CN114813117A - Fault diagnosis method and device for RV reducer - Google Patents
Fault diagnosis method and device for RV reducer Download PDFInfo
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Abstract
The invention discloses a fault diagnosis method and device for an RV reducer. The invention firstly extracts a signal of the whole cycle movement, then carries out time-frequency analysis on the whole cycle signal by continuous wavelet transform, and intercepts the signal of the stable stage in the ascending movement. By intercepting the step, the non-stationary vibration component in the analyzed signal is reduced, so that the deviation between each theoretical calculation value and each actual calculation value of the signal is reduced as much as possible, and the non-stationary signal is approximately converted into a stationary signal. Then, considering the NeighCoeff method, setting the threshold value by taking the adjacent coefficients as a whole, not only can more characteristic information be kept, but also the noise reduction effect is better than that of the wavelet threshold value. Therefore, the invention uses the NeighCoeff method to carry out filtering and noise reduction on the intercepted signal while reserving more impact information. And finally, carrying out FFT on the noise reduction signal, and carrying out fault diagnosis on the transformed frequency domain signal.
Description
Technical Field
The invention relates to a fault diagnosis method and device for an RV reducer, and belongs to the technical field of state monitoring and fault diagnosis of mechanical equipment.
Background
With the progress of modern manufacturing industry, high precision, high rotating speed and stable control gradually become the development direction of industrial robots, and the cooperation of high-precision speed reducers cannot be separated. The speed reducer is used as a core component of the joint of the industrial robot, and the health condition of the speed reducer determines whether the industrial robot can normally operate. The RV reducer (Rotate Vector reducer) is the most commonly used reducer for robots due to its small size, large transmission ratio, high efficiency, etc. Since the RV reducer is sealed inside the joint as a high-precision core component, when the reducer fails, how to quickly complete the failure diagnosis becomes an inevitable problem.
At present, the fault diagnosis research on rolling bearings, or gears, has never been interrupted. Most researches on RV reducers relate to stress analysis, dynamic modeling, testing devices and the like, and fault diagnosis is rarely related. The reason is that the working condition of the RV reducer is usually intermittent reciprocating motion and variable-speed motion, so that the fault signal of the RV reducer is a non-stationary signal, and the fault characteristics cannot be accurately identified by the traditional signal processing method.
Disclosure of Invention
The invention provides a fault diagnosis method and device for an RV reducer, which are used for realizing fault diagnosis of the RV reducer under the condition of reciprocating periodic intermittent motion.
The technical scheme of the invention is as follows: a fault diagnosis method for an RV reducer, comprising:
s1, picking up an observation signal S (n) of mechanical vibration in the reciprocating intermittent motion process of the articulated arm 1 through the acceleration sensor 2, and calculating the relevant theoretical characteristic frequency of the RV reducer; wherein n is the sampling time of the vibration signal s (n);
s2, intercepting the observation signal S (n) obtained in the step S1 to obtain a periodic motion signal S including a rising stage and a falling stage 1 (n)；
S3, obtaining the periodic motion signal S in the step S2 1 (n) performing continuous wavelet transform, and synchronously intercepting periodic motion signal s according to time-frequency diagram 1 (n) rising phase, approximately stationary vibration signal of sample length m, as preprocessing signal s 2 (n)；
S4, pre-processing signal S obtained in step S3 2 (n) filtering to obtain a filtered signal x (n);
and S5, carrying out FFT on the filtering signal x (n) obtained in the step S4, and carrying out fault diagnosis on the frequency domain signal after transformation.
The relevant theoretical characteristic frequency of the RV reducer comprises the revolution frequency f of the sun gear 1 Planet wheel frequency conversion f 2 Support plate frequency conversion f 3 Planet wheel failure frequency f s r Sun gear and planet gear engagement frequency f 1c 。
The specific steps for calculating the relevant theoretical characteristic frequency of the RV reducer are as follows:
s1.1, knowing the preset speed n of the articulated arm 1 3 By calculating the rotational speed n 3 Conversion to a rotary frequency, and the knuckle arm rotary frequency is equivalent to a supporting disc rotary frequency f 3 (ii) a According to the formula
Calculating to obtain the main shaft rotating speed n of the motor 5 1 (ii) a Wherein n is 1 The spindle speed of the motor 5; z 1 Is the number of sun gear teeth, Z 2 Number of teeth of planet gear, Z 4 The number of teeth of the pin gear;
s1.2, by following the formula
The revolution frequency f of the sun gear is obtained through calculation 1 ；
S1.3, according to the formula
And
calculating the planet wheel rotation frequency f of the RV reducer 2 And the engagement frequency f of the sun wheel and the planet wheel 1c (ii) a Wherein Z is 3 The number of teeth of the cycloid gear is shown;
s1.4, failure frequency f of planet wheel s r The calculation formula is f for the rotation frequency of the planet wheel relative to the planet carrier s r ＝f 2 -f 3 。
In the continuous wavelet transformation, Morlet wavelets are selected as mother wavelets.
The sample length m is more than or equal to 10F max (ii) a Wherein, F max Representing 2 times the highest frequency value among the relevant theoretical characteristic frequencies of the RV reducer.
Preprocessing the signal s using the NeighCoeff algorithm 2 (n) filtering.
The fault diagnosis of the transformed frequency domain signal specifically comprises: carrying out multi-tooth wear fault diagnosis on the planet gear on the frequency domain signal after FFT, and when the frequency domain spectral line and the planet gear fault frequency f s r When the absolute value of the error is more than 0.2Hz, the multi-tooth abrasion fault of the planet wheel is determined not to occur; when the error between the frequency domain spectral line and the fault frequency of the planet wheel is between-0.2 Hz and 0Hz or the value is 0, the multi-tooth wear fault of the planet wheel is determined; when the error between the frequency domain spectral line and the planet wheel fault frequency is between 0Hz and 0.2Hz, cepstrum analysis is carried out on the filtering signal x (n), and fault diagnosis is realized.
A fault diagnosis device for an RV reducer, comprising:
the signal pickup module is used for picking up an observation signal s (n) of mechanical vibration in the reciprocating intermittent motion process of the joint arm 1 through the acceleration sensor 2 and calculating the relevant theoretical characteristic frequency of the RV reducer; wherein n is the sampling time of the vibration signal s (n);
a first obtaining module for obtaining the observation signal s (n) obtained by the signal pickup moduleIntercepting the whole-cycle signal to obtain a periodic motion signal s simultaneously containing a rising phase and a falling phase 1 (n)；
An intercepting module for intercepting the periodic motion signal s obtained by the first obtaining module 1 (n) performing continuous wavelet transform, and synchronously intercepting periodic motion signal s according to time-frequency diagram 1 (n) rising phase, approximately stationary vibration signal of sample length m, as preprocessing signal s 2 (n)；
A second obtaining module for obtaining the preprocessed signal s 2 (n) filtering to obtain a filtered signal x (n);
and the fault diagnosis module is used for performing FFT on the filtering signal x (n) obtained by the third obtaining module and performing fault diagnosis on the transformed frequency domain signal.
A processor for executing a program, wherein the program when executed performs any of the above-described fault diagnosis methods for a RV reducer.
A computer-readable storage medium including a stored program, wherein when the program runs, the apparatus where the computer-readable storage medium is located is controlled to execute the fault diagnosis method for the RV reducer.
The invention has the beneficial effects that: the method for intercepting the steady-phase signals through time-frequency analysis provided by the invention has the advantages that the error between the actual calculation result and the theoretical calculation value is smaller; the method can complete the fault identification of the RV reducer under the condition of unstable vibration.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is an actual layout diagram of an RV reducer fault simulation experiment table in the invention;
FIG. 3 is a 3D schematic diagram of an RV reducer fault simulation experiment table according to the invention;
FIG. 4 is a diagram showing a planet wheel failure in the present invention;
FIG. 5 is a time domain waveform of a vibration observation signal picked up by a sensor when the RV reducer fault simulation experiment table runs, and the operation of the vibration observation signal after the whole-cycle signal is intercepted is carried out;
FIG. 6 is a time-frequency spectrum of a vibration observation signal picked up by a sensor during operation of the bearing fault simulation experiment table according to the present invention; and intercepts the stable part in the rising phase of the observed signal (intercepts about 0.5s of signal);
FIG. 7 is a time domain waveform of a vibration observation signal after a stabilization phase is intercepted by time-frequency analysis according to the present invention;
FIG. 8 is a time domain waveform of a steady-state signal filtered by a NeighCoeff algorithm according to the present invention;
FIG. 9 is a frequency domain waveform of a filtered signal after FFT in accordance with the present invention;
FIG. 10 is a schematic cepstral diagram of a filtered signal subjected to cepstral analysis in accordance with the present invention;
in fig. 2 and 3, the reference numerals are as follows: the test bench comprises a 1-joint arm, a 2-acceleration sensor, a 3-reducer retainer, a 4-RV reducer, a 5-motor and a 6-test bench base.
Detailed Description
The invention will be further described with reference to the following figures and examples, but the scope of the invention is not limited thereto.
Example 1: as shown in fig. 1-10, a fault diagnosis method for an RV reducer includes:
s1, firstly, mounting the acceleration sensor 2 on the upper surface of the reducer holder 3, picking up an observation signal S (n) of mechanical vibration in the reciprocating intermittent motion process of the articulated arm 1 through the acceleration sensor 2, and calculating the relevant theoretical characteristic frequency of the RV reducer; wherein n is the sampling time of the vibration signal s (n);
s2, intercepting the observation signal S (n) obtained in the step S1 to obtain a periodic motion signal S including a rising stage and a falling stage 1 (n)；
S3, obtaining the periodic motion signal S in the step S2 1 (n) performing continuous wavelet transform, and synchronously intercepting periodic motion signal s according to time-frequency diagram 1 (n) rising phase, approximately stationary vibration signal of sample length m, as pre-processingSignal s 2 (n)；
S4, pre-processing signal S obtained in step S3 2 (n) filtering to obtain a filtered signal x (n);
and S5, carrying out FFT on the filtering signal x (n) obtained in the step S4, and carrying out fault diagnosis on the frequency domain signal after transformation.
Further, it may be provided that the relevant theoretical characteristic frequency of the RV reducer comprises a sun gear revolution frequency f 1 Planet wheel frequency conversion f 2 Support plate frequency conversion f 3 Planet gear fault frequency f s r Sun gear and planet gear engagement frequency f 1c 。
Since the RV reducer fault is mostly occurred in the primary speed reducing mechanism (i.e. the planetary gear train), further, the specific steps of calculating the relevant theoretical characteristic frequency of the RV reducer can be set as follows:
s1.1, knowing the preset speed n of the articulated arm 1 3 By calculating the rotational speed n 3 Conversion to a rotary frequency, and the knuckle arm rotary frequency is equivalent to a supporting disc rotary frequency f 3 (ii) a According to the formula
Calculating to obtain the main shaft rotating speed n of the motor 5 1 (ii) a Wherein n is 1 The spindle speed of the motor 5; z 1 Is the number of sun gear teeth, Z 2 Number of teeth of planet gear, Z 4 The number of teeth of the pin gear;
s1.2, by following the formula
The revolution frequency f of the sun gear is obtained through calculation 1 ；
S1.3, according to the formula
And
calculating the planet wheel rotation frequency f of the RV reducer 2 And the engagement frequency (i.e. primary engagement frequency) f of the sun and planet gears 1c (ii) a Wherein，Z 3 The number of teeth of the cycloid gear is shown;
s1.4, failure frequency f of planet wheel s r The calculation formula is f for the rotation frequency of the planet wheel relative to the planet carrier s r ＝f 2 -f 3 。
Further, in the continuous wavelet transform, a Morlet wavelet may be selected as a mother wavelet.
Further, the sample length m is set to be more than or equal to 10F max (ii) a Wherein, F max Representing 2 times the highest frequency value among the relevant theoretical characteristic frequencies of the RV reducer. Because the signal collected by the RV reducer is a transient impact signal, when the intercepted sample length does not meet the requirement, the amplitude at the moment of impact cannot be captured, obviously, the sample length mode intercepted by the method also considers the signal frequency which is more than 10 times on the basis of considering 2 times of the highest frequency value in the related theoretical characteristic frequencies of the RV reducer, thereby not only ensuring the frequency of the signal not to be distorted, but also further ensuring the amplitude of the signal not to be distorted, and ensuring that the collected signal can be fit to the reality. Specifically, in the embodiment of the present invention, the maximum frequency of the analysis is 465.87Hz, and considering that 2-fold frequency may be needed, the number of sample points should not be less than 9300, so the length m of the sample obtained by the analysis is 12.5k (i.e. 0.5 s).
Further, it may be arranged to use the NeighCoeff algorithm on the preprocessed signal s 2 (n) filtering.
Further, the fault diagnosis of the transformed frequency domain signal may be set to specifically: carrying out multi-tooth wear fault diagnosis on the planet gear on the frequency domain signal after FFT, and when the frequency domain spectral line and the planet gear fault frequency f s r When the absolute value of the error is more than 0.2Hz (namely more than or equal to 0.2 Hz), determining that the multi-tooth wear fault of the planet wheel does not occur; when the error between the frequency domain spectral line and the fault frequency of the planet wheel is between-0.2 Hz and 0Hz or the value is 0 (namely the value between-0.2 Hz and 0Hz or the value is 0), the multi-tooth wear fault of the planet wheel is determined; when the error between the frequency domain spectral line and the planet wheel fault frequency is between 0Hz and 0.2Hz (namely, when the error takes a value between 0Hz and 0.2 Hz), filtering signals x (n)And performing cepstrum analysis to realize fault diagnosis.
Still further, in step S3, the specific steps of performing continuous wavelet transform on the periodic signal are as follows:
s3.1, setting function psi ∈ L 2 (R)∩L 1 (R) and
from psi, a cluster of functions can be derived via scaling and translation:
wherein a, b belongs to R, a is not equal to 0 and is called psi a,b Is a continuous wavelet, a is a scale factor, b is a translation factor, and psi is a mother wavelet. a is used for changing the shape of the continuous wavelet, and b is used for changing the displacement of the continuous wavelet;
s3.2, for any function, the continuous wavelet transform is defined as:
in the formula (I), the compound is shown in the specification,<f,ψ a,b >the inner product of the two functions is represented,
represents the complex conjugate of ψ (t);
s3.3, each wavelet transformation coefficient W can be obtained through the sub-wavelet with the scale a and the translation b and the signal inner product a,b (a, b) when the signal is more similar to the wavelet, the larger the coefficient value, the more the feature will converge somewhere on the time scale phase plane into a high amplitude block of energy, otherwise the energy will diverge. Finally obtaining a coefficient matrix W through different scale transformation f (a, b) are capable of characterizing a two-dimensional time scale of the signal. The Morlet wavelet is selected as the mother wavelet considering that the Morlet wavelet is similar to the shape of the impact signal generated when the rotating machine is in failure.
In S4, NeighCoe was usedff algorithm on the preprocessed signal S obtained in step S3 2 (n) filtering, specifically:
s4.1, performing discrete wavelet transform on a signal containing noise;
s4.2, grouping the wavelet coefficients into blocks B with length L for each scale j i j ；
S4.3, for each block B i j Estimating new coefficients by using a contraction rule;
in the formula: λ is a parameter used to adjust the threshold, and the length L is L ═ ln n, where n is the signal length;
is determined by the following formula:
and S4.4, performing inverse wavelet transform on the obtained wavelet coefficient to obtain a new signal, and completing signal noise reduction.
The working condition of the existing industrial robot is usually intermittent reciprocating motion, so the acquired signal is usually characterized by non-stable vibration. The invention firstly extracts a signal of the whole cycle movement, then carries out time-frequency analysis on the whole cycle signal by continuous wavelet transform, and intercepts the signal of the stable stage in the ascending movement. By intercepting the step, the non-stationary vibration component in the analyzed signal is reduced, so that the deviation between each theoretical calculation value and each actual calculation value of the signal is reduced as much as possible, and the non-stationary signal is approximately converted into a stationary signal. Then, considering the NeighCoeff method, setting the threshold value by taking the adjacent coefficients as a whole, not only can more characteristic information be kept, but also the noise reduction effect is better than that of the wavelet threshold value. Therefore, the invention uses the NeighCoeff method to filter and reduce noise of the intercepted signal while keeping more impact information. And finally, carrying out FFT on the noise reduction signal, and carrying out fault diagnosis on the transformed frequency domain signal.
Example 2: as shown in fig. 1-10, and further, in conjunction with experimental data, the present invention presents alternative embodiments as follows:
a mechanical fault diagnosis method for an RV reducer is characterized in that a test bed used in the example is an RV reducer test bed for simulating joint motion of a robot, and FIG. 3 shows collection positions of the test bed and sensors, wherein the test bed comprises a joint arm 1, an acceleration sensor 2, a reducer retainer 3, an RV reducer 4, a motor 5 and a test bed base 6; wherein, install reduction gear holder 3 on the test bench base 6, install RV reduction gear 4 through reduction gear holder 3, the output shaft of motor 5 passes through RV reduction gear 4 and connects articulated arm 1. RV40E model reduction gear is selected for use in the experiment and is fixed in the test bench with the pin wheel, and its main parameter includes that the reduction ratio is 121, planetary gear figure 2, sun gear tooth number 12, planet wheel tooth number 42, cycloid wheel tooth number 39 and pin wheel tooth number 40. FIG. 4 is a physical diagram of a planet wheel containing a multi-tooth wear failure. The acquisition system is composed of an NI-USB9234 acquisition card and a one-way acceleration sensor. The vibration signal is acquired by an accelerator sensor placed on the reducer holder, and the sampling frequency is 25.6 kHz. Presetting an experiment: the articulated arm is fixed on a supporting disc in the RV reducer 4, and the movement range is 0-90 degrees (single lifting or descending is 90 degrees). The swing arm operating speed is 100 degrees. The result of each characteristic frequency can be directly calculated according to the parameters as follows: sun gear frequency conversion f 1 Is 39.1Hz, and the planet wheel frequency conversion is f 2 11.09Hz, supporting disk frequency conversion of 0.28Hz and primary transmission meshing frequency f 1c Is 465.87Hz, planet wheel fault frequency f s r Is 10.81 Hz. The specific diagnosis method comprises the following steps:
s1, firstly, mounting the acceleration sensor 2 on the upper surface of the reducer holder 3, picking up an observation signal S (n) of mechanical vibration in the reciprocating intermittent motion process of the robot joint arm 1 through the acceleration sensor 2, and calculating the relevant theoretical characteristic frequency of the RV reducer; wherein n is the sampling time of the vibration signal s (n);
s2, step S1The sampled observation signal s (n) is intercepted to obtain a periodic motion signal s which simultaneously comprises an ascending stage and a descending stage 1 (n) as shown in FIG. 5;
s3, passing formula
The periodic motion signal S obtained in step S2 1 (n) performing continuous wavelet transform to obtain coefficient matrix W f (a, b) and plots the time-frequency plot of the signal, as shown in FIG. 6. And intercepting the signal of the stable stage in the rising motion according to the time-frequency diagram (intercepting the signal between about 0.5s and 1 s), thereby obtaining a preprocessed signal s 2 (n) as shown in FIG. 7;
s4, the NeighCoeff algorithm filtering operation is as follows: first of all for the preprocessed signal s 2 (n) obtaining wavelet coefficients using a discrete wavelet transform. Then according to the formula
And formulas
The wavelet coefficient is corrected, and then filtering of the reconstructed signal is performed to obtain a filtered signal x (n), as shown in fig. 8;
s5, FFT is performed on the filtered signal x (n) in step S4, to obtain fig. 9. The fault frequency in fig. 9 is 10.94Hz and frequency multiplication, and the actual fault frequency (10.94Hz) is between the planetary wheel rotation frequency f 2 (11.09Hz) and theoretical planet wheel fault frequency f s r (10.81Hz), the error is between 0Hz and 0.2Hz, and the fault diagnosis can not be accurately finished;
s6, considering that the frequency spectrum can not judge the fault, carrying out cepstrum analysis on the filtering signal x (n) in the step S5, wherein a cepstrum graph is shown as 10; two more obvious spectral lines exist simultaneously, the frequency of the spectral line on the left side is calculated to be 11.09Hz, the frequency of the spectral line on the right side is calculated to be the planet wheel frequency conversion, the frequency of the spectral line on the right side is calculated to be 10.94Hz, and the spectral lines with the frequencies of 11.09Hz and 10.94Hz simultaneously appear in the cepstrum, so that the multi-tooth wear fault of the planet wheel can be considered to occur.
Example 3: a fault diagnosis device for an RV reducer, comprising:
the signal pickup module is used for picking up an observation signal s (n) of mechanical vibration in the reciprocating intermittent motion process of the joint arm 1 through the acceleration sensor 2 and calculating the relevant theoretical characteristic frequency of the RV reducer; wherein n is the sampling time of the vibration signal s (n);
a first obtaining module, configured to intercept the observation signal s (n) obtained by the signal pickup module by a whole-cycle signal to obtain a periodic motion signal s including a rising phase and a falling phase 1 (n)；
An intercepting module for intercepting the periodic motion signal s obtained by the first obtaining module 1 (n) performing continuous wavelet transform, and synchronously intercepting periodic motion signal s according to time-frequency diagram 1 (n) rising phase, approximately stationary vibration signal of sample length m, as preprocessing signal s 2 (n)；
A second obtaining module for obtaining the preprocessed signal s 2 (n) filtering to obtain a filtered signal x (n);
and the fault diagnosis module is used for performing FFT on the filtering signal x (n) obtained by the third obtaining module and performing fault diagnosis on the transformed frequency domain signal.
Example 4: a processor for executing a program, wherein the program when executed performs any of the above-described fault diagnosis methods for a RV reducer.
Example 5: a computer-readable storage medium including a stored program, wherein when the program runs, the apparatus where the computer-readable storage medium is located is controlled to execute the fault diagnosis method for the RV reducer.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
While the present invention has been described in detail with reference to the embodiments, the present invention is not limited to the embodiments, and various changes can be made without departing from the spirit of the present invention within the knowledge of those skilled in the art.
Claims (10)
1. A fault diagnosis method for an RV reducer is characterized in that: the method comprises the following steps:
s1, picking up an observation signal S (n) of mechanical vibration in the reciprocating intermittent motion process of the articulated arm (1) through the acceleration sensor (2), and calculating the relevant theoretical characteristic frequency of the RV reducer; wherein n is the sampling time of the vibration signal s (n);
s2, intercepting the observation signal S (n) obtained in the step S1 to obtain a periodic motion signal S including a rising stage and a falling stage 1 (n)；
S3, obtaining the periodic motion signal S in the step S2 1 (n) performing continuous wavelet transform, and synchronously intercepting periodic motion signal s according to time-frequency diagram 1 (n) rising phase, approximately stationary vibration signal of sample length m, as preprocessing signal s 2 (n)；
S4, pre-processing signal S obtained in step S3 2 (n) filtering to obtain a filtered signal x (n);
and S5, carrying out FFT on the filtering signal x (n) obtained in the step S4, and carrying out fault diagnosis on the frequency domain signal after transformation.
2. The fault diagnosis method for an RV reducer according to claim 1, characterized in that: the relevant theoretical characteristic frequency of the RV reducer comprises the revolution frequency f of the sun gear 1 Planet wheel frequency conversion f 2 Support plate frequency conversion f 3 Planet wheel failure frequency f s r Sun gear and planet gear engagement frequency f 1c 。
3. The fault diagnosis method for an RV reducer according to claim 1, characterized in that: the specific steps for calculating the relevant theoretical characteristic frequency of the RV reducer are as follows:
s1.1, knowing the preset rotation speed n of the articulated arm (1) 3 By calculating the rotational speed n 3 Conversion to a frequency conversion, and knuckle arm frequency conversion equivalent to support plate frequency conversion f 3 (ii) a According to the formula
The rotating speed n of the main shaft of the motor (5) is obtained by calculation 1 (ii) a Wherein n is 1 The rotating speed of a main shaft of the motor (5); z 1 Is the number of sun gear teeth, Z 2 Number of teeth of planet gear, Z 4 The number of teeth of the pin gear;
s1.2, by following the formula
The revolution frequency f of the sun gear is obtained through calculation 1 ；
S1.3, according to the formula
And
calculating the planet wheel rotation frequency f of the RV reducer 2 And the meshing frequency f of the sun wheel and the planet wheel 1c (ii) a Wherein Z is 3 The number of teeth of the cycloid gear is shown;
s1.4, failure frequency f of planet wheel s r The calculation formula is f for the rotation frequency of the planet wheel relative to the planet carrier s r ＝f 2 -f 3 。
4. The fault diagnosis method for an RV reducer according to claim 1, characterized in that: in the continuous wavelet transformation, Morlet wavelets are selected as mother wavelets.
5. The fault diagnostic for an RV reducer of claim 1The method is characterized in that: the sample length m is more than or equal to 10F max (ii) a Wherein, F max Representing 2 times the highest frequency value among the relevant theoretical characteristic frequencies of the RV reducer.
6. The fault diagnosis method for an RV reducer according to claim 1, characterized in that: preprocessing the signal s using the NeighCoeff algorithm 2 (n) filtering.
7. The fault diagnosis method for an RV reducer according to claim 1, characterized in that: the fault diagnosis of the transformed frequency domain signal specifically comprises: carrying out multi-tooth wear fault diagnosis on the planet gear on the frequency domain signal after FFT, and when the frequency domain spectral line and the planet gear fault frequency f s r When the absolute value of the error is more than 0.2Hz, the multi-tooth abrasion fault of the planet wheel is determined not to occur; when the error between the frequency domain spectral line and the fault frequency of the planet wheel is between minus 0.2Hz and 0Hz or the value is 0, the planet wheel is determined to be a multi-tooth wear fault; when the error between the frequency domain spectral line and the planet wheel fault frequency is between 0Hz and 0.2Hz, cepstrum analysis is carried out on the filtering signal x (n), and fault diagnosis is realized.
8. A failure diagnosis device for an RV reducer, characterized in that: the method comprises the following steps:
the signal pickup module is used for picking up an observation signal s (n) of mechanical vibration in the reciprocating intermittent motion process of the articulated arm (1) through the acceleration sensor (2) and calculating the relevant theoretical characteristic frequency of the RV reducer; wherein n is the sampling time of the vibration signal s (n);
a first obtaining module, configured to intercept the observation signal s (n) obtained by the signal pickup module by a whole-cycle signal to obtain a periodic motion signal s including a rising phase and a falling phase 1 (n)；
An intercepting module for intercepting the periodic motion signal s obtained by the first obtaining module 1 (n) performing continuous wavelet transform, and synchronously intercepting periodic motion signal s according to time-frequency diagram 1 (n) a rise phase with sample length of mStationary vibration signal as preprocessing signal s 2 (n)；
A second obtaining module for obtaining the preprocessed signal s 2 (n) filtering to obtain a filtered signal x (n);
and the fault diagnosis module is used for performing FFT on the filtering signal x (n) obtained by the third obtaining module and performing fault diagnosis on the transformed frequency domain signal.
9. A processor, characterized in that: the processor is configured to execute a program, wherein the program executes the fault diagnosis method for the RV reducer according to any one of claims 1 to 7.
10. A computer-readable storage medium characterized by: the computer-readable storage medium includes a stored program, wherein the program, when executed, controls an apparatus in which the computer-readable storage medium is located to perform the fault diagnosis method for the RV reducer according to any one of claims 1 to 7.
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