the measurement information to the data acquisition module 4; the data acquisition module 4 transmits the collected information to the wavelet multi-scale analysis module 5, and the wavelet multi-scale analysis module 5 pairs MEMS The signal of the gyro is subjected to wavelet multi-scale analysis, and then the analysis result is transmitted to the wavelet entropy fault detection module 6 .

小波多尺度分析模块5对MEMS陀螺的信号进行小波多尺度分析,分析的流程如图2所示,具体按照以下过程实施: The wavelet multi-scale analysis module 5 performs wavelet multi-scale analysis on the signal of the MEMS gyroscope. The flow of analysis is shown in Figure 2, and it is specifically implemented according to the following process:

在尺度上,对于输入陀螺的信号序列,其离散小波变换的分析形式如下述公式: in scale On, for the signal sequence of the input gyroscope , the analytical form of discrete wavelet transform is as follows:

(1) (1)

式中,为小波变换中的尺度系数,为小波变换中的小波系数,选择合适的小波基函数,就可以得到相应的尺度系数和小波系数,由公式(1)可以得到尺度上陀螺信息中的近似信号和细节信号。继续这个过程对尺度上陀螺的近似信号进行离散小波分解,可以得到尺度上陀螺信息中的近似信号和细节信号,重复到最优的分解尺度N上,可以得到小波离散分解的近似信号和细节信号In the formula, is the scale coefficient in the wavelet transform, For the wavelet coefficients in the wavelet transform, select the appropriate wavelet basis function, and then the corresponding scale coefficients and wavelet coefficients can be obtained, and the scale coefficient can be obtained from the formula (1). Approximate signal in upper gyro information and detail signal . Continue this process for the scale Approximate signal of upper gyro Performing discrete wavelet decomposition, the scale can be obtained Approximate signal in upper gyro information and detail signal , repeated to the optimal decomposition scale N, the approximate signal of wavelet discrete decomposition can be obtained and detail signal .

然后将多尺度分析的结果:第(i~N)尺度上的细节信号和第N层尺度上的近似信号送到小波熵故障检测模块6等待处理。 Then the results of the multi-scale analysis: the detail signal on the (i~N)th scale and the approximate signal on the Nth layer scale are sent to the wavelet entropy fault detection module 6 for processing.

2.小波熵故障检测模块6接收小波多尺度分析的数据之后计算小波熵,并用小波熵对多尺度上的信号进行故障检测,若检测结果为故障,则将该检测故障的信号返回重置MEMS陀螺模块2的工作状态,若检测结果为正常,则将检测后的信号传递给多尺度重构模块7。 2. The wavelet entropy fault detection module 6 calculates the wavelet entropy after receiving the wavelet multi-scale analysis data, and uses the wavelet entropy to perform fault detection on multi-scale signals. If the detection result is a fault, the fault detection signal is returned to reset the MEMS The working state of the gyro module 2, if the detection result is normal, the detected signal is transmitted to the multi-scale reconstruction module 7.

小波熵故障检测模块6将收集到的由小波多尺度分析模块5输入的第(i~N)尺度上的细节信号和第N层尺度上的近似信号,按照以下算法计算小波熵,并用小波熵对多尺度上的信号进行故障检测。 The wavelet entropy fault detection module 6 calculates the wavelet entropy according to the following algorithm from the collected detail signals on the (i~N)th scale and the approximate signal on the Nth layer input by the wavelet multi-scale analysis module 5, and uses the wavelet entropy Fault detection on signals at multiple scales.

一方面,多尺度分解是把小波变换等效为一组镜像滤波器,在个尺度上将信号进行正交分解,相当于用一组高通和低通镜像滤波器对信号作逐步分解。低通滤波器产生信号的低频分量(信号近似),高通滤波器产生信号的高频分量(信号细节)。每次把上一尺度的低频分量作分解,得到下一尺度的两个分解分量。经过层分解后得到互相正交的分量: On the one hand, multi-scale decomposition is equivalent to wavelet transform as a set of mirror filters, in signal on a scale Orthogonal decomposition is equivalent to using a set of high-pass and low-pass mirror filters to decompose the signal step by step. Low-pass filter produces low-frequency components of the signal (signal approximation) , the high-pass filter produces high-frequency components of the signal (signal details) . Each time the low-frequency components of the previous scale are decomposed to obtain two decomposed components of the next scale. go through After layer decomposition, the mutually orthogonal components are obtained:

(2) (2)

由公式(2)可以看出一个信号的多尺度分解表达式是每个尺度上的细节信号和最粗尺度上的近似信号之和。 It can be seen from formula (2) that the multi-scale decomposition expression of a signal is the sum of the detail signal on each scale and the approximate signal on the coarsest scale.

另一方面,设为MEMS陀螺测量的随机信号序列由帕斯瓦尔方程可知,正交小波基下的小波变换具有能量守恒的性质,根据(2)式,基于时间序列的能量可以在尺度域上进行分解,即多分辨率分析的能量可分解为: On the other hand, let It is a random signal sequence measured by a MEMS gyroscope . According to Pasval's equation, the wavelet transform under the orthogonal wavelet basis has the property of energy conservation. According to (2), the energy based on the time series can be decomposed in the scale domain, namely The energy for multiresolution analysis can be decomposed as:

(3) (3)

则基于实测数据的方差为: Then the variance based on the measured data is:

(4) (4)

由于的逼近,由(4)式定义尺度j上的平均小波能量或小波方差,并归一化: because yes Approximation of , the average wavelet energy or wavelet variance on scale j is defined by equation (4), and normalized:

(5) (5)

式(5)中,为信号总能量,显然有:。归一化后能量序列称为能量序列的经验分布,为各尺度的小波能量与总能量的比例。结合信息熵的定义,我们采用小波各尺度的能量序列的分布取代信号的概率分布,这种基于能量分布得到的熵称为小波熵,其定义为: In formula (5), is the total energy of the signal, obviously: . normalized energy sequence It is called the empirical distribution of the energy sequence, which is the ratio of the wavelet energy of each scale to the total energy. Combined with the definition of information entropy, we use the distribution of energy sequences of wavelet scales Instead of the probability distribution of the signal, the entropy obtained based on the energy distribution is called wavelet entropy, which is defined as:

(6) (6)

从小波熵的定义可以看出,它计算时间序列在多个尺度上的样本熵值,体现了时间序列在尺度上的无规则度。若小波熵值突然增加,则序列的不稳定性增加,提示信息并不可靠,MEMS陀螺测量工作可能出现故障;若小波熵值没有很大的变化,代表信息稳定可靠,MEMS陀螺测量工作正常。 It can be seen from the definition of wavelet entropy that it calculates the sample entropy values of time series on multiple scales, reflecting the irregularity of time series on scales. If the wavelet entropy value increases suddenly, the instability of the sequence increases, indicating that the information is not reliable, and the MEMS gyro measurement may fail; if the wavelet entropy value does not change greatly, it means that the information is stable and reliable, and the MEMS gyro measurement is working normally.

如检测结果为出现故障,那么将该检测故障的信号返回重置MEMS陀螺模块2的工作状态。如果检测结果正常,把检测后可靠的多尺度MEMS陀螺信号,传递给小波多尺度重构模块7。 If the detection result is a failure, then the signal of the detection failure is returned to reset the working state of the MEMS gyro module 2 . If the detection result is normal, the reliable multi-scale MEMS gyroscope signal after detection is passed to the wavelet multi-scale reconstruction module 7 .

3.多尺度重构模块7将小波熵故障检测模块6的得到的正常信号进行小波多尺度重构后传输给数据输出模块8。 3. The multi-scale reconstruction module 7 performs wavelet multi-scale reconstruction on the normal signal obtained by the wavelet entropy fault detection module 6 and transmits it to the data output module 8 .

如图3所示,多尺度重构模块7中小波重构具体过程如下: As shown in Figure 3, the specific process of wavelet reconstruction in multi-scale reconstruction module 7 is as follows:

(7) (7)

式(7)中,分别是尺度i上的近似信号和细节信号,分别是尺度i上的尺度系数和小波系数,是经过小波重构得到的尺度i+1上的近似信号,即尺度i+1上陀螺的近似信息。对各个尺度上的信号进行多尺度的重构,得到原始尺度上的MEMS陀螺信号,将其传送给数据输出模块8。 In formula (7), and are the approximate signal and detail signal on scale i, respectively, and are the scale coefficients and wavelet coefficients on scale i, respectively, is the approximate signal on scale i+1 obtained through wavelet reconstruction, that is, the approximate information of the gyroscope on scale i+1. Multi-scale reconstruction is performed on the signals on each scale to obtain the MEMS gyroscope signal on the original scale, and send it to the data output module 8 .

4.数据输出模块8将多尺度重构模块7的输出信息转换成需要的形式后输出,以为后续设备提供准确,可靠的MEMS陀螺测量的信息,完成基于小波熵的MEMS陀螺故障检测和隔离。 4. The data output module 8 converts the output information of the multi-scale reconstruction module 7 into the required form and then outputs it, so as to provide accurate and reliable MEMS gyro measurement information for subsequent equipment, and complete the MEMS gyro fault detection and isolation based on wavelet entropy.

本发明采用小波分析法,小波分析具有多分辨率特性,可以给同一层次上的信息进行多尺度分解,得到多层次上的信息。信息熵可以表征信源的总体特征,是信源输出信息的不确定性和事件发生的随机性的统计学量度。小波熵是在小波的多尺度分解基础上结合信息熵的研究,利用对MEMS陀螺的信号进行分析,达到内置的敏感的故障检测效果。 The invention adopts the wavelet analysis method, and the wavelet analysis has multi-resolution characteristics, and can decompose the information on the same level in multiple scales to obtain information on multiple levels. Information entropy can characterize the overall characteristics of the information source, and is a statistical measure of the uncertainty of the output information of the information source and the randomness of event occurrence. Wavelet entropy is based on the multi-scale decomposition of wavelet combined with the research of information entropy, and uses the signal analysis of MEMS gyro to achieve the built-in sensitive fault detection effect.

Claims (2)

1.一种基于小波熵的MEMS陀螺故障检测系统,其特征在于:包括直流稳压电源模块(1),用于向MEMS陀螺供电; 1. A MEMS gyroscope fault detection system based on wavelet entropy, characterized in that: comprising a DC stabilized power supply module (1), for supplying power to the MEMS gyroscope; MEMS陀螺模块(2),用于测量运动载体的运动信息; MEMS gyro module (2), used to measure the motion information of the motion carrier; 运动载体模块(3),通过测试夹具固定MEMS陀螺; The motion carrier module (3), fixes the MEMS gyroscope through the test fixture; 数据采集模块(4),用于采集MEMS陀螺的信号,并传输至小波多尺度分析模块(5); The data acquisition module (4) is used to collect the signal of the MEMS gyroscope and transmit it to the wavelet multi-scale analysis module (5); 小波多尺度分析模块(5),接收数据采集模块(4)的信息,并将MEMS陀螺的信号进行多尺度小波分解后传输至小波熵故障检测模块(6); The wavelet multi-scale analysis module (5) receives the information from the data acquisition module (4), and decomposes the signal of the MEMS gyroscope by multi-scale wavelet, and then transmits it to the wavelet entropy fault detection module (6); 小波熵故障检测模块(6),接收小波多尺度分析模块(5)的信息,检测MEMS陀螺的信号,把检测正常的信号输至小波多尺度重构模块(7),把检测故障的信号传输至MEMS陀螺模块(2),使得MEMS陀螺模块(2)重置; The wavelet entropy fault detection module (6) receives the information from the wavelet multi-scale analysis module (5), detects the signal of the MEMS gyroscope, and transmits the detected normal signal to the wavelet multi-scale reconstruction module (7), and transmits the detected fault signal to to the MEMS gyro module (2), so that the MEMS gyro module (2) resets; 小波多尺度重构模块(7),接收小波熵波故障检测模块(6)的信息之后将该信息重构到原始尺度上,并传输至数据输出模块(8); The wavelet multi-scale reconstruction module (7), after receiving the information of the wavelet entropy wave fault detection module (6), reconstructs the information to the original scale, and transmits the information to the data output module (8); 数据输出模块(8),接收小波多尺度重构模块(7)的输出信号,并输出经过故障检测和隔离后的数据。 The data output module (8) receives the output signal of the wavelet multi-scale reconstruction module (7), and outputs the data after fault detection and isolation. 2.一种基于小波熵的MEMS陀螺故障检测方法,其特征在于是按照下述步骤进行的:MEMS陀螺模块(2)测量运动载体模块(3)的状态,把测量信息传递给数据采集模块(4);数据采集模块(4)把采集到的信息输送至小波多尺度分析模块(5);小波多尺度分析模块(5)对MEMS陀螺的信号进行小波多尺度分析,然后将分析结果传输至小波熵故障检测模块(6);小波熵故障检测模块(6)接收小波多尺度分析的数据之后计算小波熵,并用小波熵对多尺度上的信号进行故障检测,若检测结果为故障,则将该检测故障的信号返回重置MEMS陀螺模块(2)的工作状态,若检测结果为正常,则将检测后的信号传递给多尺度重构模块(7);多尺度重构模块(7)将小波熵故障检测模块(6)得到的正常信号进行小波多尺度重构后传输给数据输出模块(8),数据输出模块(8)将多尺度重构模块(7)的输出信息转换形式后输出。 2. A MEMS gyroscope fault detection method based on wavelet entropy is characterized in that it is carried out according to the following steps: the MEMS gyroscope module (2) measures the state of the motion carrier module (3), and the measurement information is delivered to the data acquisition module ( 4); the data acquisition module (4) transmits the collected information to the wavelet multi-scale analysis module (5); the wavelet multi-scale analysis module (5) performs wavelet multi-scale analysis on the signal of the MEMS gyroscope, and then transmits the analysis results to The wavelet entropy fault detection module (6); the wavelet entropy fault detection module (6) calculates the wavelet entropy after receiving the wavelet multi-scale analysis data, and uses the wavelet entropy to perform fault detection on signals on multiple scales, if the detection result is a fault, the The fault detection signal is returned to reset the working state of the MEMS gyroscope module (2), if the detection result is normal, the detected signal is passed to the multi-scale reconstruction module (7); the multi-scale reconstruction module (7) will The normal signal obtained by the wavelet entropy fault detection module (6) is reconstructed by wavelet multi-scale and then transmitted to the data output module (8), and the data output module (8) converts the output information of the multi-scale reconstruction module (7) to output .
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN201017233Y (en) * 2006-11-23 2008-02-06 浙江大学 Fault Diagnosis Device for Industrial Production Process Based on Wavelet Analysis
CN101201937A (en) * 2007-09-18 2008-06-18 上海医疗器械厂有限公司 Digital image enhancement method and device based on wavelet restruction and decompose
CN101711446A (en) * 2007-06-15 2010-05-19 通用电气公司 Mems based motor starter with motor failure detection
CN102023010A (en) * 2010-10-26 2011-04-20 西安理工大学 MEMS (micro-electromechanical system)-based wavelet field multisensor information fusion system and fusion method
CN102143775A (en) * 2008-10-22 2011-08-03 生物技术公司 Mems fluid pump with integrated pressure sensor for dysfunction detection

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN201017233Y (en) * 2006-11-23 2008-02-06 浙江大学 Fault Diagnosis Device for Industrial Production Process Based on Wavelet Analysis
CN101711446A (en) * 2007-06-15 2010-05-19 通用电气公司 Mems based motor starter with motor failure detection
CN101201937A (en) * 2007-09-18 2008-06-18 上海医疗器械厂有限公司 Digital image enhancement method and device based on wavelet restruction and decompose
CN102143775A (en) * 2008-10-22 2011-08-03 生物技术公司 Mems fluid pump with integrated pressure sensor for dysfunction detection
CN102023010A (en) * 2010-10-26 2011-04-20 西安理工大学 MEMS (micro-electromechanical system)-based wavelet field multisensor information fusion system and fusion method

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
《多尺度小波分解融合在微机电陀螺数据处理中的应用》;任亚飞等;《应用科学学报》;20100731;第28卷(第4期);394-398 *
《微机电陀螺数据融合中的小波基的选择》;任亚飞等;《信息与控制》;20101031;第39卷(第5期);646-656 *

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