CN111890126A - Early turning flutter early warning and monitoring method based on sound pressure energy kurtosis index - Google Patents
Early turning flutter early warning and monitoring method based on sound pressure energy kurtosis index Download PDFInfo
- Publication number
- CN111890126A CN111890126A CN202010634862.5A CN202010634862A CN111890126A CN 111890126 A CN111890126 A CN 111890126A CN 202010634862 A CN202010634862 A CN 202010634862A CN 111890126 A CN111890126 A CN 111890126A
- Authority
- CN
- China
- Prior art keywords
- flutter
- energy
- sound pressure
- frequency band
- wavelet packet
- 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.)
- Granted
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 26
- 238000000034 method Methods 0.000 title claims description 30
- 238000011081 inoculation Methods 0.000 claims abstract description 23
- 238000005516 engineering process Methods 0.000 claims abstract description 8
- 238000012545 processing Methods 0.000 claims description 20
- 238000000354 decomposition reaction Methods 0.000 claims description 18
- 238000005520 cutting process Methods 0.000 claims description 17
- 238000005070 sampling Methods 0.000 claims description 10
- 238000012360 testing method Methods 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000003860 storage Methods 0.000 claims description 3
- 230000004044 response Effects 0.000 abstract description 8
- 238000003754 machining Methods 0.000 abstract description 7
- 238000011160 research Methods 0.000 abstract description 2
- 238000009826 distribution Methods 0.000 description 5
- 230000009466 transformation Effects 0.000 description 5
- 230000008859 change Effects 0.000 description 4
- 238000000605 extraction Methods 0.000 description 4
- 230000001133 acceleration Effects 0.000 description 3
- TZCXTZWJZNENPQ-UHFFFAOYSA-L barium sulfate Chemical compound [Ba+2].[O-]S([O-])(=O)=O TZCXTZWJZNENPQ-UHFFFAOYSA-L 0.000 description 3
- 238000013524 data verification Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 238000010276 construction Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000004880 explosion Methods 0.000 description 2
- 238000009434 installation Methods 0.000 description 2
- 238000005316 response function Methods 0.000 description 2
- 238000001228 spectrum Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 230000005764 inhibitory process Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 208000024891 symptom Diseases 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
- B23Q17/00—Arrangements for observing, indicating or measuring on machine tools
- B23Q17/09—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring cutting pressure or for determining cutting-tool condition, e.g. cutting ability, load on tool
- B23Q17/0952—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring cutting pressure or for determining cutting-tool condition, e.g. cutting ability, load on tool during machining
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
- B23Q17/00—Arrangements for observing, indicating or measuring on machine tools
- B23Q17/12—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring vibration
Landscapes
- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
Abstract
The invention belongs to the field of flutter monitoring in the machining process; most research parameter structures can accurately judge the state after the chatter occurs at present, but the parameters have very slow response to early chatter signs and the selection of a threshold value is not easy to determineK E And energy ratioRWhen the preset threshold value is exceeded, the flutter inoculation characteristic early warning and/or flutter alarm is sent out, and the problems that early flutter signals are weak, monitoring is not easy, and response to flutter signal characteristics is slow in the existing flutter monitoring technology are solved.
Description
Technical Field
The invention belongs to the field of flutter monitoring in a machining process, and particularly relates to an early turning flutter early warning and monitoring method based on a sound pressure energy kurtosis index.
Background
In the machining process, due to uneven cutting thickness, the whole turning system consisting of a workpiece and a cutter is easy to vibrate, the machining precision of the workpiece is seriously influenced, the service lives of the cutter and a machine tool are reduced, and cutting noise polluting the surrounding environment is generated. With the development of factory automation, the flexibility of machining requires that the cutting process can be performed on different workpieces and under different working conditions, so that the method for preventing and controlling chatter often cannot fundamentally prevent the chatter phenomenon, and online monitoring, forecasting and controlling the chatter phenomenon become a key technology for improving the stability of a cutting system. The flutter monitoring technology consists of three links of sensor selection, characteristic signal extraction and forecast judgment.
At present, most researchers at home and abroad adopt vibration measuring sensors (such as vibration displacement and vibration acceleration), force sensors and current signal sensors (traditional vibration and force measuring sensors) and extract characteristic information representing flutter from the vibration measuring sensors, but the sensors are directly contacted with a workpiece, a cutter and a main shaft, so that the installation is inconvenient. The disclosure of the method for detecting vibration signals by using an acceleration sensor includes: the patent number is CN106021906A, and the technical scheme is provided by a flutter online monitoring method based on cepstrum analysis. Compared with a vibration acceleration signal, the force sensor is less interfered by the outside, but is more complex to install.
For the extraction of characteristic signals, early weak flutter signs are easily submerged in other signals, and the occurrence of the flutter signs is generally difficult to judge directly through sensing signals, so that flutter sign characteristics (such as time domain variance, frequency spectrogram and the like) are obtained in a time domain or a frequency domain by using a signal processing method, the variance is easily influenced by noise to cause misjudgment, and fast Fourier transform has no capacity for non-stable signals.
The accuracy and the rapidity of the forecast judgment are related to the construction of characteristic parameters and the selection of threshold values, most of research parameter constructions can accurately judge the state after the flutter occurs at present, but the parameters have extremely slow response to early flutter symptoms, and the selection of the threshold values is not easy to determine. The key of the flutter monitoring is the accuracy and timeliness of signal feature extraction, namely, the structure of a feature parameter must be capable of fully reflecting the nature and the feature of cutting flutter, and the simplicity and the feasibility of signal acquisition and data processing are fully considered; at present, most detection means and feature extraction methods can accurately express the occurrence of flutter, but structural feature parameters do not respond quickly to the start of the flutter inoculation.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide an early turning chatter early warning and monitoring method based on a sound pressure energy kurtosis index.
In order to achieve the purpose, the invention provides the following technical scheme:
an early turning vibration early warning and monitoring method based on sound pressure energy kurtosis index includes collecting sound pressure signals in a cutting process in real time by a sound pressure signal sensor, transmitting the sound pressure signals to a computer for storage by taking each rotation of a workpiece as a data processing interval, carrying out wavelet packet decomposition on the sound pressure signals, extracting wavelet packet coefficients of a target vibration frequency band, calculating wavelet packet energy of the target vibration frequency band, and calculating energy kurtosis index K of the target vibration frequency bandEAnd comparing the energy ratio R with a set threshold value respectively when the energy kurtosis index K is reachedESending out flutter inoculation characteristic early warning when the threshold value is larger than a set threshold value; when the energy ratio R is larger than a set threshold value, a flutter alarm is given, and the method specifically comprises the following steps:
fs is the sampling frequency in units: hz; Ω is the spindle speed, unit: rpm; eta is a data overlapping coefficient;
Further, the energy ratio R is calculated using the following formula:
Further, in step 4, the energy kurtosis index K of the wavelet packetEExpressed as:
where N is the number of signal packet samples, Em，iRepresents the energy of the wavelet packet of the mth layer, the ith band,
for sampling the expected energy value of the target frequency band of the signal, ErmsIs the energy root mean square value.
In conclusion, the invention has the following beneficial effects:
the method comprises the steps of carrying out wavelet packet decomposition on a sound pressure signal acquired in real time in the cutting process, taking a frequency band of a first-order natural frequency of a workpiece cutter system as a target flutter frequency band after wavelet packet decomposition, obtaining signal characteristics of the target flutter frequency band, and calculating an energy kurtosis index and an energy ratio of the wavelet packet of the target frequency band to be respectively used as characteristic parameters of starting flutter inoculation and flutter outbreak; the method better solves the problems that the early flutter signal is weak, the monitoring is not easy and the response to the characteristics of the flutter signal is slow in the existing flutter monitoring technology, the energy kurtosis index of the wavelet packet can more quickly reflect the sudden change information of the early flutter from inexistence to inexistence, the energy ratio can more accurately express that the energy of the flutter frequency in the whole signal is dominant, and the method can be used as the accurate index of the flutter outbreak; compared with other traditional contact sensors (such as vibration sensors and force sensors), the acoustic pressure sensor is convenient to install.
Drawings
FIG. 1 is a wavelet packet decomposition tree;
FIG. 2 is a monitoring flow diagram of the present invention;
FIG. 3 is a frequency response function plot of FRF (frequency response function) obtained from a hammered bar test;
FIG. 4 is a time-frequency spectrum gray scale diagram obtained by CWT (continuous wave transform) transformation of sound pressure signals monitored by turning bar test flutter;
FIG. 5 is a graph showing the energy distribution trend of a target frequency band wavelet packet corresponding to the flutter inoculation process of the turning bar test flutter monitoring;
FIG. 6 is an energy kurtosis index K of the flutter monitoring in the turning bar testEA change trend graph of the curve and the energy ratio R curve;
FIG. 7 is a data verification diagram of a turned bar stock: sound pressure signal and characteristic parameter curve trend.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
As shown in FIGS. 1-2, the invention discloses an early turning chatter warning and monitoring method based on an acoustic pressure energy kurtosis index, in order to discover the sign of the vibration earlier and win more response time for the inhibition of the vibration so as to solve the problems that the early vibration signal is weak, the monitoring is difficult and the response to the characteristic of the vibration signal is slow in the existing vibration monitoring technology, a sound pressure signal sensor with the model number of INV9206A is adopted to collect the sound pressure signal Z (t) in the cutting process in real time, the sound pressure signal is transmitted to a computer for storage by taking each rotation of the workpiece as a data processing interval, in order to quickly and accurately reflect the information of the early inoculation of the flutter, performing wavelet packet m-layer decomposition on the data of each real-time acquired sound pressure signal, extracting the wavelet packet coefficient of a target flutter frequency band, and calculating the wavelet packet energy of the target flutter frequency band, and calculating the energy kurtosis index K of the target flutter frequency band.EAnd the energy ratio R is compared with a set threshold value respectively, and an energy kurtosis index K in the method is combined with literature data and experimental dataEIs set to 3, the threshold of the energy ratio R is set to 60%, and when the energy kurtosis index K is setESending out flutter inoculation characteristic early warning when the threshold value is larger than a set threshold value; when the energy ratio R is larger than a set threshold value, the flutter explosion is considered, and the method specifically comprises the following steps:
fs is the sampling frequency in units: hz; Ω is the spindle speed, unit: rpm; η is the data overlap factor.
In step 4, the wavelet packet transformation is the popularization of the discrete wavelet transformation, the defect that the discrete wavelet transformation cannot decompose the high-frequency signals is effectively overcome, the characteristic of any multi-scale and very high time-frequency resolution are realized, and in the wavelet packet decomposition tree, the corresponding wavelet packet coefficient of a node is
According to the Pasval theorem:
wavelet packet energy kurtosis index KEExpressed as:
where N is the number of signal packet samples, Em，iRepresents the energy of the wavelet packet of the mth layer, the ith band,
for sampling the expected energy value of the target frequency band of the signal, ErmsWhen the energy ratio R of the target flutter frequency band is greater than or equal to the threshold value, the energy of the frequency band is considered to be dominant in the whole frequency domain range, the energy frequency band is transferred, and the energy is concentrated and can be used as strong evidence of the occurrence of the flutter.
In step 5, since the value of the kurtosis index in mathematical statistics is used for representing the degree of deviation of data from the standard normal distribution, and the kurtosis index is exactly normal distribution when the kurtosis index is 3, the wavelet energy kurtosis index K introduced by the inventionEIs a threshold value with 3 as the parameter; wavelet packet energy kurtosis index KEThe introduction of the method can quickly respond to sudden change of flutter frequency band energy, namely can represent the nature of flutter, and provide early warning for the beginning of early weak flutter signal inoculation.
In step 6, the threshold size of the target flutter frequency band energy ratio is set empirically through relevant literature data and test data of the invention, and when the target flutter frequency band energy ratio R is greater than 60%, the frequency band energy is considered to be dominant in the whole frequency domain range, the energy frequency band is transferred, the energy is concentrated, and the target flutter frequency band energy ratio can be used as strong evidence for the occurrence of flutter.
The monitoring process adopted by the invention has the advantages that the calculation method is simple, convenient, quick and effective; the method not only fully reflects the essence and the characteristics of cutting flutter inoculation, but also fully considers the simplicity and the practicability of signal acquisition and data processing, and realizes the forecast of early flutter and the identification of flutter state.
As shown in fig. 3 to 6, in the present embodiment, the turning bar stock test chatter monitoring is performed, the workpiece supporting mode is that one end is fixed, and the other end is free, as shown in fig. 3, the first-order natural frequency of the workpiece obtained through the hammering test is 928.1Hz, and the workpiece cutter system undergoes transition from a stable cutting state to a non-stable cutting state. Fig. 4 is a time-frequency spectrum grayscale chart obtained by cwt (continuous wave transform) transformation of a sound pressure acquisition signal of a workpiece, in which the brightness of a region near a 928Hz frequency component gradually becomes brighter, which illustrates a process requiring gradual inoculation from the generation of chatter vibration to the final explosion; in this embodiment, the value of m is 3, that is, the signal is subjected to 3-layer wavelet packet decomposition, the mother wavelet is 'dB 5', and the first-order natural frequency 928.1Hz of the workpiece cutter system just falls in the frequency band (750Hz-1250Hz) corresponding to the node 3 in fig. 5; as shown in FIG. 5, the observation of the distribution trend of the energy of the wavelet packet of the target frequency band corresponding to the flutter inoculation process of the signals 6-10s shows that the energy ratio of the frequency band signals corresponding to the node 3 is obviously increased.
Data verification is carried out on the collected sound pressure signals through the steps of the method, the sampling frequency fs is 6000Hz, the spindle rotating speed W is 1200rpm, the feed amount f is 0.15mm/r, the cutting depth d is 0.08mm, the overlapping coefficient eta is 50%, and the number n of data packet points is 600; the kurtosis index is mathematical statistics knowledge, the kurtosis index represents the degree of data deviating from standard normal distribution, the threshold of the energy kurtosis index is set to be 3, and the threshold of the energy ratio is set to be 60%; calculating an energy kurtosis index KEDrawing a curve shown in figure 6 together with the energy ratio R, comparing the change trend of the sound pressure signal time domain waveform in the time range of 0-10 s, and the ratio of the curve R fluctuates sharply in the range of 0-50%; during this period, it is difficult to determine the turning state by the time-domain amplitude and energy ratio, which is the opposite of the energy kurtosis index KEThe curve of (A) shows a steep large peak at 4.2s, and then the magnitude is maintained at about 2.8, which shows that the energy of the frequency band continuously shows a slight impact, and the energy kurtosis index K is over 10.2sEIf the energy is larger than the threshold value 3, the larger impact component appears in the target frequency band energy, and the impact component is regarded as the response of the beginning of flutter inoculation; after 11s the energy ratio continues to increase and is above the threshold 60%, indicating the occurrence of chatter. FIG. 7 is a data verification plot of a turned bar, with the abscissa being the workpiece position and the ordinate being the acoustic pressure signal amplitude and the characteristic parameter amplitude; the cutting depth is changed to 1mm, the other parameters are unchanged, and a characteristic parameter curve graph shows that when the energy kurtosis index at a scale of 92mm is greater than a threshold value 3, a flutter inoculation characteristic early warning is sent out, the graph shows that the flutter starts to inoculate at the position, and the surface state of a processed workpiece at the position of 92mm has no obvious vibration lines; then the energy ratio R of the workpiece at the position of 102mm is more than 60%, a vibration alarm is sent out, and obvious vibration lines appear between 102mm and 130mm on the machined workpiece, namely the workpiece is atAnd (4) machining under the condition of generating vibration, so that vibration lines appear on the surface of the workpiece. The experimental result proves the effectiveness of the method, and compared with other methods, the method can realize accurate discovery and quick response of the early weak flutter signal characteristics earlier, and the embodiment verifies the effectiveness of the method.
The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may occur to those skilled in the art without departing from the principle of the invention, and are considered to be within the scope of the invention.
Claims (3)
1. An early turning flutter early warning and monitoring method based on a sound pressure energy kurtosis index is characterized in that: the method comprises the steps of adopting a sound pressure signal sensor to collect sound pressure signals in the cutting process in real time, taking each rotation of a workpiece as a data processing interval, transmitting the sound pressure signals to a computer for storage, carrying out wavelet packet decomposition on the sound pressure signals, extracting wavelet packet coefficients of a target flutter frequency band, calculating wavelet packet energy of the target flutter frequency band, and calculating an energy kurtosis index K of the target flutter frequency bandEAnd comparing the energy ratio R with a set threshold value respectively when the energy kurtosis index K is reachedESending out flutter inoculation characteristic early warning when the threshold value is larger than a set threshold value; when the energy ratio R is larger than a set threshold value, a flutter alarm is given, and the method specifically comprises the following steps:
step 1, before turning, carrying out an effective hammering test on a flexible workpiece spindle system to obtain a first-order natural frequency of the workpiece spindle system, and taking a frequency band where the first-order natural frequency of the workpiece is as a target flutter frequency band;
step 2, a sound pressure signal sensor is fixed beside a machine tool and close to a center position, the sound pressure signal sensor is used for collecting a sound pressure signal in the cutting process in real time, and a data processing packet of the sound pressure signal is recorded as Z (i) ═ x (1), x (2), x (3) … x (n)), wherein Z (i) represents an ith collected sound pressure signal data packet; by applying a data overlapping processing technology, a data processing packet of n sampling points is obtained by taking each circle of rotation of a workpiece as an interval, wherein n represents the number of data points acquired by taking the main shaft to rotate for one circle, and the expression is as follows:
fs is the sampling frequency in units: hz; Ω is the spindle speed, unit: rpm; eta is a data overlapping coefficient;
step 3, decomposing the acquired sound pressure signals according to a wavelet packet decomposition principle, wherein the bandwidth range of a decomposition frequency band is 1/3-2/3 of the first-order inherent frequency of the workpiece, and performing wavelet packet m-layer decomposition on the data processing packet; m is a value satisfying
Where f is the Nyquist frequency, ωnIs the first order natural frequency of the workpiece spindle system; calculating wavelet packet energy E of target flutter frequency band signalm，iThe calculation formula is as follows:
step 4, calculating wavelet packet energy kurtosis index K of target flutter frequency band signal corresponding to each data processing packetEAnd a wavelet packet energy ratio R;
step 5, the wavelet packet energy kurtosis index K of the target frequency bandEComparing with its threshold value, when the energy kurtosis index KEWhen the temperature is greater than the set threshold value, sending out flutter inoculation characteristic early warning, considering that flutter inoculation starts, and entering the step 6; otherwise, considering that the flutter does not start the inoculation, and returning to the step 2;
step 6, when the wavelet packet energy ratio R of the target frequency band is greater than a set threshold value, a flutter alarm is sent out; otherwise, it is assumed that no chatter has occurred.
2. The early turning chatter warning and monitoring method based on the sound pressure energy kurtosis index as claimed in claim 1, wherein: the energy ratio R is calculated using the following formula:
where E is the sum of the energies of all frequency bands.
3. The early turning chatter early warning and monitoring method based on the sound pressure energy kurtosis index as claimed in claim 1 or 2, wherein: in the step 4, the energy kurtosis index K of the wavelet packetEExpressed as:
where N is the number of signal packet samples, Em，iRepresents the energy of the wavelet packet of the mth layer, the ith band,
for sampling the expected energy value of the target frequency band of the signal, ErmsIs the energy root mean square value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010634862.5A CN111890126B (en) | 2020-07-03 | 2020-07-03 | Early turning flutter early warning and monitoring method based on sound pressure energy kurtosis index |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010634862.5A CN111890126B (en) | 2020-07-03 | 2020-07-03 | Early turning flutter early warning and monitoring method based on sound pressure energy kurtosis index |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111890126A true CN111890126A (en) | 2020-11-06 |
| CN111890126B CN111890126B (en) | 2022-03-11 |
Family
ID=73191486
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010634862.5A Active CN111890126B (en) | 2020-07-03 | 2020-07-03 | Early turning flutter early warning and monitoring method based on sound pressure energy kurtosis index |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111890126B (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112405072A (en) * | 2020-11-11 | 2021-02-26 | 上海交通大学 | On-line monitoring method and device for cutting chatter of machine tool |
| CN113705421A (en) * | 2021-08-24 | 2021-11-26 | 西安交通大学 | Method and system for online monitoring of vibration marks on surface of grinding workpiece |
| CN114055251A (en) * | 2021-12-17 | 2022-02-18 | 沈阳科网通信息技术有限公司 | Deep decomposition-based electric spindle system early fault detection method |
| CN115293219A (en) * | 2022-09-29 | 2022-11-04 | 中国电建集团华东勘测设计研究院有限公司 | Wavelet and kurtosis fused pulse signal denoising method |
| CN115555920A (en) * | 2022-10-12 | 2023-01-03 | 山东大学 | Online flutter detection method and system based on adaptive variational modal decomposition |
| CN115555920B (en) * | 2022-10-12 | 2024-05-10 | 山东大学 | Online chatter detection method and system based on adaptive variation modal decomposition |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101330923B1 (en) * | 2012-09-28 | 2013-11-18 | 인하대학교 산학협력단 | Method for sound quality analysis of vehicle noise using gammatone filter and apparatus thereof |
| CN106289774A (en) * | 2016-07-26 | 2017-01-04 | 北京工业大学 | A kind of rolling bearing fault identification and trend forecasting method |
| CN106363463A (en) * | 2016-08-15 | 2017-02-01 | 大连理工大学 | Milling flutter on-line monitoring method based on energy occupation ratio |
| CN106564012A (en) * | 2016-11-01 | 2017-04-19 | 苏州微著设备诊断技术有限公司 | Detection method of grinding processing chattering |
| CN106959432A (en) * | 2017-03-23 | 2017-07-18 | 中国石油化工股份有限公司胜利油田分公司海洋采油厂 | A kind of offshore work platform personnel positioning method based on wavelet decomposition low frequency coefficient |
| CN108152037A (en) * | 2017-11-09 | 2018-06-12 | 同济大学 | Method for Bearing Fault Diagnosis based on ITD and improvement shape filtering |
| CN108710889A (en) * | 2018-04-02 | 2018-10-26 | 天津大学 | A kind of scarce cylinder method for diagnosing faults of automobile engine |
-
2020
- 2020-07-03 CN CN202010634862.5A patent/CN111890126B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101330923B1 (en) * | 2012-09-28 | 2013-11-18 | 인하대학교 산학협력단 | Method for sound quality analysis of vehicle noise using gammatone filter and apparatus thereof |
| CN106289774A (en) * | 2016-07-26 | 2017-01-04 | 北京工业大学 | A kind of rolling bearing fault identification and trend forecasting method |
| CN106363463A (en) * | 2016-08-15 | 2017-02-01 | 大连理工大学 | Milling flutter on-line monitoring method based on energy occupation ratio |
| CN106564012A (en) * | 2016-11-01 | 2017-04-19 | 苏州微著设备诊断技术有限公司 | Detection method of grinding processing chattering |
| CN106959432A (en) * | 2017-03-23 | 2017-07-18 | 中国石油化工股份有限公司胜利油田分公司海洋采油厂 | A kind of offshore work platform personnel positioning method based on wavelet decomposition low frequency coefficient |
| CN108152037A (en) * | 2017-11-09 | 2018-06-12 | 同济大学 | Method for Bearing Fault Diagnosis based on ITD and improvement shape filtering |
| CN108710889A (en) * | 2018-04-02 | 2018-10-26 | 天津大学 | A kind of scarce cylinder method for diagnosing faults of automobile engine |
Non-Patent Citations (2)
| Title |
|---|
| 任学平: "小波包和峭度在轴承早期故障分析中的应用", 《特钢技术》 * |
| 薛光辉: "综放垮落煤岩声压信号小波包频带能量特征提取", 《辽宁工程技术大学学报（自然科学版）》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112405072A (en) * | 2020-11-11 | 2021-02-26 | 上海交通大学 | On-line monitoring method and device for cutting chatter of machine tool |
| CN113705421A (en) * | 2021-08-24 | 2021-11-26 | 西安交通大学 | Method and system for online monitoring of vibration marks on surface of grinding workpiece |
| CN114055251A (en) * | 2021-12-17 | 2022-02-18 | 沈阳科网通信息技术有限公司 | Deep decomposition-based electric spindle system early fault detection method |
| CN115293219A (en) * | 2022-09-29 | 2022-11-04 | 中国电建集团华东勘测设计研究院有限公司 | Wavelet and kurtosis fused pulse signal denoising method |
| CN115555920A (en) * | 2022-10-12 | 2023-01-03 | 山东大学 | Online flutter detection method and system based on adaptive variational modal decomposition |
| CN115555920B (en) * | 2022-10-12 | 2024-05-10 | 山东大学 | Online chatter detection method and system based on adaptive variation modal decomposition |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111890126B (en) | 2022-03-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111890126B (en) | Early turning flutter early warning and monitoring method based on sound pressure energy kurtosis index | |
| Yoon et al. | On the use of a single piezoelectric strain sensor for wind turbine planetary gearbox fault diagnosis | |
| CN110160767B (en) | Impact period automatic identification and extraction method and system based on envelope analysis | |
| CN105518295A (en) | Status monitoring device for wind power generation device | |
| Gao et al. | Chatter detection and stability region acquisition in thin-walled workpiece milling based on CMWT | |
| Elforjani et al. | Analysis of extremely modulated faulty wind turbine data using spectral kurtosis and signal intensity estimator | |
| CN108007681A (en) | A kind of method that mechanical fault detection is carried out using microphone array | |
| Wang et al. | Size estimation for naturally occurring bearing faults using synchronous averaging of vibration signals | |
| CN104596766A (en) | Early fault determining method for bearing | |
| Mishra et al. | Stability analysis in milling process using spline based local mean decomposition (SBLMD) technique and statistical indicators | |
| CN111256993A (en) | Method and system for diagnosing fault type of main bearing of wind turbine generator | |
| CN108956142A (en) | A kind of bearing fault recognition methods | |
| EP4060436A1 (en) | Determination apparatus, machining system, determination method, and carrier means | |
| CN103394972B (en) | Milling Process surface roughness on-line prediction method based on acoustic emission signal | |
| Jain et al. | A review on vibration signal analysis techniques used for detection of rolling element bearing defects | |
| CN111975451B (en) | Milling flutter online monitoring method based on nonlinear adaptive decomposition and Shannon entropy | |
| CN113237619B (en) | Fault early warning method, device, equipment and storage medium for variable-speed rotating machinery vibration | |
| Li et al. | Application of a Method of Identifiying Instantaneous Shaft Speed from Spectrum in Aeroengine Vibration Analysis | |
| Jiang et al. | A tacholess order tracking method based on spectral amplitude modulation for variable speed bearing fault diagnosis | |
| Tang et al. | A Comparative Experimental Study of Vibration and Acoustic Emission on Fault Diagnosis of Low-speed Bearing | |
| Li et al. | On-line fault detection in wind turbine transmission system using adaptive filter and robust statistical features | |
| Miljković | Brief review of vibration based machine condition monitoring | |
| CN110633686A (en) | Equipment rotating speed identification method based on vibration signal data driving | |
| JP2022145537A (en) | Determination device, determination method, program, and processing system | |
| de Aguiar et al. | Acoustic emission applied to detect workpiece burn during grinding |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20231114 Address after: No.7, Dachang South Road, Tanghuai Park, Taiyuan Comprehensive Reform Demonstration Zone, Taiyuan, Shanxi 030032 Patentee after: Shanxi Weida Transmission Technology Co.,Ltd. Address before: 030024 No. 79 West Main Street, Taiyuan, Shanxi, Yingze Patentee before: Taiyuan University of Technology |