CN103439109A - Wind turbine generator set drive system fault early-warning method - Google Patents

Abstract

The invention relates to the technical field of wind power generation, in particular to a wind turbine generator set drive system fault early-warning method. A temperature signal and an acceleration magnitude of key parts of the wind turbine generator set drive system are utilized to monitor the drive system of the wind turbine generator set. The method includes the steps of firstly acquiring practical temperature values T(t) of all measurement points and the vibration acceleration value a(t) of the drive system, then calculating the kurtosis index value of the vibration acceleration and the average temperature value of the wind turbine generator set, comparing the obtained parameters with the preset threshold values, giving an alarm when the parameter exceeds the threshold value, and determining the health condition of the wind turbine generator set drive system by comparing the temperature and the kurtosis index value with the threshold values. The method can improve the fault early-warning accuracy rate, has promotion effects on shutdown protection and fault early-warning of the wind turbine generator set and can reduce shutdown time caused by fault judgment.

Description

The method of a kind of wind power generating set driving unit fault early warning

Technical field

The invention particularly relates to the method for a kind of wind power generating set driving unit fault early warning, relate to technical field of wind power generation.

Background technology

The energy is the important substance basis that human society is depended on for existence and development progressive.Due to comprehensive anxiety of fossil energy, and environmental pollution increasingly sharpens, and the challenge that All Countries all faces is how under the prerequisite that does not weaken economy and social development, to take action to be transitioned into the energy system of safer a, low-carbon (LC).Therefore to the development and utilization of regenerative resource, become one of energy development strategy of countries in the world.Wind energy is clean energy resource with fastest developing speed in regenerative resource, and wind-power electricity generation is also the generation mode that has most large-scale development and commercialized development prospect, so the development and utilization of wind energy occupies critical role in country's whole energy strategy in future.

Wind power generating set running environment is severe, and failure rate is high, and the wind-electricity integration outstanding problem; therefore in limited working time; can ensure effectively exerts oneself is very necessary, and early warning promptly and accurately and fault judgement can be shortened stop time, so fault pre-alarming just seems very important.

Existing wind field is all to carry out the every half a year of planned repair and maintenance once, and fault diagnosis mode instantly is mainly data acquisition and supervisor control (the Supervisory Control And Data Acquisition that depends on wind energy turbine set master Control Room, SCADA) system, wind field is become to ten, remote monitoring is concentrated in the operation of up to a hundred units, to unit part operational factor (as temperature, wind speed, output power, single-point or two point vibration etc.) carry out the collection storage at long period interval, can take full advantage of these information, pass through temperature, the parameters such as current signal change the fault pre-alarming of realizing this Mechatronic Systems of wind energy conversion system, and the operational reliability of complete machine to be assessed. the assessment of these integrated informations often needs wind field staff's experience, therefore lack of wisdom and accuracy aspect the fault pre-alarming of blower fan.

The method that the current temperature by monitoring wind power generating set kinematic train part is monitored the operation conditions of wind power generating set is widely adopted, but in the practical application of wind field, and list of references: Guo Peng, David Infield, Yang Xiyun. " wind-driven generator group wheel box temperature trend status monitoring and analytical approach " [J]. Proceedings of the CSEE .2011 (31): shown in 129-136, likely the unit continuous operation time is long causes in the early warning of excess Temperature, not necessarily due to the generation of fault, produces.Therefore the technician of wind field can not do the judgement of being out of order and occurring in time.

Summary of the invention

The present invention is directed to current wind power generating set fault alarm not accurate enough, the wind power generating set kinematic train is tested, proposed the method for a kind of wind power generating set driving unit fault early warning.

The method of a kind of wind power generating set driving unit fault early warning, the method comprises the following steps:

Step 1: use acceleration transducer; level and vertical vibration to main shaft bearing, gearbox input shaft bearing, gear case planet wheel side, gear case intermediate shaft side, high speed shaft of gearbox side, gearbox high-speed axle bearing, generator front bearing and this eight place of generator rear bearing are measured, and obtain the acceleration signal a (t) of level and vertical vibration;

The serviceability temperature sensor, measured the temperature signal of main shaft bearing, gearbox input shaft bearing, gearbox output shaft bearing, gear case fluid, generator front bearing, this six places measuring point of generator rear bearing, obtains temperature signal T (t);

Step 2: the level that collects and the acceleration signal a (t) of vertical vibration are carried out to the magnitude extraction of acceleration sequentially successively, extract altogether 10 times, extract N point at every turn, be designated as: A={a ₁, a ₂a _n; Wherein, N is setting value, and N>=4096; Calculate respectively the kurtosis desired value K of each acceleration signal according to formula (1), and the kurtosis desired value K to 10 times averages by formula (2)

$K=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{\left(\frac{a_i-\stackrel{\‾}{a}}{\mathrm{\σ}}\right)}^4---\left(1\right)$

Wherein, $\stackrel{\‾}{a}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{a}_i,{\mathrm{\σ}}^2=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{(a_i-\stackrel{\‾}{a})}^2,i=\mathrm{1,2,3},......,N;$

Wherein, a _ibe the amplitude of i acceleration, for the average of the amplitude of acceleration, the standard deviation that σ is accekeration;

$\stackrel{\&OverBar;}{K}=\frac{K_1+K_2......{\mathrm{<figure-callout\; id="10"\; label="K"\; filenames="HDA0000381254950000012.png,HDA0000381254950000021.png"\; state="\{\{state\}\}">K</figure-callout>}}_{10}}{10}---\left(2\right)$

Step 3: carry out successively sequentially the extraction of temperature data value to collecting temperature signal T (t), once extract M temperature value, be designated as T={T ₁, T ₂, T ₃..., T _m; Wherein, M is setting value, M>=60; Calculate the mean value of M temperature value according to formula (3)

$\stackrel{\&OverBar;}{T}=\frac{1}{M}\underset{j=1}{\overset{M}{\mathrm{\&Sigma;}}}{T}_j---\left(3\right)$

Wherein, j=1,2,3 ..., M; T _jbe j temperature value;

Step 4: temperature pre-warning threshold values T is determined in the oil temperature restriction of the temperature value while turning round according to part in system and this lubricating oil used _f; According to the kurtosis index more serious principle of major break down more, set three kurtosis index early warning threshold values, be respectively K _f1, K _f2and K _f3;

Step 5: according to temperature pre-warning threshold values T _fwith three kurtosis index early warning threshold values K _f1, K _f2and K _f3, by the kinematic train Health Category division principle of wind power generating set, the health status of wind power generating set is divided into to eight grades;

Step 6: the mean value that will calculate the kurtosis desired value

mean value with temperature value with corresponding early warning threshold values compare, if existence surpasses early warning threshold values, this system alarm at first respectively; Finally, according to the kinematic train Health Category division principle of wind power generating set, judge the residing Health Category of this wind power generating set kinematic train, reach the early warning to the wind power generating set driving unit fault.

The kinematic train Health Category division principle of described wind power generating set is as follows:

As temperature t<T _f, and the kurtosis desired value

in interval [0, the K of threshold values _f1] time, being defined as " grade 1 " of the kinematic train Health Category of wind power generating set, the wind power generating set kinematic train running status in this grade is normal, and does not have fault to produce;

As temperature t<T _f, and the kurtosis desired value

in the interval (K of threshold values _f1, K _f2] time, the kinematic train Health Category of definition wind power generating set is " grade 2 ", the wind power generating set kinematic train running status in this grade is abnormal, and has minor failure to occur;

As temperature t<T _f, and the kurtosis desired value

in the interval (K of threshold values _f2, K _f3] time, the kinematic train Health Category of definition wind power generating set is " grade 3 ", the wind power generating set kinematic train running status in this grade is abnormal, and has the moderate fault to produce;

As temperature t<T _f, and the kurtosis desired value in the interval (K of threshold values _f3,+∞] time, the kinematic train Health Category of definition wind power generating set is " class 4 ", the wind power generating set kinematic train running status in this grade is abnormal, and catastrophic failure is arranged;

Work as temperature t>T _f, and the kurtosis desired value

in interval [0, the K of threshold values _f1] time, the kinematic train Health Category of definition wind power generating set is " class 5 ", the wind power generating set kinematic train running status in this grade is abnormal, but does not have fault to produce;

Work as temperature t>T _f, and the kurtosis desired value

in the interval (K of threshold values _f1, K _f2] time, the kinematic train Health Category of definition wind power generating set is " class 6 ", the wind power generating set kinematic train running status in this grade is abnormal, and has minor failure to produce;

Work as temperature t>T _f, and the kurtosis desired value

in the interval (K of threshold values _f2, K _f3] time, the kinematic train Health Category of definition wind power generating set is " grade 7 ", the wind power generating set kinematic train running status in this grade is abnormal, and the moderate fault is arranged;

Work as temperature t>T _f, and the kurtosis desired value

in the interval (K of threshold values _f3,+∞] time, the kinematic train Health Category of definition wind power generating set is " grade 8 ", the wind power generating set kinematic train running status in this grade is abnormal, and has catastrophic failure to produce.

Beneficial effect of the present invention: 1, the present invention has increased the monitoring parameter to the wind power generating set kinematic train, and according to the early warning threshold values of monitoring parameter, wind power generating set kinematic train health status is carried out to classification; 2, can improve the accuracy rate of fault pre-alarming by the method, to stoppage protection and the fault pre-alarming of wind power generating set, can play good facilitation effect, can reduce the stop time caused due to failure judgement; 3, the present invention is divided into 8 grades by the kinematic train Health Category of wind power generating set, can judge the out of order order of severity, improves the degree of accuracy of the health status of assessment wind power generating set, and to the wind power generating set kinematic train, rationally maintenance provides foundation; 4, the selected parameter of the present invention is convenient to calculate, and is suitable for on-line monitoring.

The accompanying drawing explanation

The fault early warning method calculation flow chart that Fig. 1 is wind power generating set provided by the invention;

The vibration acceleration signal time domain waveform figure to be measured that Fig. 2 is the specific embodiment of the invention, (a) and (b), (c) figure are respectively " a ", " b " and " c " number wind-driven generator group wheel box high speed shaft vibration acceleration signal a(t collected) " time domain waveform figure ";

" temperature signal-time " to be measured oscillogram that Fig. 3 is the specific embodiment of the invention, (a) and (b), (c) figure are respectively " a ", " b " and " c " number wind-driven generator group wheel box oil liquid temperature signal T(t collected) " temperature-time " figure.

Embodiment

Below in conjunction with accompanying drawing, embodiments of the present invention are described further:

The 1.5 megawatt wind driven generator group kinematic trains that domestic certain blower fan manufacturer is produced are carried out periodic monitoring, acceleration transducer and temperature sensor are installed by the measuring point requirement in the step 1 of claim 1, early warning system gathers the parameter of an aerogenerator kinematic train operation every 30min, the acceleration signal sample frequency is 32768Hz, and the sampling time is 1min; Temperature sensor measurement speed is the 850ms/ point, and the sampling time is 1min; Here three same wind field model wind power generating set of the same race kinematic trains are tested, this three typhoons power generator group is designated as respectively " a ", " b " and " c ".

In conjunction with Figure of description 1 process flow diagram, concrete implementation step is as described below:

The method of a kind of wind power generating set driving unit fault early warning, tested the wind power generating set kinematic train, and the method comprises the following steps:

Step 1: use acceleration transducer; level and vertical vibration to main shaft bearing, gearbox input shaft bearing, gear case planet wheel side, gear case intermediate shaft side, high speed shaft of gearbox side, gearbox high-speed axle bearing, generator front bearing and this eight place of generator rear bearing are measured; obtain the acceleration signal a (t) of level and vertical vibration, the time domain waveform figure of the vibration acceleration a (t) that Fig. 2 is 3 groups of wind-driven generator group wheel box high speed shaft measuring points collecting;

The serviceability temperature sensor, temperature signal to main shaft bearing, gearbox input shaft bearing, gearbox output shaft bearing, gear case fluid, generator front bearing, this six places measuring point of generator rear bearing is measured, obtain temperature signal T (t), " temperature-time " waveform of the temperature T (t) that Fig. 3 is 3 groups of wind-driven generator group wheel box fluid collecting;

Step 2: the level that collects in Fig. 2 and the acceleration signal a (t) of vertical vibration are carried out to the magnitude extraction of acceleration sequentially successively, extract altogether 10 times, extract N point at every turn, be designated as: A={a ₁, a ₂a _n; Wherein, N is setting value, and N>=4096;

Calculate respectively the kurtosis desired value K of each acceleration signal according to formula (1), and the kurtosis desired value K to 10 times averages by formula (2)

:

$K=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{\left(\frac{a_i-\stackrel{\&OverBar;}{a}}{\mathrm{\&sigma;}}\right)}^4---\left(1\right)$

Wherein, $\stackrel{\&OverBar;}{a}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{a}_i,{\mathrm{\&sigma;}}^2=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{(a_i-\stackrel{\&OverBar;}{a})}^2,i=\mathrm{1,2,3},......,N;$

Wherein, a _ibe the amplitude of i acceleration,

for the average of the amplitude of acceleration, the standard deviation that σ is accekeration;

$\stackrel{\&OverBar;}{K}=\frac{K_1+K_2......{\mathrm{<figure-callout\; id="10"\; label="K"\; filenames="HDA0000381254950000012.png,HDA0000381254950000021.png"\; state="\{\{state\}\}">K</figure-callout>}}_{10}}{10}---\left(2\right)$

Calculate respectively the kurtosis desired value of the acceleration signal of (a) and (b), (c) 3 groups of wind-driven generator group wheel box high speed shaft measuring points, by the kurtosis desired value K that calculates and the mean value of kurtosis desired value

count table 1;

Table 1 acceleration signal kurtosis desired value result of calculation

Step 3: carry out successively sequentially the extraction of temperature data value to collecting temperature signal T (t) in Fig. 3, once extract M temperature value, be designated as T={T ₁, T ₂, T ₃..., T _m; Wherein, M is setting value, M>=60; Calculate the mean value of M temperature value according to formula (3)

:

$\stackrel{\&OverBar;}{T}=\frac{1}{M}\underset{j=1}{\overset{M}{\mathrm{\&Sigma;}}}{T}_j---\left(3\right)$

Wherein, j=1,2,3 ..., M; T _jbe j temperature value;

Calculate respectively the mean value of (a) and (b), (c) 3 groups of wind-driven generator group wheel box oil liquid temperatures, by the mean value of the temperature value that calculates

count table 2;

The mean value calculation result of table 2 temperature signal

Step 4: the temperature threshold values of this model wind power generating set in example of the present invention is set to: gear case oil temperature 80 degree; Gearbox input shaft holds 85 degree; Gearbox output shaft holds 85 degree; Generator front bearing 100 degree; Rear bearing 100 degree; Main shaft bearing 50 degree; The early warning threshold values of each measuring point of kurtosis index is identical, and kurtosis index early warning threshold values is divided into to Three Estate, is respectively K _f1=2.0, K _f2=2.5 and K _f3=3.0;

Step 5: according to temperature pre-warning threshold values T _fwith three kurtosis index early warning threshold values K _f1, K _f2and K _f3, by the kinematic train Health Category division principle of wind power generating set, the health status of wind power generating set kinematic train is divided into to eight grades;

The kinematic train Health Category division principle of described wind power generating set is as follows:

As temperature t<T _f, and the kurtosis desired value in interval [0, the K of threshold values _f1] time, being defined as " grade 1 " of the kinematic train Health Category of wind power generating set, the wind power generating set kinematic train running status in this grade is normal, and does not have fault to produce;

As temperature t<T _f, and the kurtosis desired value

in the interval (K of threshold values _f1, K _f2] time, the kinematic train Health Category of definition wind power generating set is " grade 2 ", the wind power generating set kinematic train running status in this grade is abnormal, and has minor failure to occur;

As temperature t<T _f, and the kurtosis desired value

in the interval (K of threshold values _f2, K _f3] time, the kinematic train Health Category of definition wind power generating set is " grade 3 ", the wind power generating set kinematic train running status in this grade is abnormal, and has the moderate fault to produce;

As temperature t<T _f, and the kurtosis desired value in the interval (K of threshold values _f3,+∞] time, the kinematic train Health Category of definition wind power generating set is " class 4 ", the wind power generating set kinematic train running status in this grade is abnormal, and catastrophic failure is arranged;

Work as temperature t>T _f, and the kurtosis desired value

in interval [0, the K of threshold values _f1] time, the kinematic train Health Category of definition wind power generating set is " class 5 ", the wind power generating set kinematic train running status in this grade is abnormal, but does not have fault to produce;

Work as temperature t>T _f, and the kurtosis desired value

in the interval (K of threshold values _f1, K _f2] time, the kinematic train Health Category of definition wind power generating set is " class 6 ", the wind power generating set kinematic train running status in this grade is abnormal, and has minor failure to produce;

Work as temperature t>T _f, and the kurtosis desired value

in the interval (K of threshold values _f2, K _f3] time, the kinematic train Health Category of definition wind power generating set is " grade 7 ", the wind power generating set kinematic train running status in this grade is abnormal, and the moderate fault is arranged;

Work as temperature t>T _f, and the kurtosis desired value

in the interval (K of threshold values _f3,+∞] time, the kinematic train Health Category of definition wind power generating set is " grade 8 ", the wind power generating set kinematic train running status in this grade is abnormal, and has catastrophic failure to produce.

Step 6: the mean value that will calculate the kurtosis desired value mean value with temperature value

, with corresponding early warning threshold values, compare at first respectively, if existence surpasses early warning threshold values, this system alarm; Finally, according to the kinematic train Health Category division principle of wind power generating set, judge the residing Health Category of this wind power generating set kinematic train, reach the early warning to the wind power generating set driving unit fault.

The a wind power generating set, the mean value that calculates the kurtosis desired value be 1.6507 and the mean value of temperature value be 75.6, these two values compare with corresponding early warning threshold values respectively, the mean value 1.672 of finding the kurtosis desired value is less than kurtosis desired value early warning valve, the mean value 75.6 of temperature value is also low than temperature value early warning threshold values, does not send warning.Kinematic train Health Category division principle according to wind power generating set, the residing Health Category that judges this wind power generating set kinematic train is " grade 1 ", wind power generating set kinematic train running status in this grade is normal, and does not have fault to produce.

The b wind power generating set, the mean value that calculates the kurtosis desired value be 2.9919 and the mean value of temperature value be 83.8, these two values compare with corresponding early warning threshold values respectively, the mean value 2.9919 of finding the kurtosis desired value is larger than kurtosis desired value early warning valve, the mean value 83.8 of temperature value is higher than temperature value early warning threshold values, sends warning.According to the kinematic train Health Category division principle of wind power generating set, judge that the residing Health Category of this wind power generating set kinematic train is " grade 7 ", the wind power generating set kinematic train running status in this grade is abnormal, and the moderate fault is arranged;

The c wind power generating set, wind power generating set, the mean value that calculates the kurtosis desired value be 3.1746 and the mean value of temperature value be 76.4, these two values compare with corresponding early warning threshold values respectively, the mean value 3.1746 of finding the kurtosis desired value is larger than kurtosis desired value early warning valve, the mean value 76.4 of temperature value is lower than temperature value early warning threshold values, sends warning.According to the kinematic train Health Category division principle of wind power generating set, judge that the residing Health Category of this wind power generating set kinematic train is " class 4 ", the wind power generating set kinematic train running status in this grade is abnormal, and catastrophic failure is arranged;

By above, can be found out, method for early warning by two kinds of parameter monitorings to temperature t and kurtosis value K, can carry out early warning to fault and the excess Temperature occurred timely, improve the accuracy rate of fault judgement, and wind power generating set kinematic train health status is carried out to classification, provide reference proposition to next step repair and maintenance.

Claims (2)

1. the method for wind power generating set driving unit fault early warning, tested the wind power generating set kinematic train, it is characterized in that the method comprises the following steps:

Step 1: use acceleration transducer; level and vertical vibration to main shaft bearing, gearbox input shaft bearing, gear case planet wheel side, gear case intermediate shaft side, high speed shaft of gearbox side, gearbox high-speed axle bearing, generator front bearing and this eight place of generator rear bearing are measured, and obtain the acceleration signal a (t) of level and vertical vibration;

The serviceability temperature sensor, measured the temperature signal of main shaft bearing, gearbox input shaft bearing, gearbox output shaft bearing, gear case fluid, generator front bearing, this six places measuring point of generator rear bearing, obtains temperature signal T (t);

Step 2: the level that collects and the acceleration signal a (t) of vertical vibration are carried out to the magnitude extraction of acceleration sequentially successively, extract altogether 10 times, extract N point at every turn, be designated as: A={a ₁, a ₂a _n; Wherein, N is setting value, and N>=4096; Calculate respectively the kurtosis desired value K of each acceleration signal according to formula (1), and the kurtosis desired value K to 10 times averages by formula (2)

$K=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{\left(\frac{a_i-\stackrel{\&OverBar;}{a}}{\mathrm{\&sigma;}}\right)}^4---\left(1\right)$

Wherein, $\stackrel{\&OverBar;}{a}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{a}_i,{\mathrm{\&sigma;}}^2=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{(a_i-\stackrel{\&OverBar;}{a})}^2,i=\mathrm{1,2,3},......,N;$

Wherein, a _ibe the amplitude of i acceleration,

for the average of the amplitude of acceleration, the standard deviation that σ is accekeration;

$\stackrel{\&OverBar;}{K}=\frac{K_1+K_2......K_{10}}{10}---\left(2\right)$

Step 3: carry out successively sequentially the extraction of temperature data value to collecting temperature signal T (t), once extract M temperature value, be designated as T={T ₁, T ₂, T ₃..., T _m; Wherein, M is setting value, and M>=60; Calculate the mean value of M temperature value according to formula (3)

:

$\stackrel{\&OverBar;}{T}=\frac{1}{M}\underset{j=1}{\overset{M}{\mathrm{\&Sigma;}}}{T}_j---\left(3\right)$

Wherein, j=1,2,3 ..., M; T _jbe j temperature value;

Step 4: temperature pre-warning threshold values T is determined in the oil temperature restriction of the temperature value while running well according to part in system and this lubricating oil used _f; According to the kurtosis index more serious principle of major break down more, set three kurtosis index early warning threshold values, be respectively K _f1, K _f2and K _f3;

Step 5: according to temperature pre-warning threshold values T _fwith three kurtosis index early warning threshold values K _f1, K _f2and K _f3, by the kinematic train Health Category division principle of wind power generating set, the health status of wind power generating set is divided into to eight grades;

Step 6: the mean value that will calculate the kurtosis desired value

mean value with temperature value

, with corresponding early warning threshold values, compare at first respectively, if existence surpasses early warning threshold values, this system alarm; Finally, according to the kinematic train Health Category division principle of wind power generating set, judge the residing Health Category of this wind power generating set kinematic train, reach the early warning to the wind power generating set driving unit fault.

2. the method for a kind of wind power generating set driving unit fault early warning according to claim 1, is characterized in that, the kinematic train Health Category division principle of described wind power generating set is as follows:

As temperature t<T _f, and the kurtosis desired value

in interval [0, the K of threshold values _f1] time, being defined as " grade 1 " of the kinematic train Health Category of wind power generating set, the wind power generating set kinematic train running status in this grade is normal, and does not have fault to produce;

As temperature t<T _f, and the kurtosis desired value

in the interval (K of threshold values _f1, K _f2] time, the kinematic train Health Category of definition wind power generating set is " grade 2 ", the wind power generating set kinematic train running status in this grade is abnormal, and has minor failure to occur;

As temperature t<T _f, and the kurtosis desired value

in the interval (K of threshold values _f2, K _f3] time, the kinematic train Health Category of definition wind power generating set is " grade 3 ", the wind power generating set kinematic train running status in this grade is abnormal, and has the moderate fault to produce;

As temperature t<T _f, and the kurtosis desired value in the interval (K of threshold values _f3,+∞] time, the kinematic train Health Category of definition wind power generating set is " class 4 ", the wind power generating set kinematic train running status in this grade is abnormal, and catastrophic failure is arranged;

Work as temperature t>T _f, and the kurtosis desired value

in interval [0, the K of threshold values _f1] time, the kinematic train Health Category of definition wind power generating set is " class 5 ", the wind power generating set kinematic train running status in this grade is abnormal, but does not have fault to produce;

Work as temperature t>T _f, and the kurtosis desired value

in the interval (K of threshold values _f1, K _f2] time, the kinematic train Health Category of definition wind power generating set is " class 6 ", the wind power generating set kinematic train running status in this grade is abnormal, and has minor failure to produce;

Work as temperature t>T _f, and the kurtosis desired value

in the interval (K of threshold values _f2, K _f3] time, the kinematic train Health Category of definition wind power generating set is " grade 7 ", the wind power generating set kinematic train running status in this grade is abnormal, and the moderate fault is arranged;

Work as temperature t>T _f, and the kurtosis desired value

in the interval (K of threshold values _f3,+∞] time, the kinematic train Health Category of definition wind power generating set is " grade 8 ", the wind power generating set kinematic train running status in this grade is abnormal, and has catastrophic failure to produce.

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