CN103940608B - A kind of improve the method that gearbox of wind turbine fault level judges precision - Google Patents
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Abstract
The present invention provides a kind of and improves the method that gearbox of wind turbine fault level judges precision, including step: A, determine fault type according to box bearing position vibration signal characteristics value；B, determine each monitoring the currently monitored parameters corresponding to object distribution situation in the fault level matrix of this fault type of gearbox of wind turbine；Described fault level matrix includes each the fault level set, and each fault level includes the parameters span that under this fault level, each monitoring object of gearbox of wind turbine is corresponding；C, calculate the probability that each fault level occurs according to described distribution situation, and determine therefrom that current described fault level.By upper, just can realize different weight based on various faults signal and carry out schematic diagram convergence analysis, it is to avoid the blindness of single failure parameter location fault, reduce rate of false alarm, improve the precision that gearbox of wind turbine fault level judges.
Description
Technical field
The present invention relates to Wind turbines monitoring technical field, improve, particularly to a kind of, the method that gearbox of wind turbine fault level judges precision.
Background technology
Wind turbines machine driving fault the most all can cause the sign of each side factor, the such as appearance of a bearing fault occurred the exception of vibration signal characteristics before this, development along with fault occurs the rising of bearing temperature immediately, the damage completely of bearing finally occurs, the appearance of gear distress is the most also exception vibration signal characteristics first occur, the exception of gear-box inner fluid occurs immediately, the scrapping of the most whole gear-box that fractures of gear tooth finally occurs.
Traditional fault level determination methods only selects vibration amplitude as unique basis for estimation, is judged fault level by algorithm, it is clear that have ignored the impact on fault level of other parameters, thus causes judge fault level inaccurate.
Summary of the invention
The application provides a kind of and improves the method that gearbox of wind turbine fault level judges precision, different weight based on various faults signal carries out schematic diagram convergence analysis, avoid the blindness of single failure parameter location fault, reduce rate of false alarm, improve the precision that gearbox of wind turbine fault level judges.
Described raising gearbox of wind turbine fault level judges that the method for precision includes step:
A, determine fault type according to box bearing position vibration signal characteristics value；
B, determine each monitoring the currently monitored parameters corresponding to object distribution situation in the fault level matrix of this fault type of gearbox of wind turbine；
Described fault level matrix includes each the fault level set, and each fault level includes the parameters span that under this fault level, each monitoring object of gearbox of wind turbine is corresponding；
C, calculate the probability that each fault level occurs according to described distribution situation, and determine therefrom that current described fault level.
By upper, just can realize different weight based on various faults signal and carry out schematic diagram convergence analysis, it is to avoid the blindness of single failure parameter location fault, reduce rate of false alarm, improve the precision that gearbox of wind turbine fault level judges.
Optionally, determine described in step A that fault type includes: determine that fault type is bearing inner race, bearing outer ring, bearing roller or retainer according to the envelope signal that box bearing position vibration signal is corresponding.
By upper, it is achieved for the judgement of fault type.
Optionally, fault level matrix described in step B is arranged previously according to statistics, and the parameters scope that each monitoring object of described gearbox of wind turbine is corresponding includes:
Fault characteristic frequency vibration amplitude [the Vi_x at box bearing position, Vi_y], wear particle rate of increase [IN_Ri_x in gear-box fluid, IN_Ri_y], gear case oil temperature [T_Oi_x, T_Oi_y], the parameters scope of bearing temperature [T_Bi_x, T_Bi_y]；
Described fault level matrix is:
In matrix, F_Leveln represents fault level, and n is positive integer.
By upper, it is achieved the division to the fault level under different faults type.
Optionally, the parameter that different monitoring objects are corresponding is provided with different weights, and described weight includes statically and/or dynamically weight；
When calculating, according to described distribution situation, the probability that each fault level occurs described in step C, herein in connection with described weight calculation.
Optionally, described static weight includes at least one:
The static weight of the fault characteristic frequency vibration amplitude at box bearing position is 1；
Respectively according to wear particle rate of increase, gear case oil temperature, bearing temperature and the degree of correlation of fault characteristic frequency vibration amplitude in gear-box fluid, calculate wear particle rate of increase, gear case oil temperature, the static weight of bearing temperature three in gear-box fluid.
Optionally, described changeable weight includes at least one:
The changeable weight of the fault characteristic frequency vibration amplitude at box bearing position is determined according to Wind turbines realtime power and rated power ratio；
The changeable weight of wear particle rate of increase in gear-box fluid is determined according to the ratio of the natural law currently run with the total natural law of design and operation；
FoundationDetermining the changeable weight of gear case oil temperature and bearing temperature, in formula, W2_TO is the changeable weight of gear case oil temperature, and W2_TB is the changeable weight of bearing temperature, T_ambiFor actual measurement ambient temperature, T_ambmaxFor the maximum of ambient temperature, T_ambminMinima for ambient temperature.
By upper, by the setting of different weights, or static weight is set, or changeable weight is set, or static weight and changeable weight are set simultaneously, the division to each parameters weighting, it is beneficial to blend different parameters with the analysis to fault in varied situations, it is clear that improve accuracy.
Optionally, it is characterised in that when calculating, according to described distribution situation, the probability that each fault level occurs described in step C, described probability corresponding under each fault level uses following manner to calculate:
P=(Vi_Factor*W1_V*W2_V+IN_Ri_Factor*W1_IN_Ri*W2_IN_Ri+TBi_F actor*W1_TB*W2_TB+TOi_Factor*W1_TO*W2_TO)/(W1_V*W2_V+W1_IN_Ri*W2_IN_Ri+W1_TB*W2_TB+W1_TO*W2_TO)；
Wear particle rate of increase, gear case oil temperature and the static weight of bearing temperature during in formula, W1_V, W1_IN_Ri, W1_TB and W1_TO represent the fault characteristic frequency vibration amplitude at box bearing position, gear-box fluid respectively；
Wear particle rate of increase, gear case oil temperature and the changeable weight of bearing temperature during in formula, W2_V, W2_IN_Ri, W2_TB and W2_TO represent the fault characteristic frequency vibration amplitude at box bearing position, gear-box fluid respectively；
The calculating factor under Vi_Factor, IN_Ri_Factor, TBi_Factor and TOi_Factor represent current failure grade respectively in formula, in the range of the parameter value that the currently monitored parameter that monitoring object is corresponding is corresponding under this fault level, calculating factor value is 1, and otherwise value is 0.
By upper, different weights based on various faults signal carry out schematic diagram convergence analysis, it is to avoid the blindness of single failure parameter location fault, reduce rate of false alarm, improve the precision that gearbox of wind turbine fault level judges.
Accompanying drawing explanation
Fig. 1 is the flow chart improving the method that gearbox of wind turbine fault level judges precision；
Fig. 2 is the fault parameter scope schematic diagram corresponding to the different brackets of fault；
Fig. 3 is fault level matrix schematic diagram.
Detailed description of the invention
Involved in the present invention a kind of improve the method that gearbox of wind turbine fault level judges precision, based on wear particle, gear-box oil temperature, the method for four fault parameter convergence analysis of box bearing temperature in box bearing position vibration signal, gear-box fluid, it is achieved improve the precision that gearbox of wind turbine fault level judges.
Principle schematic as shown in Figure 1, including step:
S10: according to historical data, for different faults type, determine the fault parameter scope of its different faults grade.
The fault of gearbox of wind turbine is common in bearing inner race, bearing outer ring, bearing roller and retainer four part.Thus, the fault coming across above-mentioned position everywhere respectively is divided into four kinds of fault types.
Further, above-mentioned four types all comprise level Four fault level, is respectively as follows: fault-free level (F_Level1), minor failure level (F_Level2), generic failure level (F_Level3) and catastrophe failure level (F_Level4).
The fault parameter of gearbox of wind turbine includes: the fault characteristic frequency vibration amplitude (Vi) detected by vibrating sensor；Wear particle rate of increase (IN_Ri) in the gear-box fluid detected by online fluid sensor；The gear case oil temperature (T_Oi) detected by temperature sensor and bearing temperature (T_Bi).
Use historical data when breaking down, count the fault parameter scope corresponding to the different brackets of different faults type.As in figure 2 it is shown, illustrate as a example by vibration amplitude during so that bearing inner race fault to occur.After bearing inner race fault occurs in gearbox of wind turbine, transfer the historical data that sensor is gathered.Fig. 2 abscissa is the time series of sampled point, and vertical coordinate is vibration amplitude.By visual in figure, when vibration amplitude interval is [Vi_1, Vi_2], time, equipment is fault-free level；When vibration amplitude interval is [Vi_3, Vi_4], time, equipment is minor failure level；When vibration amplitude interval is [Vi_5, Vi_6], time, equipment is generic failure level；When vibration amplitude interval is [Vi_7, Vi_8], equipment is catastrophe failure level.In the present embodiment, Vi_1=0.85, Vi_2=1.2, Vi_3=1.7, Vi_4=2.2, Vi_5=2.4, Vi_6=3.2, Vi_7=3.8, Vi_8=4.3.
By upper, with this type of heap, statistics show that four fault levels of bearing inner race are distinguished wear particle rate of increase, bearing temperature and gear case oil temperature range in corresponding fluid respectively.
In like manner, then counting under bearing outer ring fault, bearing roller fault and retainer failure condition respectively, level Four fault level is distinguished each fault parameter scope of correspondence.
Step S20: for without fault type, set up fault level matrix respectively.
As it is shown on figure 3, illustrate as a example by bearing inner race fault.
During fault-free level, i.e. F_Level1, the parameter interval of fault characteristic frequency vibration amplitude Vi, wear particle rate of increase IN_Ri, oil liquid temperature value T_Oi and bearing temperature value T_Bi correspondence respectively is: [Vi_1, Vi_2], [IN_Ri_1, IN_Ri_2], [T_Oi_1, T_Oi_2] and [T_Bi_1, T_Bi_2]；
During minor failure level, i.e. F_Level2, four fault parameter corresponding intervals respectively are: [Vi_3, Vi_4], [IN_Ri_3, IN_Ri_4], [T_Oi_3, T_Oi_4] and [T_Bi_3, T_Bi_4]；
During generic failure level, i.e. F_Level3, four fault parameter corresponding intervals respectively are: [Vi_5, Vi_6], [IN_Ri_5, IN_Ri_6], [T_Oi_5, T_Oi_6] and [T_Bi_5, T_Bi_6]；
And during catastrophe failure level, i.e. F_Level4, four fault parameter corresponding intervals respectively are: [Vi_7, Vi_8], [IN_Ri_7, IN_Ri_8], [T_Oi_7, T_Oi_8] and [T_Bi_7, T_Bi_8].
In like manner, more respectively fault level matrix when bearing outer ring fault, bearing roller fault and retainer fault is set up.The difference showed due to different fault types, therefore, although four fault types all use parameter above to set up ranking matrix, but each interval value in the matrix of each fault type is different.
Step S30: determine fault type, and determined the fault level under this fault type according to gathering each fault parameter.
Concrete, step S30 includes:
Step S301: determine and i.e. determine in fault type, this example which part of bearing inner race, bearing outer ring, bearing roller and retainer four part breaks down.
The vibration performance value gathered is resolved by Hilbert (Hilbert) conversion, asks for the envelope signal of analytic signal, envelope signal carries out fast Fourier transform (FFT), to obtain envelope spectrum figure.By the analysis of envelope spectrogram i.e. be can determine that fault type.Such as when bearing inner race position exists fault, envelope spectrogram there will be higher magnitude at 5～6 times turns of frequent rates；When bearing outer ring position exists fault, envelope spectrogram there will be higher magnitude at 3～4 times turns of frequent rates.Above-mentioned determine that the concrete steps of fault type are same as the prior art, therefore repeat no more.
Step S302: on the basis of determining fault type, determines the fault level under this fault type.
In the present embodiment, use the probabilistic algorithm formula that following fault level occurs
Pij=(Vi_Factor*W1_V*W2_V+IN_Ri_Factor*W1_IN_Ri*W2_IN_Ri+TBi_F actor*W1_TB*W2_TB+TOi_Factor*W1_TO*W2_TO)/(W1_V*W2_V+W1_IN_Ri*W2_IN_Ri+W1_TB*W2_TB+W1_TO*W2_TO), j=1,2,3,4, calculate the probability that different faults grade occurs respectively.
In formula, PijRepresent under fault type of the same race, the probability that different faults grade is occurred, Pi1Represent trouble-free probability, Pi2Represent the probability of minor failure level, Pi3Represent the probability of generic failure level, Pi4Represent the probability of catastrophe failure level.
Vi_Factor (vibration amplitude), IN_Ri_Factor (wear particle rate of increase), TBi_Factor (bearing temperature) and TOi_Factor (gear case oil temperature) represent under certain fault level respectively, the calculating factor of Real-time Collection fault parameter, described calculating factor value 1 or 0.Illustrate, when calculating the probability that fault-free level occurs, the matrix set up according to step S20, if Real-time Collection fault parameter is respectively at [Vi_1, Vi_2], [IN_Ri_1, IN_Ri_2], [T_Oi_1, T_Oi_2] and [T_Bi_1, T_Bi_2] in the range of, then corresponding, each calculates factor value 1, otherwise, if which Real-time Collection fault parameter is not in above-mentioned scope, then the calculating factor value 0 of which correspondence.
W1_V, W1_IN_Ri, W1_TB and W1_TO represent each collection data inactivity weight that statistical computation goes out respectively.
Owing to fault characteristic frequency vibration amplitude is the Main Basis judging gearbox of wind turbine fault, therefore, the weighted value W1_V=1 of fault characteristic frequency vibration amplitude is set；
Cross correlation algorithm is used to calculate other three fault parameters weight compared to W1_V, static weight W1_ of wear particle rate of increase
The static weight of bearing temperature In formulaRepresent the meansigma methods of bearing temperature；
The static weight of gear case oil temperature
W2_V, W2_IN_Ri, W2_TB and W2_TO represent each collection data changeable weight that statistical computation goes out respectively.
Above-mentioned four class fault parameters are affected by varying environment, and such as fault characteristic frequency vibration amplitude is along with the change of power of fan P；Wear particle rate of increase runs time change with gear case of blower, and bearing temperature and gear case oil temperature are with variation of ambient temperature.Thus, four class fault parameters, under different ambient parameters, exist and dynamically change weight to what fault degree positioned.
The changeable weight W2_V=1-Pi/P_rate of fault characteristic frequency vibration amplitude, in formula, P_rate is blower fan rated power, and Pi is blower fan realtime power.Wherein, when the realtime power Pi of blower fan is the biggest, represent that it is the strongest for the impact of fault characteristic frequency vibration amplitude, accordingly, when determining fault type by fault characteristic frequency vibration amplitude, its precision change can decline, based on this, use above-mentioned formula, when the realtime power Pi of blower fan is the biggest, for reducing its impact for fault characteristic frequency vibration amplitude, therefore make its changeable weight value the least.
The changeable weight W2_IN_Ri=1-DAYSi/DAYS_Design of wear particle rate of increase, in formula, DAYS_Design is the total natural law of equipment design and operation, the natural law that DAYSi is currently run by equipment.Wherein, when equipment institute, days running is the most, and its degree of aging is the highest, therefore, it is possible to occur, even if when fault-free, wear particle rate of increase also can rise.Based on this, when setting the changeable weight of wear particle rate of increase, reduce due to the time impact on it.
The changeable weight W2_TB of bearing temperature is identical, i.e. with the changeable weight W2_TO of gear case oil temperature T_amb in formulaiFor actual measurement ambient temperature, T_ambmaxFor the maximum of ambient temperature, T_ambminMinima for ambient temperature.Owing to difference is in season, different for the impact in equipment cabin.Due to summer high temperature, being therefore affected by, bearing temperature and gear case oil temperature can increase, it is clear that above-mentioned high temperature non-faulting cause, but ambient temperature causes.Thus, use above-mentioned formula just can effectively reduce the bearing temperature caused by the rising of ambient temperature and gear-box oil temperature rises.
By upper, calculating the probability occurred in different faults grade respectively, i.e. calculate the probability that fault level 1-4 each occurs, probability height person is broken down grade.Use said method, based on metallic particles, bearing temperature and the diagnostic method of four fault parameter convergence analysis of gear-box oil temperature in bear vibration, gear-box fluid, it is greatly improved the accuracy that gearbox of wind turbine fault level judges, avoid the blindness of single failure parameter location fault, reduce rate of false alarm.
Step S303: fault type and fault level judged result are displayed to the user that, so that user learns type and the fault level of current failure.
The foregoing is only presently preferred embodiments of the present invention, not in order to limit the present invention, such as, although above-mentioned as a example by four class fault types, as a example by each fault type uses four fault parameters, this is because, here fault is the fault that most probable occurs, these fault parameters are the parameters the closest with these faults, and unrestricted only for four class fault types, four class fault parameters, and four above-mentioned fault levels.The most all within the spirit and principles in the present invention, any modification, equivalent substitution and improvement etc. made, should be included within the scope of the present invention.
Claims (7)
1. improve the method that gearbox of wind turbine fault level judges precision, its feature
It is, including step:
A, determine fault type according to box bearing position vibration signal characteristics value；
B, determine each ginseng the currently monitored that each monitoring object of gearbox of wind turbine is corresponding
Number distribution situation in the fault level matrix of this fault type；
Described fault level matrix includes each the fault level set, each fault level bag
Include the parameters that under this fault level, each monitoring object of gearbox of wind turbine is corresponding to take
Value scope；
C, calculate the probability that each fault level occurs according to described distribution situation, and the most true
Described fault level before settled.
Method the most according to claim 1, it is characterised in that determine described in step A
Fault type includes: determine according to the envelope signal that box bearing position vibration signal is corresponding
Fault type is bearing inner race, bearing outer ring, bearing roller or retainer.
Method the most according to claim 1, it is characterised in that fault described in step B
Ranking matrix is arranged previously according to statistics, each monitoring object pair of described gearbox of wind turbine
The parameters scope answered includes:
The fault characteristic frequency vibration amplitude [Vi_x, Vi_y] at box bearing position, gear-box
Wear particle rate of increase [IN_Ri_x, IN_Ri_y] in fluid, gear case oil temperature [T_Oi_x,
T_Oi_y], the parameters scope of bearing temperature [T_Bi_x, T_Bi_y]；
Described fault level matrix is:
In matrix, F_Leveln represents fault level, and n is positive integer.
Method the most according to claim 3, it is characterised in that different monitoring objects pair
The parameter answered is provided with different weights, and described weight includes statically and/or dynamically weight；
When calculating, according to described distribution situation, the probability that each fault level occurs described in step C,
Herein in connection with described weight calculation.
Method the most according to claim 4, it is characterised in that described static weight bag
Include at least one:
The static weight of the fault characteristic frequency vibration amplitude at box bearing position is 1；
Respectively according to wear particle rate of increase, gear case oil temperature, bearing temperature in gear-box fluid
Degree and the degree of correlation of fault characteristic frequency vibration amplitude, calculate wear particle in gear-box fluid
Rate of increase, gear case oil temperature, the static weight of bearing temperature three.
Method the most according to claim 4, it is characterised in that described changeable weight bag
Include at least one:
Box bearing position is determined according to Wind turbines realtime power and rated power ratio
The changeable weight of fault characteristic frequency vibration amplitude；
Gear case oil is determined according to the ratio of the natural law currently run with the total natural law of design and operation
The changeable weight of wear particle rate of increase in liquid；
FoundationDetermine gear case oil temperature and axle
Holding the changeable weight of temperature, in formula, W2_TO is the changeable weight of gear case oil temperature,
W2_TB is the changeable weight of bearing temperature, T_ambiFor actual measurement ambient temperature, T_ambmaxFor
The maximum of ambient temperature, T_ambminMinima for ambient temperature.
Method the most according to claim 6, it is characterised in that basis described in step C
When described distribution situation calculates the probability that each fault level occurs, corresponding under each fault level
Described probability use following manner calculate:
P=(Vi_Factor*W1_V*W2_V+IN_Ri_Factor*W1_IN_Ri*W2_IN_Ri
+TBi_Factor*W1_TB*W2_TB+TOi_Factor*W1_TO*W2_TO)/(W1_V*
W2_V+W1_IN_Ri*W2_IN_Ri+W1_TB*W2_TB+W1_TO*W2_TO)；
In formula, W1_V, W1_IN_Ri, W1_TB and W1_TO represent gearbox shaft respectively
Wear particle rate of increase, tooth in the fault characteristic frequency vibration amplitude of bearing portion position, gear-box fluid
Roller box oil temperature and the static weight of bearing temperature；
In formula, W2_V, W2_IN_Ri, W2_TB and W2_TO represent gearbox shaft respectively
Wear particle rate of increase, tooth in the fault characteristic frequency vibration amplitude of bearing portion position, gear-box fluid
Roller box oil temperature and the changeable weight of bearing temperature；
Vi_Factor, IN_Ri_Factor, TBi_Factor and TOi_Factor table respectively in formula
Showing the calculating factor under current failure grade, the currently monitored parameter corresponding when monitoring object exists
In the range of parameter value corresponding under this fault level, calculating factor value is 1, otherwise value
It is 0.
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