CN112834222B - Method for dynamically monitoring service life of train bearing and electronic equipment - Google Patents
Method for dynamically monitoring service life of train bearing and electronic equipment Download PDFInfo
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- G01M13/00—Testing of machine parts
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Abstract
The invention belongs to the technical field of bearing life prediction, and particularly relates to a method and electronic equipment for dynamically monitoring the service life of a train bearing, wherein the method comprises the following steps: collecting vibration acceleration a of bearing in X, Y and Z directions through sensor x 、a y And a z The X direction and the Z direction are radial bearings, the X direction is parallel to the driving direction, the Z direction is perpendicular to the driving direction, and the Y direction is axial bearings; calculating radial load and axial load of the bearing according to average axle weight, unsprung mass and vibration acceleration of the train; calculating the equivalent dynamic load of the bearing according to the radial load, the axial load and the bearing type of the bearing; and calculating the basic rated life of the bearing according to the basic rated dynamic load, the equivalent dynamic load and the life index of the bearing. The real and accurate vibration data of each bearing are acquired through the sensor, the service life of the bearing is calculated according to the vibration data of the bearing, the service life prediction is refined to each bearing, and the accurate assessment and dynamic monitoring of the reliability of the bearing are realized.
Description
Technical Field
The invention belongs to the technical field of bearing life prediction, and particularly relates to a method for dynamically monitoring the service life of a train bearing and electronic equipment.
Background
The subway axle box bearing is critical to the safe operation of the train, and once the bearing fails in the train on-line running process, the bearing is not processed in time, so that the result is not considered. Therefore, there is a need to monitor the operational health of the shaft box bearings.
The method has the advantages that the residual service life of the bearing is accurately estimated, the current running state of the bearing can be fed back to the owner in real time, the owner is guided to make preparation for maintenance and replacement in time, the resource waste caused by early maintenance can be avoided, and the potential safety hazard caused by too late discovery can be avoided.
The current domestic methods for predicting the residual life of the bearing are roughly divided into two types: the first is intelligent prediction class, which comprises algorithms such as deep learning, random process, neural network and the like. The intelligent algorithm is used for predicting the life-span data of the bearing, a prediction model is constructed, and a health index is constructed by using multidimensional characteristic values in the construction process, so that the performance degradation condition of the bearing can be described on the basis, and the life-span of the bearing is further predicted. The bearings used in subways belong to non-calibrated products in many cases, the whole life data of the bearings are lacked, and a later prediction model is difficult to construct.
And secondly, two-dimensional calculation, namely comprehensively evaluating and calculating the residual life of the bearing by taking a load working condition and a line working condition as two dimensions and combining related parameters of the bearing when calculating the life of the bearing. Most scholars do not consider the dynamic nature of the actual working condition of the bearings and the difference of the running states among different bearings, so that the calculated residual service life is a constant value, each bearing is not refined, and the actual situation is not met.
Disclosure of Invention
In order to solve the problems, the invention provides a method for dynamically monitoring the service life of a train bearing and electronic equipment.
Specifically, the technical scheme of the invention is as follows:
a method of dynamically monitoring the service life of a train bearing comprising the steps of:
s1: collecting vibration acceleration a of bearing in X, Y and Z directions through sensor x 、a y And a z The X direction and the Z direction are radial bearings, the X direction is parallel to the driving direction, the Z direction is perpendicular to the driving direction, and the Y direction is axial bearings;
s2: calculating radial load and axial load of the bearing according to average axle weight, unsprung mass and vibration acceleration of the train;
s3: calculating the equivalent dynamic load of the bearing according to the radial load, the axial load and the bearing type of the bearing;
s4: and calculating the basic rated life of the bearing according to the basic rated dynamic load, the equivalent dynamic load and the life index of the bearing.
Preferably, S1 comprises:
collecting vibration acceleration a of bearing in X, Y and Z directions through triaxial acceleration sensor x 、a y And a z ；
And carrying out wavelet noise reduction on the acquired vibration acceleration data.
Preferably, S2 comprises:
according to the axle weights of wheel sets under different load working conditions and the occurrence probability of each load working condition, calculating to obtain the average axle weight of the train by a weighted average method;
according to F Z1 ＝(F t -m 0 * g) Calculating static load F of bearing in Z direction z1 Wherein F is t For average axle weight of train, m 0 G is gravitational acceleration;
according to F z2 ＝m 0 *a z Calculating dynamic load F of bearing in Z direction z2 ；
According to F rz ＝F z1 +F z2 Calculating the load F of the bearing in the Z direction rz ；
According to F rx ＝m 0 *a x Calculating the load F of the bearing in the X direction rx ；
According to
Calculating radial load F of bearing r ；
According to F a ＝m 0 *a y Calculating the axial load F of the bearing a 。
Preferably, S3 is specifically:
according to p=mf r +nF a And calculating the equivalent dynamic load P of the bearing, wherein m is a radial load coefficient, n is an axial load coefficient, and m and n are determined according to the type of the bearing.
Preferably, S4 is specifically:
according to
Calculating the basic rated life L of a bearing 10 Wherein C is the basic rated dynamic load, epsilon is the life index.
Preferably, the method further comprises:
s5: for the basic nominal life L, based on the operating temperature of the bearing and/or the impact to which the bearing is subjected 10 And (5) performing correction.
Preferably, S5 is specifically: according to
Correcting the basic rated life L10, wherein f t Is the temperature coefficient, f p Is the impact coefficient.
Preferably, the method further comprises:
s6: according to L 10s ＝L 10 *πD/10 3 Will have a basic rated life L 10 Converted into the mileage L 10s Wherein D is tread diameter;
s7: according to l=l 10s -S, calculating the remaining life L of the bearing, wherein S is the number of miles travelled by the train.
Preferably, the method further comprises:
collecting the running speed v of the train through a speed sensor arranged on the train;
and calculating the number of the running mileage S of the train according to the S= [ pi ] vdt.
An electronic device comprising a memory and a processor, the memory having stored thereon readable instructions which, when executed by the processor, implement a method of dynamically monitoring the service life of a train bearing as described above.
After the scheme is adopted, the beneficial effects are as follows:
vibration data of the bearings are collected through the sensors so as to obtain real and accurate dynamic data of each bearing, service life of the bearings is calculated according to the vibration data of the bearings, service life prediction is refined to each bearing, and accurate assessment and dynamic monitoring of bearing reliability are achieved.
Drawings
FIG. 1 is a flow chart of a first embodiment of the present invention;
fig. 2 is a diagram of directions according to the present invention.
Detailed Description
The invention will be described in further detail with reference to the accompanying drawings and specific examples.
Embodiment one:
as shown in fig. 1, a method for dynamically monitoring the service life of a train bearing comprises the following steps:
s1: collecting vibration acceleration a of bearing in X, Y and Z directions through sensor x 、a y And a z 。
As shown in fig. 2, the hatched portion is the cross section of the bearing, and the X and Z directions are the radial directions of the bearing, wherein the X direction is parallel to the traveling direction v, and the Z direction is perpendicular to the traveling direction v; the Y direction is the axial direction of the bearing and is vertical to the radial direction of the bearing.
S2: and calculating the radial load and the axial load of the bearing according to the average axle weight, the unsprung mass and the vibration acceleration of the train.
The radial load of the bearing comprises the load of the bearing in the X direction and the load of the bearing in the Y direction, and the axial load of the bearing is the load of the bearing in the Y direction.
The load of the bearing in the Z direction comprises the static load of the bearing in the Z direction and the dynamic load of the bearing in the Z direction, wherein the static load of the bearing in the Z direction is mainly caused by the weight of passengers and the weight of a vehicle body, and can be calculated according to the average axle weight and the unsprung mass of a train.
Dynamic load of the bearing in the Z direction is mainly caused by factors such as out-of-round tread, vertical irregularity of a track and the like; the dynamic load of the bearing in the X direction is mainly caused by factors such as traction force, braking force and the like in the acceleration and deceleration processes of the train; the dynamic load of the bearing in the Y direction is mainly caused by the factors of curve superelevation, snake-shaped swing, track transverse irregularity and the like; the dynamic load is calculated from the unsprung mass and the vibration acceleration.
The calculation method of the average axle weight of the train is not limited to the method provided in the present embodiment; the steps of calculating the radial load and the axial load of the bearing are not limited to those provided in the present embodiment.
Further, in the present embodiment, S2 includes:
s21: according to the axle weights of wheel sets under different load working conditions and the occurrence probability of each load working condition, calculating by a weighted average method to obtain the average axle weight F of the train t 。
Under different load working conditions, the axle weights of the wheel sets are different; the probability of occurrence of each load condition is also different. In this embodiment, the changing axle weight of the wheel pair is reduced to a constant (average axle weight of the train) by a weighted average method, and in other embodiments, the duration of each load condition can be counted, so that the service life of the bearing can be calculated more accurately.
Taking a subway as an example, according to passenger density, the load working conditions are divided into: no load AW0, guest load AW1, constant load AW2, overman load AW3. The probability of each load condition occurring is shown in table 1:
table 1:
| load condition | Axle weight of wheel pair | Probability of |
| AW0 | F N0 | p 0 |
| AW1 | F N1 | p 1 |
| AW2 | F N2 | p 2 |
| AW3 | F N3 | p 3 |
The calculation formula is as follows:
s22: according to F Z1 ＝(F t -m 0 * g) Calculating static load of bearing in Z directionF z1 Wherein F is t For average axle weight of train, m 0 G is gravitational acceleration, which is the unsprung mass.
S23: according to F z2 ＝m 0 *a z Calculating dynamic load F of bearing in Z direction z2 。
S24: according to F rz ＝F z1 +F z2 Calculating the load F of the bearing in the Z direction rz 。
S25: according to F rx ＝m 0 *a x Calculating the load F of the bearing in the X direction rx 。
S26: according to
Calculating radial load F of bearing r 。
S27: according to F a ＝m 0 *a y Calculating the axial load F of the bearing a 。
In other embodiments, the axial load may be calculated first, then the radial load may be calculated, and when the radial load is calculated, the load of the bearing in the X direction may be calculated first, then the load of the bearing in the Z direction may be calculated.
S3: and calculating the equivalent dynamic load of the bearing according to the radial load, the axial load and the bearing type of the bearing.
When the actual load acting on the bearing is different from the basic rated dynamic load loading condition of the bearing, the actual load needs to be converted into an imaginary load (equivalent dynamic load) identical to the basic rated dynamic load loading condition. The calculation of equivalent dynamic load is related to the bearing type, in this embodiment, the bearing is a radial roller bearing with a bearing pressure angle α+.0, and S3 is specifically: according to p=mf r +nF a The equivalent dynamic load P of the bearing is calculated, where m is the radial load coefficient and n is the axial load coefficient, as shown in table 2, with m and n being determined according to the bearing type.
Table 2:
s4: and calculating the basic rated life of the bearing according to the basic rated dynamic load, the equivalent dynamic load and the life index of the bearing.
The basic rated life of a bearing refers to a batch of identical bearings operating under identical conditions, wherein 90% of the bearings do not exhibit fatigue pitting or the number of operating hours at a certain number of revolutions. In this embodiment, S4 is specifically:
according to
Calculating the basic rated life L of a bearing 10 Wherein L is 10 The unit is r, C is basic rated dynamic load, epsilon is life index; the roller bearing epsilon is 10/3.
And S1-S4, converting the easily obtained bearing vibration signals into three-way dynamic loads of the bearing, and combining the static loads to realize the calculation of the service life of the bearing.
Further, in the present embodiment, the three-axis acceleration sensor is applied to the bearing life calculation field, and S1 includes:
s11: collecting vibration acceleration a of bearing in X, Y and Z directions through triaxial acceleration sensor x 、a y And a z 。
S12: wavelet noise reduction is carried out on the collected vibration acceleration data so as to filter useless components such as noise and the like and extract real dynamic vibration components.
In other embodiments, other existing means may be used to collect the bearing vibration acceleration in the X, Y and Z directions.
Further, the method further comprises:
s5: for the basic nominal life L, based on the operating temperature of the bearing and/or the impact to which the bearing is subjected 10 And (5) performing correction.
In this embodiment, S5 is specifically: according to
Correcting basic rated life L 10 Wherein f t The value of f is shown in Table 3 as the temperature coefficient p The impact coefficient was as shown in Table 4.
Table 3:
| bearing operating temperature | 100 | 125 | 150 | 200 | 250 | 300 |
| Temperature coefficient f t | 1 | 0.95 | 0.9 | 0.8 | 0.7 | 0.6 |
Table 4:
| machinery | f p |
| Electric motor, steam turbine, ventilator, water pump, etc | 1.0～1.2 |
| Power machinery, crane, etc | 1.2～1.8 |
The lifetime calculated in steps S1 to S4 can be corrected in step S5.
Further, the method further comprises:
s6: according to L 10s ＝L 10 *πD/10 3 Will have a basic rated life L 10 Converted into the mileage L 10s Wherein, D is tread diameter.
S7: according to l=l 10s -S, calculating the remaining life L of the bearing, wherein S is the number of miles travelled by the train.
The remaining life of the bearing in km can be calculated by steps S1 to S7, which is more intuitive and practical than the life (in revolutions) calculated by steps S1 to S4 or steps S1 to S5.
Further, the method further comprises:
collecting the running speed v of the train through a speed sensor arranged on the train;
and calculating the number of the running mileage S of the train according to the S= [ pi ] vdt.
Compared with the means of obtaining the number of the running mileage of the train through GPS positioning and the like, the speed sensor is utilized to calculate the number of the running mileage of the train, and the method is not influenced by the strength of positioning signals and is more reliable.
The bearing life prediction method can be applied to bearings at the axle boxes of subways, bearings at the gear boxes and bearings at the motors, and can also be applied to rail transportation tools with similar structures with subways. Taking a bearing at a subway axle box as an example, the method comprises the following steps:
step one: a triaxial acceleration sensor is arranged at a subway axle box to acquire vibration acceleration data a of the bearing in X, Y, Z three directions x 、a y And a z ；
Step two: installing a speed sensor in the running direction of the subway to obtain the real-time speed v of the subway;
step three: wavelet noise reduction is carried out on the collected acceleration data;
step four: calculating the average axle weight of the subway, wherein the calculation formula is as follows:
step five: calculating the Z-direction static load of the bearing, wherein the calculation formula is as follows: f (F) z1 ＝(F t -m 0 *g)/2(N)。
Step six: calculating the Z-direction dynamic load of the bearing, wherein the calculation formula is as follows: f (F) z2 ＝m 0 /2*a z (N)。
Step seven: the Z-direction load of the bearing is calculated, and the calculation formula is as follows: f (F) rz ＝F z1 +F z2 (N)。
Step eight: the X-direction load of the bearing is calculated, and the calculation formula is as follows: f (F) rx ＝m 0 /2*a x (N)。
Step nine: the radial load born by the bearing is calculated, and the calculation formula is as follows:
step ten: calculating the axial load born by the bearing, wherein the calculation formula is as follows: f (F) a ＝m 0 /2*a y (N)。
Step eleven: the equivalent dynamic load of the radial roller bearing with the bearing pressure angle alpha not equal to 0 is calculated by the following formula: p=mf r +nF a (N)。
Step twelve: the basic rated life of the bearing is calculated, and the calculation formula is as follows:
step thirteen: correcting the basic rated life of the bearing, after correction
Step fourteen: the basic rated life is converted into the mileage, and the calculation formula is as follows: l (L) 10s ＝L 10 *πD/10 3 (km)。
Fifteen steps: calculating the number of the running mileage of the train, wherein the calculation formula is as follows: s= ≡vdt (km).
Step sixteen: calculating the residual life of the bearing, wherein the calculation formula is as follows: l=l 10s -S(km)。
The above description is only of the preferred embodiments of the present invention, and is not intended to limit the present invention. It should be noted that any modifications, equivalent substitutions, improvements, etc. made by those skilled in the art without departing from the spirit and principles of the present invention are intended to be included in the scope of the present invention.
Claims (10)
1. A method for dynamically monitoring the service life of a train bearing, comprising the steps of:
s1: collecting vibration acceleration a of bearing in X, Y and Z directions through sensor x 、a y And a z The X direction and the Z direction are radial bearings, the X direction is parallel to the driving direction, the Z direction is perpendicular to the driving direction, and the Y direction is axial bearings;
s2: calculating radial load and axial load of the bearing according to average axle weight, unsprung mass and vibration acceleration of the train; s2 comprises the following steps:
according to F z1 ＝(F t -m 0 * g) Calculating static load F of bearing in Z direction z1 Wherein F is t For average axle weight of train, m 0 G is gravitational acceleration;
according to F z2 ＝m 0 *a z Calculating dynamic load F of bearing in Z direction z2 ；
According to F rz ＝F z1 +F z2 Calculating the load F of the bearing in the Z direction rz ；
According to F rx ＝m 0 *a x Calculating the load F of the bearing in the X direction rx ；
According to
Calculating radial load F of bearing r ；
According to F a ＝m 0 *a y Calculating the axial load F of the bearing a ；
S3: calculating the equivalent dynamic load of the bearing according to the radial load, the axial load and the bearing type of the bearing;
s4: and calculating the basic rated life of the bearing according to the basic rated dynamic load, the equivalent dynamic load and the life index of the bearing.
2. The method of dynamically monitoring the service life of a train bearing according to claim 1, wherein S1 comprises:
collecting vibration acceleration a of bearing in X, Y and Z directions through triaxial acceleration sensor x 、a y And a z ；
And carrying out wavelet noise reduction on the acquired vibration acceleration data.
3. The method of dynamically monitoring the service life of a train bearing according to claim 1, wherein S2 further comprises:
and calculating the average axle weight of the train by a weighted average method according to the axle weights of the wheel sets under different load working conditions and the occurrence probability of each load working condition.
4. A method for dynamically monitoring the service life of a train bearing according to claim 3, wherein S3 is specifically:
according to p=mf r +nF a And calculating the equivalent dynamic load P of the bearing, wherein m is a radial load coefficient, n is an axial load coefficient, and m and n are determined according to the type of the bearing.
5. The method for dynamically monitoring the service life of a train bearing according to claim 4, wherein S4 is specifically:
according to
Calculating the basic rated life L of a bearing 10 Wherein C is the basic rated dynamic load, epsilon is the life index.
6. The method of dynamically monitoring the service life of a train bearing of claim 5, further comprising:
s5: for the basic nominal life L, based on the operating temperature of the bearing and/or the impact to which the bearing is subjected 10 And (5) performing correction.
7. The method for dynamically monitoring the service life of a train bearing according to claim 6, wherein S5 is specifically: according to
Correcting basic rated life L 10 Wherein f t Is the temperature coefficient, f p Is the impact coefficient.
8. The method of dynamically monitoring the service life of a train bearing of claim 5, further comprising:
s6: according to L 10s ＝L 10 *πD/10 3 Will have a basic rated life L 10 Converted into the mileage L 10s Wherein D is tread diameter;
s7: according to l=l 10s -S, calculating the remaining life L of the bearing, wherein S is the number of miles travelled by the train.
9. The method of dynamically monitoring the service life of a train bearing of claim 8, further comprising:
collecting the running speed v of the train through a speed sensor arranged on the train;
and calculating the number of the running mileage S of the train according to the S= [ pi ] vdt.
10. An electronic device comprising a memory and a processor, characterized in that: a method of dynamically monitoring the service life of a train bearing as claimed in any one of claims 1 to 9 when the readable instructions are stored in a memory and executed by a processor.
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| CN115855496B (en) * | 2022-12-01 | 2024-04-12 | 广州汽车集团股份有限公司 | Method, device, equipment and medium for monitoring residual life of vehicle hub bearing |
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| CN105973597B (en) * | 2016-05-27 | 2019-04-09 | 北京交通大学 | The test prediction method in train axle box bearing service life |
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