CN111337767A - Resonant wave reducer fault analysis method - Google Patents
Resonant wave reducer fault analysis method Download PDFInfo
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Abstract
The application provides a resonant wave reducer fault analysis method, which comprises the following steps: acquiring an actual output signal y (n) of the motor current; carrying out data noise reduction on the current actual output signal y (n) according to an MCKD algorithm, and restoring to obtain a fault characteristic signal r (n); performing signal processing on the fault characteristic signal r (n) according to a CEEMD algorithm to obtain a sensitive IMF component signal; carrying out characteristic value calculation on the sensitive IMF component signals; and obtaining a fault diagnosis result according to a data set consisting of all characteristic values calculated according to the continuously acquired current signals. The beneficial effect of this application is: the method has the advantages that the collected current signals are subjected to noise reduction by collecting the current signals of the motor through an MCKD method, CEEMD decomposition processing is then carried out, feature quantity calculation is carried out on decomposed sensitive IMF components, further fault diagnosis is carried out on the speed reducer, economic cost of fault analysis of the resonant wave speed reducer is saved, and monitoring and diagnosis reliability is improved.
Description
Technical Field
The disclosure relates to the technical field of resonant wave reducer fault analysis, in particular to a resonant wave reducer fault analysis method.
Background
The harmonic reducer is different from a common reducer, has the advantages of large transmission speed ratio, high bearing capacity, high transmission precision, high transmission efficiency, convenience in installation and the like, and is widely applied to the fields of aerospace, precise medical instruments, industrial robots and the like.
The reliability of the harmonic reducer, which is a main part in the industrial robot, is a main factor for determining the mean time between failures of the industrial robot. At present, the reliability and the service life of the domestic harmonic reducer are far from the import harmonic reducer, and the ever-increasing market demand of industrial robots cannot be met.
Statistically, 40% -70% of electric drive system and motor failures are due to retarder failure, and such equipment failures tend to increase application costs. In industrial production, retarder failure is usually due to progressive evolution of fatigue damage or damage caused by solid particles. Therefore, in consideration of economic safety concerns and the like, in order to ensure that a fault is discovered in a timely manner before the reduction gear fails, it is necessary to monitor the state of the reduction gear.
Traditionally, the operating state of a harmonic reducer is often monitored by measuring its vibration signal. Therefore, some scholars have proposed techniques for vibration research, such as cepstral analysis, discrete wavelet transform, cyclostationary analysis, and the like. However, the limitations of the vibration measurement technique are mainly:
1. due to the limited space of the equipment and the high-temperature environment in the running process of the equipment, a vibration sensor is difficult to place at the position of the tested speed reducer;
2. various excitation signals exist in an industrial environment, so that the problems of sensitivity when a sensor is installed and the source of a vibration signal are considered;
3. the vibration measurement system has the problems of high price, technical monopoly and the like, and can be only applied in important occasions to ensure the economy and reasonableness.
Therefore, other methods for monitoring the resonant wave retarder are needed to be solved.
Disclosure of Invention
The application aims to solve the problems and provides a resonant wave speed reducer fault analysis method.
In a first aspect, the present application provides a harmonic reducer fault analysis method, including the following steps: acquiring an actual output signal y (n) of the motor current; carrying out data noise reduction on the current actual output signal y (n) according to an MCKD algorithm, and restoring to obtain a fault characteristic signal r (n); performing signal processing on the fault characteristic signal r (n) according to a CEEMD algorithm to obtain a sensitive IMF component signal; carrying out characteristic value calculation on the sensitive IMF component signals; and obtaining a fault diagnosis result according to a data set consisting of all characteristic values calculated according to the continuously acquired current signals.
According to the technical scheme provided by the embodiment of the application, the actual current output signal y (n) is subjected to data noise reduction according to an MCKD algorithm, and a fault characteristic signal r (n) is obtained by reduction, and the method specifically comprises the following steps: finding the optimal FIR filter f (n) through the MCKD algorithm; and restoring the collected current actual output signal y (n) to a fault characteristic signal r (n), wherein r (n) is y (n) f (n).
According to the technical scheme provided by the embodiment of the application, the signal processing is performed on the fault characteristic signal r (n) according to a CEEMD algorithm to obtain a sensitive IMF component signal, and the method specifically comprises the following steps: adding k sets of positive and negative paired white noises in the fault characteristic signal r (n) to obtain a pair of aggregate signals,
in the above formula: i is 1,2, …, k, ni(t) white noise added i-th time, PiIs r (t) the signal obtained by adding white noise to the ith time, NiSubtracting white noise from the ith x (t); decomposing the two signals in the formula by EMD to obtain two groups of IMF component signals,
in the above formula: m represents the number of IMF component signals obtained after EMD decomposition of the signals,
represents PiDecomposing the obtained j-th IMF component signal,
represents NiObtained by decompositionThe jth IMF component signal; repeating the above process M times, and then combining and averaging each IMF component signal:
in the above formula: c. Cj(t) represents the jth IMF component signal obtained by the CEEMD processing algorithm.
According to the technical scheme provided by the embodiment of the application, the sensitive IMF component signal is selected as c1(t)。
According to the technical scheme provided by the embodiment of the application, the characteristic value calculation of the sensitive IMF component signal specifically comprises the following steps: and calculating the characteristic value of the sensitive IMF component signal by using an RMS (root mean square) method.
According to the technical scheme provided by the embodiment of the application, the finding of the optimal FIR filter f (n) through the MCKD algorithm specifically includes the following steps: setting parameters: a pulse period T, a filter length L, a time-shifting period number M and a data point number N; calculating the actual output signal y (n) of the current
And YmTWherein M is 1,2, …, M,
obtaining a filtered fault characteristic signal r (n); calculating A from r (n)mAnd B, wherein
Updating the result f of the filter, where f ═ f1,f2,…,fL]TTo search for the optimal filter, order
The matrix expression form of the filter obtained by sorting is
When the difference value between the current actual output signal y (n) before filtering and the correlation kurtosis of the fault characteristic signal r (n) after filtering is maximum is less than a set thresholdWhen the value is positive, stopping iteration, otherwise, continuously obtaining a fault characteristic signal r (n) extracted after filtering; wherein the relative kurtosis is expressed as:
x (n) is a signal sequence.
The invention has the beneficial effects that: the current signal of the motor is collected to replace the traditional vibration measurement technology, the collected current signal is subjected to noise reduction by using a Maximum Correlation Kurtosis Deconvolution (MCKD) method, so that the fault impact component in the current signal can be highlighted, then complementary set empirical mode decomposition (CEEMD) decomposition processing is performed, the characteristic quantity calculation is performed on the decomposed sensitive IMF component, and then fault diagnosis is performed on the speed reducer, the economic cost of fault analysis of the resonant wave speed reducer is saved, and the reliability of monitoring and diagnosis is improved.
Drawings
FIG. 1 is a flow chart of a first embodiment of the present application;
FIG. 2 is a comparison graph of current signals before and after the current signals are denoised by the MCKD algorithm;
fig. 3 is a flowchart of S200 in fig. 1.
FIG. 4 is a flowchart of S210 in FIG. 3;
FIG. 5 is a flowchart of S300 in FIG. 1;
fig. 6 is a curve variation diagram of different characteristic quantities varying with the fault condition after data processing is performed on a plurality of groups of current signals of the speed reducer.
Detailed Description
In order that those skilled in the art will better understand the technical solutions of the present invention, the following detailed description of the present invention is provided in conjunction with the accompanying drawings, and the description of the present section is only exemplary and explanatory, and should not be construed as limiting the scope of the present invention in any way.
Fig. 1 shows a flow chart of a first embodiment of the present application, which includes the following steps:
s100, acquiring an actual output signal y (n) of the motor current.
The actual output signal of the current when the reducer is locally damaged is assumed to be represented as y (n) ═ h (n) × r (n) + e (n), where: y (n) is a current actual output signal; r (n) is a failure cycle impact component; h (n) is the response of the system transmission path; e (n) is a noise component.
S200, carrying out data noise reduction on the current actual output signal y (n) according to the MCKD algorithm, and restoring to obtain a fault characteristic signal r (n).
The MCKD algorithm is a maximum correlation kurtosis deconvolution algorithm, and aims to find an optimal FIR filter f (n), extract a fault characteristic signal r (n) from an actual output signal y (n), and maximize the correlation kurtosis of the actual output signal y (n), so as to extract periodic impact components in the signal and remove noise interference.
As shown in fig. 2, 5000 raw current data points were chosen for observing the before and after contrast map for noise reduction according to the MCKD algorithm.
In this embodiment, as shown in fig. 3, S200 specifically includes the following steps:
s210, finding the optimal FIR filter f (n) through the MCKD algorithm.
In this embodiment, the system defaults to f (1) as the initial FIR filter, and then performs the iterative process of the optimal FIR filter.
As shown in fig. 4, the present step specifically includes the following steps:
s211, setting parameters: pulse period T, filter length L, time shift period number M and data point number N.
S212, calculating the actual output signal y (n) of the current
And YmTWherein M is 1,2, …, M,
s213, the filtered fault characteristic signal r (n) is obtained.
In this embodiment, the initial fault characteristic signal r (1) is y (1) f (1).
S214, calculating A according to r (n)mAnd B, wherein
S215, updating the result f of the filter, where f ═ f1,f2,…,fL]T. To search for the optimal filter, order
The matrix expression form of the filter obtained by sorting is
S216, difference value delta CK of correlation kurtosis of current actual output signal y (n) and filtered fault characteristic signal r (n)MAnd (T) stopping iteration when the value is smaller than the set threshold value, otherwise, repeating the steps S213-S215 to continuously obtain the fault characteristic signal r (n) extracted after filtering.
Wherein the relative kurtosis is expressed as:
x (n) is the signal sequence, and the purpose of the MCKD algorithm is to find an optimal filter to maximize the correlation kurtosis of the extracted fault characteristic signal r (n), i.e. to extract the fault characteristic signal r (n)
The correlation kurtosis has the characteristic of larger impact and larger kurtosis value, and meanwhile, the characteristics of a correlation function are also saved, and the signals in a specific period can be extracted.
S220, restoring the collected current actual output signal y (n) to the fault characteristic signal r (n), r (n) ═ y (n) × f (n).
S300, carrying out signal processing on the fault characteristic signal r (n) according to a CEEMD algorithm to obtain a sensitive IMF component signal.
As shown in fig. 5, the present step specifically includes the following steps:
s310, adding k groups of positive and negative paired white noises in the fault characteristic signal r (n) to obtain a pair of aggregate signals,
in the above formula: i is 1,2, …, k, ni(t) white noise added i-th time, PiIs r (t) the signal obtained by adding white noise to the ith time, NiIs x (t) the signal obtained by subtracting white noise the ith time.
S320, decomposing the two signals in the formula (1) by using EMD to obtain two groups of IMF component signals,
in the above formula: m represents the number of IMF component signals obtained after EMD decomposition of the signals,
represents PiDecomposing the obtained j-th IMF component signal,
represents NiAnd decomposing the obtained j-th IMF component signal.
S330, repeating the process for M times, and then combining and averaging each IMF component signal
cj(t)，
In the above formula: c. Cj(t) represents the jth IMF component signal obtained by the CEEMD processing algorithm.
In this embodiment, at an early failure stage of the resonant wave reducer, the impact characteristics of the acquired current signals are weak and the background noise interference is severe, so that the current signals are subjected to noise reduction processing by the MCKD algorithm first, the failure impact components in the current signals are retained, and then the signals are decomposed into a plurality of IMF components by being processed by the CEEMD algorithm. When the speed reducer is damaged, the damage is often causedHigh-frequency noise is generated, wherein the IMF1 component contains more high-frequency components, so that the fault characteristics of the speed reducer mostly exist in the IMF1, and therefore, in the embodiment, the sensitive IMF component signal is selected from the IMF1, that is, the average value of the IMF1 component signal is c1(t)。
And S400, calculating characteristic values of the sensitive IMF component signals.
In this step, RMS characteristic quantities are used to characterize the fault degree of the sensitive IMF component signals.
In order to accurately determine the degree of failure of the speed reducer, it is necessary to perform feature quantity calculation on the IMF1 component containing much failure information. Firstly, the fault degree of the speed reducer is classified according to the damage size of the speed reducer, and the classification result is shown in table 1:
TABLE 1
| Size of | Categories | |
| 0～0.2mm | Healthy | |
| 0.2～2mm | Level-1 | |
| 2～4.5mm | Level-2 | |
| >4.5mm | Level-3 |
Then, selecting characteristic quantities suitable for representing the damage degree of the speed reducer, wherein the characteristic quantities widely referred in the industry today comprise: mean (RMS), Shannon Entropy (SE), peak Coefficient (CF), etc.
In this embodiment, in order to verify the result, the following test is performed, and a current signal of a harmonic reducer of a certain model is selected as a reference, and is processed by successively adopting the MCKD algorithm and the CEEMD algorithm. Selected harmonic reducers of different failure degrees are shown in table 2:
TABLE 2
| Numbering | Degree of damage |
| K004 | Healthy |
| KA05 | Level-1 |
| KA06 | Level-2 |
| KB24 | Level-3 |
The operating conditions of the resonant wave retarder are shown in table 3:
TABLE 3
As shown in fig. 6, a plurality of groups of speed reducers under different fault categories are selected for experiment to acquire current data, IMF1 component signals are selected from the current signals subjected to noise reduction processing by the MCKD algorithm and processing by the CEEMD algorithm for calculation of the mean value (RMS), the Shannon Entropy (SE) and the Crest Factor (CF) of the three characteristic quantities, so as to facilitate the calculationIn the result comparison, the calculation results of each set of feature quantities are normalized:
as shown in fig. 6, it can be clearly observed that under four operating conditions, namely a, b, c, and d, the RMS characteristic value curve monotonically increases with the damage degree of the resonant wave reducer under the four operating conditions, and the other two characteristic quantities (SE and CF) have no obvious change rule, so in this embodiment, the RMS value of the IMF1 component after the current signal is processed by the MCKD and CEEMD methods is used as the indicator for fault diagnosis.
In order to ensure the accuracy and rationality of fault diagnosis, it is necessary to analyze a large number of current signals of the resonant wave reducer to obtain a diagnostic index value adapted to the resonant wave reducer. The RMS characteristic quantity shows a monotonous increasing or decreasing trend along with the increase of the fault degree, so that the RMS characteristic value is finally selected as a reference standard, the use condition of the speed reducer is conveniently judged, and the fault is timely eliminated so as to avoid economic loss.
And S500, obtaining a fault diagnosis result according to a data set consisting of all characteristic values calculated according to the continuously acquired current signals.
In order to judge the running state of the speed reducer, current signals need to be continuously acquired to obtain RMS characteristic values for continuous judgment, and the running state of the speed reducer is judged according to monotonicity of an RMS characteristic value curve.
The principles and embodiments of the present application are explained herein using specific examples, which are presented only to assist in understanding the method and its core concepts of the present application. The foregoing is only a preferred embodiment of the present application, and it should be noted that there are objectively infinite specific structures due to the limited character expressions, and it will be apparent to those skilled in the art that a plurality of modifications, decorations or changes may be made without departing from the principle of the present application, and the technical features described above may be combined in a suitable manner; such modifications, variations, combinations, or adaptations of the invention using its spirit and scope, as defined by the claims, may be directed to other uses and embodiments, or may be learned by practice of the invention.
Claims (6)
1. A resonant wave reducer fault analysis method is characterized by comprising the following steps:
acquiring an actual output signal y (n) of the motor current;
carrying out data noise reduction on the current actual output signal y (n) according to an MCKD algorithm, and restoring to obtain a fault characteristic signal r (n);
performing signal processing on the fault characteristic signal r (n) according to a CEEMD algorithm to obtain a sensitive IMF component signal;
carrying out characteristic value calculation on the sensitive IMF component signals;
and obtaining a fault diagnosis result according to a data set consisting of all characteristic values calculated according to the continuously acquired current signals.
2. The method for analyzing the fault of the resonant wave speed reducer according to claim 1, wherein the actual current output signal y (n) is subjected to data noise reduction according to an MCKD algorithm, and a fault characteristic signal r (n) is obtained through reduction, and the method specifically comprises the following steps:
finding the optimal FIR filter f (n) through the MCKD algorithm;
and restoring the collected current actual output signal y (n) to a fault characteristic signal r (n), wherein r (n) is y (n) f (n).
3. The harmonic reducer fault analysis method according to claim 1, wherein the fault characteristic signal r (n) is subjected to signal processing according to a CEEMD algorithm to obtain a sensitive IMF component signal, and the method specifically comprises the following steps:
adding k sets of positive and negative paired white noises in the fault characteristic signal r (n) to obtain a pair of aggregate signals,
in the above formula: i is 1,2, …, k, ni(t) white noise added i-th time, PiIs r (t) the signal obtained by adding white noise to the ith time, NiSubtracting white noise from the ith x (t);
decomposing the two signals in the formula (1) by EMD to obtain two groups of IMF component signals,
in the above formula: m represents the number of IMF component signals obtained after EMD decomposition of the signals,
represents PiDecomposing the obtained j-th IMF component signal,
represents NiDecomposing the obtained jth IMF component signal;
repeating the above process M times, and then combining and averaging each IMF component signal:
in the above formula: c. Cj(t) represents the jth IMF component signal obtained by the CEEMD processing algorithm.
4. The harmonic reducer fault analysis method of claim 3, wherein the sensitive IMF component signal is selected as c1(t)。
5. The harmonic reducer fault analysis method according to claim 1, wherein the characteristic value calculation of the sensitive IMF component signal specifically includes: and calculating the characteristic value of the sensitive IMF component signal by using an RMS (root mean square) method.
6. The resonant wave retarder fault analysis method of claim 2, wherein the finding of the optimal FIR filter f (n) by the MCKD algorithm comprises the steps of:
setting parameters: a pulse period T, a filter length L, a time-shifting period number M and a data point number N;
calculating the actual output signal y (n) of the current
And YmTWherein M is 1,2, …, M,
obtaining a filtered fault characteristic signal r (n);
calculating A from r (n)mAnd B, wherein
The result f of the filter is updated and,
f＝[f1,f2,…,fL]T(4)
to search for the optimal filter, order
The matrix expression form of the filter obtained by sorting is
The difference value DeltaCK of the correlation kurtosis of the current actual output signal y (n) before filtering and the fault characteristic signal r (n) after filteringM(T) stopping iteration when the value is smaller than a set threshold value, otherwise, continuously obtaining a filtered fault characteristic signal r (n); wherein the relative kurtosis is expressed as:
x (n) is a signal sequence.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112904200A (en) * | 2021-02-08 | 2021-06-04 | 河北工业大学 | Signal acquisition device based on motor current diagnosis harmonic speed reducer ware trouble |
| CN113878613A (en) * | 2021-09-10 | 2022-01-04 | 哈尔滨工业大学 | Industrial robot harmonic reducer early fault detection method based on WLCTD and OMA-VMD |
| CN114487826A (en) * | 2022-02-14 | 2022-05-13 | 爱科赛智能科技(浙江)有限公司 | Motor starting locked rotor detection method based on current kurtosis |
| CN115758094A (en) * | 2023-01-09 | 2023-03-07 | 河北工业大学 | Method for extracting degradation index of speed reducer and predicting service life of speed reducer |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105547627A (en) * | 2016-01-28 | 2016-05-04 | 南京信息工程大学 | Rotating machinery feature extraction method on the basis of WPT-CEEMD |
| CN106383028A (en) * | 2016-09-05 | 2017-02-08 | 北京航空航天大学 | Gear case fault diagnosis method |
| CN107329044A (en) * | 2017-06-30 | 2017-11-07 | 国网江苏省电力公司徐州供电公司 | A kind of wire selection method for power distribution network single phase earthing failure based on electric arc transient state component |
| CN207472565U (en) * | 2017-11-17 | 2018-06-08 | 中国计量大学 | A kind of industrial robot decelerator trouble prognostic experiment system |
| CN108303250A (en) * | 2017-01-12 | 2018-07-20 | 天津工业大学 | A method of passing through electric signal residual vector signal identification gearbox fault |
| CN109029977A (en) * | 2018-07-12 | 2018-12-18 | 福州大学 | A kind of epicyclic gearbox Incipient Fault Diagnosis method based on VMD-AMCKD |
| CN109405889A (en) * | 2018-12-13 | 2019-03-01 | 中国航空工业集团公司上海航空测控技术研究所 | System and method for for the prediction of development machine working arm decelerator trouble |
| CN109596349A (en) * | 2018-12-06 | 2019-04-09 | 桂林电子科技大学 | A kind of decelerator trouble diagnostic method based on VMD and PCT |
| CN109612732A (en) * | 2019-01-24 | 2019-04-12 | 北华大学 | A kind of Fault Diagnosis of Roller Bearings based on deconvolution and envelope spectrum |
| CN109632311A (en) * | 2019-01-21 | 2019-04-16 | 北京化工大学 | A kind of adaptive acoustical signal Method for Bearing Fault Diagnosis |
| CN109975656A (en) * | 2019-03-06 | 2019-07-05 | 河南理工大学 | Based on flexible direct current power distribution network high resistance earthing fault detection method |
| CN110139733A (en) * | 2016-12-27 | 2019-08-16 | 川崎重工业株式会社 | Trouble-shooter, method for diagnosing faults and the mechanical device for having above-mentioned trouble-shooter of retarder |
| CN110208624A (en) * | 2019-06-19 | 2019-09-06 | 河南辉煌科技股份有限公司 | A kind of humping signal equipment on-line monitoring system and retarder on-line monitoring method |
-
2020
- 2020-02-25 CN CN202010114554.XA patent/CN111337767B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105547627A (en) * | 2016-01-28 | 2016-05-04 | 南京信息工程大学 | Rotating machinery feature extraction method on the basis of WPT-CEEMD |
| CN106383028A (en) * | 2016-09-05 | 2017-02-08 | 北京航空航天大学 | Gear case fault diagnosis method |
| CN110139733A (en) * | 2016-12-27 | 2019-08-16 | 川崎重工业株式会社 | Trouble-shooter, method for diagnosing faults and the mechanical device for having above-mentioned trouble-shooter of retarder |
| CN108303250A (en) * | 2017-01-12 | 2018-07-20 | 天津工业大学 | A method of passing through electric signal residual vector signal identification gearbox fault |
| CN107329044A (en) * | 2017-06-30 | 2017-11-07 | 国网江苏省电力公司徐州供电公司 | A kind of wire selection method for power distribution network single phase earthing failure based on electric arc transient state component |
| CN207472565U (en) * | 2017-11-17 | 2018-06-08 | 中国计量大学 | A kind of industrial robot decelerator trouble prognostic experiment system |
| CN109029977A (en) * | 2018-07-12 | 2018-12-18 | 福州大学 | A kind of epicyclic gearbox Incipient Fault Diagnosis method based on VMD-AMCKD |
| CN109596349A (en) * | 2018-12-06 | 2019-04-09 | 桂林电子科技大学 | A kind of decelerator trouble diagnostic method based on VMD and PCT |
| CN109405889A (en) * | 2018-12-13 | 2019-03-01 | 中国航空工业集团公司上海航空测控技术研究所 | System and method for for the prediction of development machine working arm decelerator trouble |
| CN109632311A (en) * | 2019-01-21 | 2019-04-16 | 北京化工大学 | A kind of adaptive acoustical signal Method for Bearing Fault Diagnosis |
| CN109612732A (en) * | 2019-01-24 | 2019-04-12 | 北华大学 | A kind of Fault Diagnosis of Roller Bearings based on deconvolution and envelope spectrum |
| CN109975656A (en) * | 2019-03-06 | 2019-07-05 | 河南理工大学 | Based on flexible direct current power distribution network high resistance earthing fault detection method |
| CN110208624A (en) * | 2019-06-19 | 2019-09-06 | 河南辉煌科技股份有限公司 | A kind of humping signal equipment on-line monitoring system and retarder on-line monitoring method |
Non-Patent Citations (7)
| Title |
|---|
| 任学平等: "基于CEEMD 与MCKD 的滚动轴承早期故障诊断", 《轴承》 * |
| 任学平等: "基于CEEMD 与MCKD 的滚动轴承早期故障诊断", 《轴承》, no. 8, 31 December 2018 (2018-12-31), pages 50 - 54 * |
| 潘洋洋等: "基于CEEMD与IMCKD的滚动轴承故障诊断方法", 《机电工程技术》 * |
| 潘洋洋等: "基于CEEMD与IMCKD的滚动轴承故障诊断方法", 《机电工程技术》, vol. 48, no. 10, 31 October 2019 (2019-10-31), pages 98 - 102 * |
| 王志坚: "《齿轮箱复合故障诊断方法研究》", 31 August 2017, 兵器工业出版社, pages: 45 - 61 * |
| 粟雨: "基于经验模态分解的滚动轴承故障灰色预测方法研究", 《中国优秀博硕士学位论文全文数据库(硕士)》 * |
| 粟雨: "基于经验模态分解的滚动轴承故障灰色预测方法研究", 《中国优秀博硕士学位论文全文数据库(硕士)》, no. 06, 15 June 2019 (2019-06-15), pages 029 - 76 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112904200A (en) * | 2021-02-08 | 2021-06-04 | 河北工业大学 | Signal acquisition device based on motor current diagnosis harmonic speed reducer ware trouble |
| CN113878613A (en) * | 2021-09-10 | 2022-01-04 | 哈尔滨工业大学 | Industrial robot harmonic reducer early fault detection method based on WLCTD and OMA-VMD |
| CN113878613B (en) * | 2021-09-10 | 2023-01-31 | 哈尔滨工业大学 | Industrial robot harmonic reducer early fault detection method based on WLCTD and OMA-VMD |
| CN114487826A (en) * | 2022-02-14 | 2022-05-13 | 爱科赛智能科技(浙江)有限公司 | Motor starting locked rotor detection method based on current kurtosis |
| CN115758094A (en) * | 2023-01-09 | 2023-03-07 | 河北工业大学 | Method for extracting degradation index of speed reducer and predicting service life of speed reducer |
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