CN103438983B - Data processing method of signal random average spectrums - Google Patents
Data processing method of signal random average spectrums Download PDFInfo
- Publication number
- CN103438983B CN103438983B CN201310323803.6A CN201310323803A CN103438983B CN 103438983 B CN103438983 B CN 103438983B CN 201310323803 A CN201310323803 A CN 201310323803A CN 103438983 B CN103438983 B CN 103438983B
- Authority
- CN
- China
- Prior art keywords
- signal
- sample signal
- section sample
- sample
- spectrum
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Landscapes
- Measuring And Recording Apparatus For Diagnosis (AREA)
- Ultra Sonic Daignosis Equipment (AREA)
- Investigating Or Analyzing Materials By The Use Of Ultrasonic Waves (AREA)
- Indication And Recording Devices For Special Purposes And Tariff Metering Devices (AREA)
Abstract
The invention discloses a data processing method of signal random average spectrums. The method comprises the steps that according to mechanical vibration original time-domain signals collected by a vibration sensor, n sections of signals which are randomly and continuously extracted from the original time-domain signals are used as sample signals, and the lengths of the n sections of sample signals are the same; Fourier transformation is respectively carried out on the n sections of sample signals, and a frequency spectrum of each section of sample signal is obtained; average value calculation is carried out on each spectrum line amplitude of the obtained frequency spectrums of the n sections of sample signals, and the average value of each spectrum line amplitude is obtained, namely the random average spectrums of the n sections of sample signals. According to the data processing method of the signal random average spectrums, the noise intensity in the signal frequency spectrums can be remarkably reduced, the data processing process is simple and easy to operate, the noise intensity can be effectively reduced, and the signal to noise ratio of the signals is improved.
Description
Technical field
The present invention relates to the data processing method of a kind of signal stochastic averagina spectrum, being particularly useful for processing the early stage of vibration signal, reduce the noise intensity in vibration signals spectrograph.
Background technology
Vibration detection is one the most frequently used in current Mechanical System Trouble diagnostic method.Mechanical part often comprises different information with the vibration signal under malfunction in normal operation, can realize the mechanical fault diagnosis based on vibration signal by signal analysis technology.But, vibration signal under actual conditions often comprises strong noise, neighbourhood noise and other interference vibration often have an impact to the result detected, and only have by the noise intensity in relevant signal processing method reduction signal, effectively could carry out subsequent analysis to vibration signal.
At present for the noise comprised in signal, a kind of method is processed by wave filter, and the method deals with process complexity, may destroy the useful information in signal while stress release treatment; Another kind method, is divided into several sections by original signal, and average to after each section of process respectively, because original signal strength is limited, sample size is less, and the method effectively can not reduce the noise intensity in signal.Method of the present invention is improved on the basis of the method, adds the quantity of sample, improves the elimination ability to noise.
Summary of the invention
Technical matters: the object of the invention is the weak point overcome in prior art, provide a kind of method simple, sample size can be increased, improve the data processing method that the signal stochastic averagina of noise elimination ability is composed.
Technical scheme: the data processing method of signal stochastic averagina spectrum of the present invention, comprise the mechanical vibration original time domain signal that the length collected by vibration transducer is L, step is as follows:
(1) in original time domain signal, randomly draw n section sample signal, n section sample signal mutual statistical is independent, and ensures that the length l of gained n section sample signal is identical, and meets l<L;
(2) respectively Fourier transform is carried out to gained n section sample signal, obtain n section sample signal frequency spectrum;
(3) respectively mean value computation is carried out to each spectral line amplitude of gained n section sample signal frequency spectrum, obtain the average of each bar spectral line amplitude, be i.e. signal stochastic averagina spectrum.
The hop count of the described n section sample signal randomly drawed is 100 ~ 10000 sections; When the hop count of n section sample signal is infinitely great, in n section sample signal stochastic averagina spectrum, the variance of each bar spectral line amplitude is δ/n, and wherein δ is the variance of sample signal each bar spectral line amplitude.
Beneficial effect: the inventive method data handling procedure is simple, be easy to the intensity operating, can effectively reduce noise.The method sampling scope comprises original signal institute a little, and grab sample quantity is large, effectively can retain the useful information of original signal.Compared to traditional method signal being divided into several sections of spectrum calculating that are averaged, the sample size of grab sample of the present invention is large, and not by the restriction of original signal strength, the standard variance of gained sample is little, more effectively can reduce the intensity of noise, improve the signal to noise ratio (S/N ratio) of vibration signal.Take length as the original time domain signal of L be example, sample signal length is l, conventionally only can obtain about L/l section sample signal, and cannot ensure the effect of its noise reduction under the condition of nonwhite noise; Adopt the signal processing method of stochastic averagina spectrum, the sample signal that L-l+1 section is different can be obtained, and can ensure that in stochastic averagina spectrum, the variance of each bar spectral line amplitude is about δ/n, wherein the variance of δ to be length be each bar spectral line amplitude of the sample signal of l.Realize by simple data processing, reduce the noise intensity that signal comprises, improve signal to noise ratio (S/N ratio), for follow-up analyzing and processing provides basis.
Accompanying drawing explanation
Fig. 1 is course of work schematic flow sheet of the present invention.
Embodiment
As shown in Figure 1, the data processing method of signal stochastic averagina spectrum of the present invention:
(1) length first collected by vibration transducer is the mechanical vibration original time domain signal of L, then in original time domain signal, randomly draw n segment length is that the continuous signal of l is as sample signal, have L-l+1 kind situation when carrying out grab sample at every turn, because will a large amount of grab sample be carried out, for ensureing the validity of grab sample, should ensure that the length l of gained n section sample signal is identical, and meet l<L; The less de-noising effect of sample standard deviation of the larger then gained of sample size is better in theory, the hop count of the n section sample signal randomly drawed in practical application is determined according to the computing power of signal handling equipment, generally get 100 ~ 10000 sections, when signal handling equipment computing power allows, n is larger for sample signal hop count, the effect of data processing is better, and namely in averaging spectrum, the variance of each bar spectral line amplitude is less; When the hop count of n section sample signal is infinitely great, in n section sample signal stochastic averagina spectrum, the variance of each bar spectral line amplitude is about δ/n, and wherein δ is the variance of n section sample signal each bar spectral line amplitude;
(2) respectively Fourier transform is carried out to gained n section sample signal, obtain the frequency spectrum that each section of sample signal is corresponding; Because the length of sample signal is identical, then corresponding frequency spectrum has identical spectral line quantity;
(3) for each spectral line of n section sample signal frequency spectrum, ask the average of this spectral line amplitude in n section sample signal section sample spectra, the average of each bar spectral line of gained is the signal spectrum after reducing noise intensity.
Claims (1)
1. a data processing method for signal stochastic averagina spectrum, comprising the length collected by vibration transducer is
lmechanical vibration original time domain signal, it is characterized in that:
(1) randomly draw in original time domain signal
nsection sample signal,
nsection sample signal mutual statistical is independent, and ensures gained
nthe length of section sample signal
lidentical, and meet
l<
l; Describedly to randomly draw
nthe hop count of section sample signal is 100 ~ 10000 sections; When
nwhen the hop count of section sample signal is infinitely great
, nin section sample signal stochastic averagina spectrum, the variance of each bar spectral line amplitude is
δ/n, wherein
δit is the variance of sample signal each bar spectral line amplitude;
(2) respectively to gained
nsection sample signal carries out Fourier transform, obtains
nsection sample signal frequency spectrum;
(3) respectively to gained
neach spectral line amplitude of section sample signal frequency spectrum carries out mean value computation, obtains the average of each bar spectral line amplitude, i.e. signal stochastic averagina spectrum.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310323803.6A CN103438983B (en) | 2013-07-29 | 2013-07-29 | Data processing method of signal random average spectrums |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310323803.6A CN103438983B (en) | 2013-07-29 | 2013-07-29 | Data processing method of signal random average spectrums |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103438983A CN103438983A (en) | 2013-12-11 |
| CN103438983B true CN103438983B (en) | 2015-04-15 |
Family
ID=49692679
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201310323803.6A Active CN103438983B (en) | 2013-07-29 | 2013-07-29 | Data processing method of signal random average spectrums |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103438983B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103743470B (en) * | 2013-12-23 | 2016-05-18 | 广西科技大学 | A kind of automobile noise frequency spectrum analysis method |
| CN104156591B (en) * | 2014-08-06 | 2017-02-15 | 北京信息科技大学 | Markov fault trend prediction method |
| CN105528583A (en) * | 2015-12-17 | 2016-04-27 | 清华大学深圳研究生院 | Characteristic extraction method based on real number form Fourier transform and fault diagnosis method |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101251446A (en) * | 2008-04-16 | 2008-08-27 | 邓艾东 | Method for denoising bump-scrape acoustic emission signal based on discrete fraction cosine transform |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2008309955A (en) * | 2007-06-13 | 2008-12-25 | Toshiba Corp | Noise suppresser |
-
2013
- 2013-07-29 CN CN201310323803.6A patent/CN103438983B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101251446A (en) * | 2008-04-16 | 2008-08-27 | 邓艾东 | Method for denoising bump-scrape acoustic emission signal based on discrete fraction cosine transform |
Non-Patent Citations (2)
| Title |
|---|
| 基于分段谱平均的高精度频率估计方法;杨小女等;《信息工程大学学报》;20070331;第8卷(第1期);第60页右栏至第62页左栏 * |
| 滚动轴承故障诊断中数据不均衡问题的研究;刘天羽等;《计算机工程与科学》;20101231;第32卷(第5期);第151页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103438983A (en) | 2013-12-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Li et al. | Application of EEMD and improved frequency band entropy in bearing fault feature extraction | |
| Hemmati et al. | Roller bearing acoustic signature extraction by wavelet packet transform, applications in fault detection and size estimation | |
| Gao et al. | A novel wind turbine fault diagnosis method based on intergral extension load mean decomposition multiscale entropy and least squares support vector machine | |
| Jiang et al. | Demodulation for hydraulic pump fault signals based on local mean decomposition and improved adaptive multiscale morphology analysis | |
| CN102840907B (en) | Rolling bearing vibration signal characteristic extracting and analyzing method under early fault state | |
| Liu et al. | A hybrid fault diagnosis method based on second generation wavelet de-noising and local mean decomposition for rotating machinery | |
| CN103499445B (en) | A kind of Fault Diagnosis of Roller Bearings based on time-frequency slice analysis | |
| Li et al. | An enhanced morphology gradient product filter for bearing fault detection | |
| Liu et al. | Synchrosqueezing extracting transform and its application in bearing fault diagnosis under non-stationary conditions | |
| Li et al. | An enhanced rolling bearing fault detection method combining sparse code shrinkage denoising with fast spectral correlation | |
| Hu et al. | Bearing fault diagnosis based on an improved morphological filter | |
| Li et al. | Research on test bench bearing fault diagnosis of improved EEMD based on improved adaptive resonance technology | |
| Zhang et al. | A new feature extraction approach using improved symbolic aggregate approximation for machinery intelligent diagnosis | |
| CN103076177A (en) | Rolling bearing fault detection method based on vibration detection | |
| CN108458871A (en) | A kind of gearbox fault recognition methods based on improvement experience wavelet transformation | |
| CN111769810B (en) | Fluid mechanical modulation frequency extraction method based on energy kurtosis spectrum | |
| He et al. | Machine fault signature analysis by midpoint-based empirical mode decomposition | |
| JP2014206403A (en) | Rolling bearing diagnostic system | |
| CN103438983B (en) | Data processing method of signal random average spectrums | |
| CN104330258A (en) | Method for identifying grey relational degree of rolling bearing fault based on characteristic parameters | |
| Guo et al. | An enhanced modulation signal bispectrum analysis for bearing fault detection based on non-Gaussian noise suppression | |
| WO2020057066A1 (en) | Fault diagnosis method employing enhanced modulation signal bispectrum analysis for rolling bearing | |
| Shuuji et al. | Low-speed bearing fault diagnosis based on improved statistical filtering and convolutional neural network | |
| CN111504640B (en) | Weighted sliding window second-order synchronous compression S transformation bearing fault diagnosis method | |
| CN108287073B (en) | Resonance band selection method based on singular value component frequency domain spectrum |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant |