State monitoring and maintenance decision support system for wind turbine generator

Abstract

The invention belongs to the technical field of wind turbine generator state monitoring, and discloses a wind turbine generator state monitoring and maintenance decision support system, which comprises the following steps: acquiring a temperature fluctuation index, a comprehensive vibration index, a comprehensive wear health index and a comprehensive environmental adaptability index; performing correlation analysis on the temperature fluctuation index, the comprehensive vibration index, the comprehensive wear health index and the comprehensive environmental adaptability index to obtain a comprehensive health index of the wind turbine generator; evaluating the current state of the wind turbine generator according to the comprehensive health index, judging whether the equipment is abnormal or not, and generating an equipment state signal; triggering an alarm mechanism according to the equipment state signal, and generating an abnormal evaluation report; while the state monitoring precision and sensitivity of the wind turbine generator are improved, the optimization of a maintenance strategy is realized, the environmental adaptability of the system is remarkably enhanced, the operation and maintenance efficiency, the equipment reliability and the economic benefit are improved, and technical support is provided for realizing intelligent operation and maintenance.

Description

Technical Field

[0001] The present invention relates to the technical field of wind turbine condition monitoring, and more specifically, to a wind turbine condition monitoring and maintenance decision support system. Background Art

[0002] With the continuous increase in wind power installed capacity, ensuring the safe and stable operation of wind turbines has become a critical issue. The working environment of wind turbines is complex and changeable, including temperature fluctuations, strong wind shocks, humidity changes, etc. under extreme climatic conditions, all of which may cause equipment performance degradation or even failure. Therefore, effective condition monitoring and maintenance decision support for wind turbines have become particularly critical.

[0003] At present, the condition monitoring of wind turbines mainly relies on regular inspections, sensor monitoring and predictive maintenance software, but regular inspections are inefficient and difficult to capture real-time changes; sensor monitoring only focuses on a single indicator and lacks comprehensive evaluation, real-time performance and sensitivity; predictive maintenance has problems such as insufficient data integration and poor environmental adaptability, which leads to inaccurate comprehensive evaluation, may increase unnecessary maintenance times, and has limited reliability in complex environments. Therefore, existing technologies are in urgent need of improvement in comprehensive evaluation, real-time response and environmental adaptability. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned purpose, the present invention provides the following technical solution: a wind turbine condition monitoring and maintenance decision support system, comprising:

[0005] Data construction module: used to collect temperature fluctuation index, comprehensive vibration index, comprehensive wear and health index and comprehensive environmental adaptability index;

[0006] Data analysis module: used to calculate the comprehensive health index of key components through temperature fluctuation index, comprehensive vibration index, comprehensive wear health index and comprehensive environmental adaptability index, and to perform correlation analysis on the comprehensive health index of each key component to obtain the comprehensive health index of the wind turbine;

[0007] Status assessment module: used to assess the current status of the wind turbine according to the comprehensive health index, determine whether the equipment is abnormal, and generate equipment status signals;

[0008] Abnormal alarm module: used to trigger the alarm mechanism according to the equipment status signal and generate abnormal assessment report.

[0009] Furthermore, the temperature fluctuation index is collected in the following manner:

[0010] In a monitoring cycle, the monitoring cycle is equally divided into n moments, and the temperature data of each key component in the monitoring cycle at each moment is collected using thermocouples and arranged in time sequence to obtain a time series array;

[0011] Among them, key components include the wind turbine's main shaft, bearings, generator, hydraulic brake assembly and gearbox;

[0012] Set a time window with a step size of wt. For any moment t on the time series array, calculate the average value in the time window before moment t, record it as the moving average temperature, then obtain the absolute difference between the moving average temperature and the temperature data at moment t, and record the absolute difference as the temperature fluctuation index at moment t.

[0013] Furthermore, the comprehensive vibration index collection method includes:

[0014] Step S31: taking the start time of the monitoring period as the initial time, using the accelerometer to collect the vibration signal data of each key component in real time, and preprocessing the vibration signal data between the initial time and time t, including using median filtering to remove high-frequency noise and normalization;

[0015] Step S32: using a high-pass filter and a low-pass filter to perform convolution processing on the preprocessed vibration signal data, and obtaining a first-level low-pass filtering result Yd1[n] and a first-level high-pass filtering result Yg1[n] respectively, the first-level low-pass filtering result Yd1[n] and the first-level high-pass filtering result Yg1[n] are the filtered vibration signal data, and still maintain the same length sequence as the preprocessed vibration signal data;

[0016] Where n is a discrete time index, representing the nth sampling point in the vibration signal data;

[0017] Step S33: sampling Yd1[n] and Yg1[n] respectively, including taking a sample from every other sampling point in Yd1[n] to obtain an approximate coefficient sequence Ad1, and taking a sample from every other sampling point in Yg1[n] to obtain a detail coefficient sequence Dg1;

[0018] Among them, a sample represents the data at a sampling point;

[0019] Step S34: Use a high-pass filter and a low-pass filter to perform convolution processing on the approximate coefficient sequence Ad1 to obtain a secondary high-pass filtering result Yg2[n] and a secondary low-pass filtering result Yd2[n];

[0020] Yd2[n] and Yg2[n] are processed using the same sampling method as step S33 to obtain an approximate coefficient sequence Ad2 and a detail coefficient sequence Dg2;

[0021] Step S35: Repeat the iteration step S34, use the high-pass filter and the low-pass filter to convolve and sample the approximate coefficient sequence Adh-1 of the previous level until the set number of times H is reached, and obtain the approximate coefficient sequence AdH and detail coefficient sequence DgH of each level, where h represents the level;

[0022] Step S36: For each level h, calculate the energy distribution of detail coefficients at each level Among them, Dg h [f] is the detail coefficient at the hth level, N h Indicates the number of detail coefficients at level h, E h represents the total energy at the hth level;

[0023] Take the mean of the total energy at all levels as the time-frequency domain feature of the key component at time t;

[0024] Step S37: Arranging the preprocessed vibration signal data in time sequence to form a vibration time sequence array;

[0025] The average of the absolute values ​​of all data in the vibration time series array is taken as the absolute average value, and the maximum absolute value in the vibration time series array is calculated by ratio with the absolute average value to obtain the vibration pulse index of the key component at time t;

[0026] Step S38: For each key component, the comprehensive vibration index CVI of the key component is created by using the time-frequency domain characteristics and the vibration pulse index: CVI=ln(WFD+1)+ln(TFF+1), where WFD represents the time-frequency domain characteristics and TFF represents the vibration pulse index.

[0027] Furthermore, the comprehensive wear health index is collected in the following ways:

[0028] Use magnetic probes to absorb metal chips in the oil of key components, and use magnetic induction sensors to measure the change in magnetic field on the magnetic probe at time t. The magnetic induction sensors output corresponding current signals, which are compared with the calibration curve to obtain the corresponding metal element concentration.

[0029] Among them, the standard curve is the relationship curve between the current signal and the magnetic field change established in the laboratory;

[0030] The calibration curve is drawn as follows:

[0031] Prepare a series of standard oil samples with known metal element concentrations, and place a magnetic probe into each standard oil sample to adsorb magnetic particles;

[0032] A magnetic induction sensor is used to record the magnetic field changes on the magnetic probe after each experiment and output the corresponding current signal;

[0033] Record the metal element concentration of each standard oil sample and its corresponding current signal, and draw a standard curve with the metal element concentration as the abscissa and the current signal as the ordinate;

[0034] The difference in metal element concentration between any time t and the previous time t-1 within the monitoring period is taken as the concentration difference, and arranged in time sequence to obtain a concentration difference array;

[0035] Using a time window with a step size of wt, for any time t on the concentration difference array, calculate the average value in the time window before time t as the wear metal content trend of the key component at time t;

[0036] The comprehensive wear health index OPLI is constructed by the wear metal content trend of key components = (WMCT) α ×(OHI) (1-α) , where WMCT represents the trend of wear metal content, OHI represents the oil health index of key components at time t, and α is a preset fixed proportional factor, α∈(0,1);

[0037] The collection methods of oil health indicators include:

[0038] Use a viscometer to measure the viscosity of the oil in each key component at time t, and take the ratio of the viscosity of the oil at time t to the viscosity of the oil at the initial time as the change in the viscosity of the oil in each key component;

[0039] The number of pollutants in the oil of each key component at time t is measured by a particle counter to obtain the oil contamination degree at time t, and the ratio of the oil contamination degree at time t to the oil contamination degree at the initial time is taken as the oil contamination degree change;

[0040] Use acid-base titration method to measure the acid value of the oil in each key component at time t, and take the ratio of the acid value of the oil at time t to the acid value of the new oil as the oil oxidation amount;

[0041] The oil health index of each key component at time t is obtained by multiplying the change in oil viscosity, oil contamination and oil oxidation.

[0042] Furthermore, the comprehensive environmental adaptability index is collected in the following ways:

[0043] Step S51: collecting environmental data of the environment where the wind turbine is located at each moment during the monitoring period through the meteorological station, including temperature, humidity, wind speed, particulate matter, salt spray, rainfall and air pressure, to form an original environmental data set;

[0044] Step S52: Standardize each environmental factor in the original environmental data set. ij , calculate its probability distribution Among them, i represents the i-th environmental factor, j is the index of the moment in the monitoring period, and n represents the number of moments in the monitoring period;

[0045] Step S53: Calculate the information entropy of each environmental factor according to the probability distribution of each environmental factor in, is the normalization coefficient;

[0046] Step S54: Calculate the weight of each environmental factor based on the information entropy of each environmental factor Where m is the number of environmental factors;

[0047] Step S55: According to each environmental factor and the corresponding weight, a weighted sum formula is used to calculate the comprehensive environmental adaptability index of the environment in which the wind turbine generator system is located at time t.

[0048] Furthermore, the method of calculating the health index of key components by using the temperature fluctuation index, the comprehensive vibration index, the comprehensive wear health index and the comprehensive environmental adaptability index includes:

[0049] Step S61: standardizing the temperature fluctuation index, comprehensive vibration index, comprehensive wear health index and comprehensive environmental adaptability index at each time t to form an index data set;

[0050] Step S62: For each indicator type, set an ideal optimal indicator value as a reference indicator;

[0051] For each indicator u at time t, calculate the absolute difference Δ between it and the reference indicator 0u (t)=|z u (t)-z0(t)|, where z u (t) is the value of the uth indicator at time t, z0(t) is the value of the reference indicator at time t;

[0052] According to the absolute difference of each indicator at time t, find the minimum value of the absolute difference min s Δ 0s (t) and maximum value max s Δ 0s (t);

[0053] Step S63: Calculate the correlation coefficient of each indicator at time t based on the minimum and maximum absolute differences Among them, γ 0u(t) represents the correlation coefficient of the u-th indicator at time t;

[0054] Take the mean correlation coefficient of all indicators at time t as the health index of each key component at time t.

[0055] Furthermore, the method of performing correlation analysis on the health index of each key component to obtain the comprehensive health index of the wind turbine generator system includes:

[0056] Step S71: taking every two key components as a key component pair, and obtaining the mechanical connection relationship between each key component pair;

[0057] For key component pairs that have no mechanical connection relationship, obtain their functional dependencies;

[0058] Step S72: constructing a graph structure, taking each key component as a node in the graph, and taking the mechanical connection or functional dependency between the key components as the edge between the nodes;

[0059] Step S73: taking the comprehensive health index of each key component at time t as the initial feature of the corresponding node;

[0060] For each node, calculate the average of the initial features of the node and all adjacent nodes as the initial new feature representation;

[0061] Step S74: For each node v, perform a linear transformation z according to the new feature representation v =QZ (q) ×XL v +pz (q) , among which, XL v For the new feature representation, QZ (q) represents the characteristic coefficient, q represents the current number of iterations, pz (q) represents the bias term;

[0062] According to the current linear transformation, the ReLU activation function is applied to obtain the new feature representation of the node. Where ReLU(z v ) represents the ReLU activation function;

[0063] Step S75: iterate step S4 until the difference between the new feature representation obtained in the current iteration and the new feature representation obtained in the previous iteration is less than a preset feature difference threshold, and then stop iterating;

[0064] Step S76: Based on the new feature representations of all nodes, the comprehensive health index of the wind turbine is calculated by weighted average method;

[0065] Characteristic coefficient QZ (q)and the bias term pz (q) The way to obtain is:

[0066] Obtain the historical health index and corresponding real comprehensive health index of each key component of the wind turbine;

[0067] Use Xavier method to separate QZ (q) and pz (q) Assign an initial value;

[0068] According to the training data set, the initial characteristic coefficients and bias terms are applied to calculate a temporary predicted comprehensive health index;

[0069] The mean square error is used as the loss function to evaluate the difference between the temporary predicted comprehensive health index and the true comprehensive health index. The gradient of the loss function relative to the feature coefficient and the bias term is calculated through the back propagation algorithm. According to the calculated gradient, the gradient descent method is used to update the feature coefficient and the bias term to minimize the loss function. This process is repeated until the value of the loss function no longer changes, and the optimal feature coefficient and bias term are obtained.

[0070] Furthermore, the method of evaluating the current state of the wind turbine according to the comprehensive health index, determining whether the equipment is abnormal, and generating the equipment state signal includes:

[0071] The equipment status signal includes the equipment normal signal and the equipment abnormal signal;

[0072] A normal operating threshold of the comprehensive health index is set. If the comprehensive health index is less than the normal operating threshold, the wind turbine is judged to be in a normal state and a normal equipment signal is generated;

[0073] If the comprehensive health index is greater than or equal to the normal operating threshold, the wind turbine is determined to be in an abnormal state and an equipment abnormality signal is generated.

[0074] Furthermore, the normal operation threshold of the comprehensive health index is set in the following manner:

[0075] According to the comprehensive health index CHI at time t-1 t-1 , calculate the reference value J t-1 =a×CHI t-1 +(1-a)×(H t-2 -J t-2 );

[0076] Where a represents the smoothing factor of the reference value, J t-2 It represents the reference value of the comprehensive health index at time t-2, and J0 is defined as the average value of the comprehensive health index of the wind turbine in the first h moments during the monitoring period. t-2 Represents CHI tThe rate of change at time t-2, H0 is defined as the estimated value of the comprehensive health index change rate of the wind turbine in the first h moments during the monitoring period CH I0 represents the comprehensive health index of the wind turbine at the first moment in the monitoring period, J t-1 Represents CHI t-1 The baseline value at time t-1;

[0077] According to CHI t-1 The baseline value at time t-1, calculate CHI t-1 The rate of change H at time t-1 t-1 =b×(J t-1 -J t-2 )+(1-b)×H t-2 , where b represents the smoothing factor of the rate of change;

[0078] Take J t-1 With H t-1 The sum of is taken as the normal operation threshold of the comprehensive health index of the wind turbine at time t.

[0079] Furthermore, the method of triggering the alarm mechanism according to the device status signal and generating an abnormality assessment report at the same time includes:

[0080] If an abnormal signal of the equipment is detected, the alarm mechanism is triggered, and the time of the alarm, CHI value, health index of each key component and the specific value of each indicator are recorded. Through the fault tree analysis method, the CHI value higher than the normal state threshold is taken as the top event, and the sub-events that cause the CHI rise are decomposed to identify the basic events that affect the CHI rise;

[0081] With time as the horizontal axis and the CHI value at each moment as the vertical axis, draw a CHI change trend graph;

[0082] The CHI change trend chart, the alarm time, the health index of each key component, the specific value of each indicator and the basic events are integrated into an abnormal assessment report, which is sent to the maintenance personnel in the form of an alarm notification.

[0083] The technical effects and advantages of a wind turbine condition monitoring and maintenance decision support system of the present invention are as follows:

[0084] The present invention collects high-frequency and high-quality data from different parts of the wind turbine (such as gearboxes, generators, etc.) and external environments (such as wind speed, temperature, etc.), and constructs important features that can characterize equipment status changes from various dimensions, including temperature fluctuation indicators, comprehensive vibration indicators, comprehensive wear health indicators, and comprehensive environmental adaptability indicators. These indicators are then correlated and analyzed to obtain a comprehensive health index (CHI) that can comprehensively reflect the overall health of the equipment. Different from the traditional single indicator monitoring method, this not only improves the accuracy and reliability of status assessment, but also makes early warning possible by integrating multi-source heterogeneous data; based on the historical data analysis of the comprehensive health index, the normal operating threshold of the CHI is dynamically updated to ensure that it adapts to the actual operating conditions of the equipment, which not only improves the accuracy of monitoring, but also can timely discover potential problems, guide specific maintenance and management actions, and ensure the safe and stable operation of the equipment; the present invention particularly emphasizes the monitoring and evaluation of environmental conditions, ensuring that reliable status monitoring services can be provided to users under any circumstances, overcoming the problem of insufficient reliability of the prior art in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] Figure 1 A schematic diagram of a wind turbine status monitoring and maintenance decision support system according to the present invention;

[0086] Figure 2 It is a schematic diagram of a method for wind turbine status monitoring and maintenance decision support according to the present invention. DETAILED DESCRIPTION

[0087] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0088] Embodiment 1

[0089] See also Figure 1 As shown, a wind turbine condition monitoring and maintenance decision support system described in this embodiment includes:

[0090] Data construction module: used to collect temperature fluctuation index, comprehensive vibration index, comprehensive wear and health index and comprehensive environmental adaptability index;

[0091] Data analysis module: used to calculate the comprehensive health index of key components through temperature fluctuation index, comprehensive vibration index, comprehensive wear health index and comprehensive environmental adaptability index, and to perform correlation analysis on the comprehensive health index of each key component to obtain the comprehensive health index of the wind turbine;

[0092] Status assessment module: used to assess the current status of the wind turbine according to the comprehensive health index, determine whether the equipment is abnormal, and generate equipment status signals;

[0093] Abnormal alarm module: used to trigger the alarm mechanism according to the equipment status signal and generate abnormal evaluation report;

[0094] The temperature fluctuation index is collected in the following ways:

[0095] In a monitoring cycle, the monitoring cycle is equally divided into n moments, and the temperature data of each key component in the monitoring cycle at each moment is collected using thermocouples, and arranged in time sequence to obtain a time series array;

[0096] Among them, key components include the wind turbine's main shaft, bearings, generator, hydraulic brake assembly and gearbox;

[0097] Set a time window with a step size of wt. For any time t on the time series array, calculate the average value in the time window before time t, record it as the moving average temperature, then obtain the absolute difference between the moving average temperature and the temperature data at time t, and record the absolute difference as the temperature fluctuation index at time t.

[0098] By setting a time window and calculating the moving average temperature and temperature fluctuation index of each key component at each moment, this method can dynamically and real-time monitor the temperature changes of key components of wind turbines. By using the time series temperature data collected by thermocouples, combined with the moving average and temperature fluctuation index, it can more accurately capture abnormal temperature fluctuations, warn of potential failures in advance, and optimize maintenance strategies. This method not only improves the ability to perceive the health status of equipment, but also supports intelligent maintenance decisions based on status, thereby effectively improving the reliability and safety of the system, reducing unplanned downtime, and extending the service life of key components.

[0099] The comprehensive vibration index collection method includes:

[0100] Step S31: taking the start time of the monitoring period as the initial time, using the accelerometer to collect the vibration signal data of each key component in real time, and preprocessing the vibration signal data between the initial time and time t, including using median filtering to remove high-frequency noise and normalization;

[0101] Step S32: using a high-pass filter and a low-pass filter to perform convolution processing on the preprocessed vibration signal data, and obtaining a first-level low-pass filtering result Yd1[n] and a first-level high-pass filtering result Yg1[n] respectively, the first-level low-pass filtering result Yd1[n] and the first-level high-pass filtering result Yg1[n] are the filtered vibration signal data, and still maintain the same length sequence as the preprocessed vibration signal data;

[0102] Where n is a discrete time index, representing the nth sampling point in the vibration signal data;

[0103] Step S33: sampling Yd1[n] and Yg1[n] respectively, including taking a sample from every other sampling point in Yd1[n] to obtain an approximate coefficient sequence Ad1, and taking a sample from every other sampling point in Yg1[n] to obtain a detail coefficient sequence Dg1 (for example, assuming that the length of the original vibration signal data is N, the lengths of Ad1 and Dg1 are both N / 2);

[0104] Among them, a sample represents the data at a sampling point;

[0105] Step S34: Use a high-pass filter and a low-pass filter to perform convolution processing on the approximate coefficient sequence Ad1 to obtain a secondary high-pass filtering result Yg2[n] and a secondary low-pass filtering result Yd2[n];

[0106] Yd2[n] and Yg2[n] are processed using the same sampling method as step S33 to obtain an approximate coefficient sequence Ad2 and a detail coefficient sequence Dg2;

[0107] Step S35: Repeat the iteration step S34, use the high-pass filter and the low-pass filter to convolve and sample the approximate coefficient sequence Adh-1 of the previous level until the set number of times H is reached, and the approximate coefficient sequence Ad of each level is obtained. H and detail coefficient sequence Dg H , where h represents the level;

[0108] Step S36: For each level h, calculate the energy distribution of detail coefficients at each level Among them, Dg h [f] is the detail coefficient at the hth level, N h Indicates the number of detail coefficients at level h, E h represents the total energy at the hth level;

[0109] Take the mean of the total energy at all levels as the time-frequency domain feature of the key component at time t;

[0110] Step S37: Arranging the preprocessed vibration signal data in time sequence to form a vibration time sequence array;

[0111] The average of the absolute values ​​of all data in the vibration time series array is taken as the absolute average value, and the maximum absolute value in the vibration time series array is calculated by ratio with the absolute average value to obtain the vibration pulse index of the key component at time t;

[0112] Step S38: For each key component, the comprehensive vibration index CVI of the key component is created by using the time-frequency domain characteristics and the vibration pulse index = ln(WFD+1)+ln(TFF+1), where WFD represents the time-frequency domain characteristics and TFF represents the vibration pulse index;

[0113] It should be noted that the comprehensive vibration index obtained by combining the vibration pulse index with the time-frequency domain characteristics can provide a more comprehensive and sensitive condition monitoring perspective. This approach combines the advantages of time-frequency domain and frequency domain analysis, and can provide a more robust and reliable condition monitoring solution in a complex and changing working environment.

[0114] The comprehensive wear health index is collected in the following ways:

[0115] Use magnetic probes to absorb metal chips in the oil of key components, and use magnetic induction sensors to measure the change in magnetic field on the magnetic probe at time t. The magnetic induction sensors output corresponding current signals, which are compared with the calibration curve to obtain the corresponding metal element concentration.

[0116] Among them, the standard curve is the relationship curve between the current signal and the magnetic field change established in the laboratory;

[0117] The calibration curve is drawn as follows:

[0118] Prepare a series of standard oil samples with known metal element concentrations, and place a magnetic probe into each standard oil sample to adsorb magnetic particles;

[0119] A magnetic induction sensor is used to record the magnetic field changes on the magnetic probe after each experiment and output the corresponding current signal;

[0120] Record the metal element concentration of each standard oil sample and its corresponding current signal, and draw a standard curve with the metal element concentration as the abscissa and the current signal as the ordinate;

[0121] The difference in metal element concentration between any time t and the previous time t-1 within the monitoring period is taken as the concentration difference, and arranged in time sequence to obtain a concentration difference array;

[0122] Using a time window with a step size of wt, for any time t on the concentration difference array, calculate the average value in the time window before time t as the wear metal content trend of the key component at time t;

[0123] The comprehensive wear health index OPLI is constructed by the wear metal content trend of key components = (WMCT) α ×(OHI) (1-α) , where WMCT represents the trend of wear metal content, OHI represents the oil health index of key components at time t, and α is a preset fixed proportional factor, α∈(0,1);

[0124] The method for obtaining the oil health index includes:

[0125] Use a viscometer to measure the viscosity of the oil in each key component at time t, and take the ratio of the viscosity of the oil at time t to the viscosity of the oil at the initial time as the change in the viscosity of the oil in each key component;

[0126] The number of pollutants in the oil of each key component at time t is measured by a particle counter to obtain the oil contamination degree at time t, and the ratio of the oil contamination degree at time t to the oil contamination degree at the initial time is taken as the oil contamination degree change;

[0127] Use acid-base titration method to measure the acid value of the oil in each key component at time t, and take the ratio of the acid value of the oil at time t to the acid value of the new oil as the oil oxidation amount;

[0128] The oil health index of each key component at time t is obtained by multiplying the change in oil viscosity, the change in oil contamination and the amount of oil oxidation.

[0129] Combining the wear metal content trend and the oil health index to calculate the comprehensive wear index can more accurately assess the wear condition of the equipment, achieve early fault warning, optimize maintenance strategies, extend the life of key components, and improve the overall reliability and safety of the system. This method not only provides a comprehensive and sensitive multi-dimensional assessment, but also reduces maintenance costs through data-driven decision-making, ensures that wind turbines are always in the best working condition, reduces unplanned downtime and prevents potential safety accidents;

[0130] The collection method of the comprehensive environmental adaptability index includes:

[0131] Step S51: collecting environmental data of the environment where the wind turbine is located at each moment during the monitoring period through the meteorological station, including temperature, humidity, wind speed, particulate matter, salt spray, rainfall and air pressure, to form an original environmental data set;

[0132] Step S52: Standardize each environmental factor in the original environmental data set.ij , calculate its probability distribution Among them, i represents the i-th environmental factor, j is the index of the moment in the monitoring period, and n represents the number of moments in the monitoring period;

[0133] Step S53: Calculate the information entropy of each environmental factor according to the probability distribution of each environmental factor in, is the normalization coefficient, ensuring that the value of information entropy is in the range of [0,1];

[0134] Step S54: Calculate the weight of each environmental factor based on the information entropy of each environmental factor Where m is the number of environmental factors, (1-e i ) represents the coefficient of variation of the i-th environmental factor;

[0135] Step S55: Based on each environmental factor and its corresponding weight, a weighted summation formula is used to calculate the comprehensive environmental adaptability index of the environment where the wind turbine is located at time t. Where, j = t;

[0136] The comprehensive environmental adaptability index of wind turbines calculated through the above steps can comprehensively and quantitatively evaluate the operational adaptability of wind turbines in complex and changeable environments. This method not only takes into account the influence of multiple environmental factors such as temperature, humidity, wind speed, etc., but also gives reasonable weights to each factor through standardization, probability distribution and information entropy calculation to ensure the scientificity and accuracy of the evaluation results. The comprehensive environmental adaptability index thus obtained can more accurately reflect the actual operating conditions of wind turbines, optimize operation and maintenance strategies, warn of potential risks in advance, improve system reliability and safety, and support intelligent decision-making, thereby effectively reducing maintenance costs and extending equipment life;

[0137] The method of calculating the health index of key components by using the temperature fluctuation index, the comprehensive vibration index, the comprehensive wear health index and the comprehensive environmental adaptability index includes:

[0138] Step S61: standardizing the temperature fluctuation index, comprehensive vibration index, comprehensive wear health index and comprehensive environmental adaptability index at each time t to form an index data set;

[0139] Step S62: For each indicator type, an ideal optimal indicator value is set (the setting of the ideal optimal indicator value is discussed by relevant industry personnel and formulated according to industry standards or specifications) as a reference indicator;

[0140] For each indicator u at time t, calculate the absolute difference Δ between it and the reference indicator 0u(t)=|z u (t)-z0(t)|, where z u (t) is the value of the uth indicator at time t, z0(t) is the value of the reference indicator at time t;

[0141] According to the absolute difference of each indicator at time t, find the minimum value of the absolute difference min s Δ 0s (t) and maximum value max s Δ 0s (t); that is, among the 4 absolute differences of the 4 indicators, find the largest and smallest absolute differences;

[0142] Step S63: Calculate the correlation coefficient of each indicator at time t based on the minimum and maximum absolute differences Among them, γ 0u (t) represents the correlation coefficient of the u-th indicator at time t;

[0143] Take the mean of the correlation coefficients of all indicators at time t as the health index of the key components of the wind turbine at time t;

[0144] The method of performing correlation analysis on the health index of each key component to obtain the comprehensive health index of the wind turbine generator system includes:

[0145] Step S71: taking every two key components as a key component pair, obtaining the mechanical connection relationship between each key component pair (referring to the situation where two key components are directly connected through a physical connection or a mechanical interface. Such a connection is usually an explicit, physical connection, which means that the action of one component will directly affect the state of another component. For example, a gearbox and a generator are usually directly connected through a shaft, so there is a mechanical connection relationship between them. If there is a problem with the gearbox, the generator may not operate normally);

[0146] For key component pairs that have no mechanical connection, obtain their functional dependencies (meaning that although two key components have no direct physical connection, they are functionally dependent on each other, that is, the normal operation of one component depends on the working state of another component, even if there is no direct mechanical connection between them. For example, for non-direct-drive wind turbines, the main shaft transmits the mechanical energy generated by the rotation of the blades to the gearbox. If there is a problem with the main shaft, it may cause the gearbox to be unable to normally receive and process the input torque, thereby affecting efficiency and life);

[0147] Step S72: construct a graph structure, take each key component as a node in the graph, and take the mechanical connection or functional dependency between the key components as the edge between the nodes (for example, there is a direct mechanical connection between the gearbox and the generator, which can be represented as an edge; there is an electrical connection between the generator and the inverter, which can also be represented as an edge. Assume that we have the following key components: node JD1: gearbox, node JD2: generator, node JD3: blade, node JD4: inverter, the connection relationship between these components can be represented as: edge (JD1, JD2): mechanical connection between the gearbox and the generator, edge (JD2, JD4): electrical connection between the generator and the inverter, edge (JD2, JD3): functional dependency between the generator and the blade);

[0148] Step S73: taking the comprehensive health index of each key component at time t as the initial feature of the corresponding node;

[0149] For each node, calculate the average of the initial features of the node and all adjacent nodes as the initial new feature representation;

[0150] Step S74: For each node v, perform a linear transformation z according to the new feature representation v =QZ (q) ×XL v +pz (q) , among which, XL v For the new feature representation, QZ (q) represents the characteristic coefficient, q represents the current number of iterations, pz (q) represents the bias term;

[0151] According to the current linear transformation, the ReLU activation function is applied to obtain the new feature representation of the node. Where ReLU(z v ) represents the ReLU activation function;

[0152] Step S75: iterate step S4 until the difference between the new feature representation obtained in the current iteration and the new feature representation obtained in the previous iteration is less than a preset feature difference threshold, and then stop iterating;

[0153] Step S76: Based on the new feature representations of all nodes, the comprehensive health index of the wind turbine is calculated by weighted average method;

[0154] Characteristic coefficient QZ (q) and the bias term pz (q) The way to obtain is:

[0155] Collect training data sets, including historical health indexes of key components of wind turbines and corresponding real comprehensive health indexes;

[0156] Use Xavier method to separate QZ (q) and pz (q) Assign an initial value;

[0157] According to the training data set, the initial characteristic coefficients and bias terms are applied to calculate a temporary predicted comprehensive health index;

[0158] Using mean square error as the loss function, evaluate the difference between the temporary predicted comprehensive health index and the real comprehensive health index, calculate the gradient of the loss function relative to the feature coefficient and the bias term through the back propagation algorithm, and use the gradient descent method to update the feature coefficient and the bias term according to the calculated gradient to minimize the loss function, repeat this process until the value of the loss function no longer changes, and obtain the optimal feature coefficient and bias term;

[0159] It should be noted that by combining multiple key indicators, the comprehensive health index provides a comprehensive perspective for equipment status assessment, which not only takes into account internal mechanical wear, but also the influence of external environmental conditions. It can more accurately reflect the overall health status of the equipment and avoid the one-sidedness that may be caused by a single indicator. This index supports real-time monitoring and early warning, can dynamically monitor the operating status of wind turbines, promptly discover and deal with potential problems, achieve early warning, and reduce the probability of sudden failures. Based on the historical data and trend analysis of the comprehensive health index, maintenance strategies can be optimized, scientific and reasonable predictive maintenance plans can be formulated, maintenance costs and downtime can be reduced, and equipment reliability and service life can be improved. In addition, the comprehensive health index helps to improve operational efficiency, ensure that wind turbines operate within a safe range through performance optimization and decision support, and reduce failure rates and downtime losses;

[0160] The method of evaluating the current state of the wind turbine according to the comprehensive health index, determining whether the equipment is abnormal, and generating the equipment state signal includes:

[0161] The equipment status signal includes the equipment normal signal and the equipment abnormal signal;

[0162] setting a normal operating threshold of the comprehensive health index, and comparing the comprehensive health index with the normal operating threshold;

[0163] If the comprehensive health index is less than the normal operating threshold, the wind turbine is judged to be in a normal state and a normal equipment signal is generated;

[0164] If the comprehensive health index is greater than or equal to the normal operation threshold, the wind turbine is judged to be in an abnormal state and an equipment abnormality signal is generated;

[0165] The method for setting the normal operation threshold of the comprehensive health index includes:

[0166] According to the comprehensive health index CHI at time t-1 t-1 , calculate the reference value J t-1 =a×CHI t-1 +(1-a)×(H t-2 -J t-2 );

[0167] Where a represents the smoothing factor of the reference value (between 0 and 1, usually 0.5), J t-2 It represents the reference value of the comprehensive health index at time t-2, and J0 is defined as the average value of the comprehensive health index of the wind turbine in the first h moments during the monitoring period. t-2 Represents CHI t The rate of change at time t-2, H0 is defined as the estimated rate of change of the comprehensive health index of the wind turbine in the first h moments during the monitoring period CHI0 represents the comprehensive health index of the wind turbine at the first moment in the monitoring period, t-1 Represents CHI t-1 The baseline value at time t-1;

[0168] According to CHI t-1 The baseline value at time t-1, calculate CHI t-1 The rate of change H at time t-1 t-1 =b×(J t-1 -J t-2 )+(1-b)×H t-2 , where b represents the smoothing factor of the rate of change (between 0 and 1, usually 0.5);

[0169] Take J t-1 With H t-1 The sum of is taken as the normal operation threshold of the comprehensive health index of the wind turbine at time t;

[0170] The method of triggering the alarm mechanism according to the device status signal and generating an abnormality assessment report at the same time includes:

[0171] When an abnormal signal of the equipment is detected, the alarm mechanism is automatically activated, and the time of the alarm, CHI value, health index of each key component and the specific value of each indicator are recorded. Through the fault tree analysis method, the CHI value higher than the normal state threshold is taken as the top event, and the sub-events that cause the CHI rise are decomposed to identify the basic events that affect the CHI rise;

[0172] With time as the horizontal axis and the CHI value at each moment as the vertical axis, draw a CHI change trend graph;

[0173] Integrate the CHI change trend chart with the alarm time, the health index of each key component, the specific value of each indicator, and basic events into an abnormal assessment report, and send it to maintenance personnel in the form of an alarm notification;

[0174] Provides an intuitive and easy-to-use visual operation panel to display relevant status information of wind turbines, making it easier for maintenance personnel to understand and operate.

[0175] In this embodiment, high-frequency and high-quality data are collected from different parts of the wind turbine (such as gearboxes, generators, etc.) and external environments (such as wind speed, temperature, etc.), and important features that can characterize equipment status changes from various dimensions are constructed, including temperature fluctuation indicators, comprehensive vibration indicators, comprehensive wear health indicators, and comprehensive environmental adaptability indicators. These indicators are then correlated and analyzed to obtain a comprehensive health index (CHI) that can comprehensively reflect the overall health of the equipment. Different from the traditional single indicator monitoring method, this not only improves the accuracy and reliability of status assessment, but also makes early warning possible by integrating multi-source heterogeneous data; based on the historical data analysis of the comprehensive health index, the normal operating threshold of the CHI is dynamically updated to ensure that it adapts to the actual operating conditions of the equipment, which not only improves the accuracy of monitoring, but also can timely discover potential problems, guide specific maintenance and management actions, and ensure the safe and stable operation of the equipment; the present invention particularly emphasizes the monitoring and evaluation of environmental conditions, ensuring that reliable status monitoring services can be provided to users under any circumstances, overcoming the problem of insufficient reliability of the prior art in complex environments.

[0176] Embodiment 2

[0177] See also Figure 2 As shown, the part not described in detail in this embodiment is described in Example 1, which provides a method for monitoring the state of a wind turbine and supporting maintenance decisions, including:

[0178] S1. Collect temperature fluctuation index, comprehensive vibration index, comprehensive wear health index and comprehensive environmental adaptability index;

[0179] S2, performing correlation analysis on the temperature fluctuation index, the comprehensive vibration index, the comprehensive wear health index and the comprehensive environmental adaptability index to obtain the comprehensive health index of the wind turbine;

[0180] S3. Evaluate the current status of the wind turbine according to the comprehensive health index, determine whether the equipment is abnormal, and generate an equipment status signal;

[0181] S4. According to the equipment status signal, the alarm mechanism is triggered and an abnormal assessment report is generated.

[0182] Embodiment 3

[0183] This embodiment discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the operation mode of the above-mentioned method for wind turbine condition monitoring and maintenance decision support is implemented.

[0184] Since the electronic device introduced in this embodiment is an electronic device used to implement a method for wind turbine status monitoring and maintenance decision support in the embodiment of this application, based on the method for wind turbine status monitoring and maintenance decision support in the embodiment of this application, a person skilled in the art can understand the specific implementation of the electronic device of this embodiment and its various variations, so how the electronic device implements the method in the embodiment of this application is not described in detail here. As long as a person skilled in the art implements the electronic device used in the method for wind turbine status monitoring and maintenance decision support in the embodiment of this application, it belongs to the scope of protection of this application.

[0185] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0186] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technical users in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A wind turbine condition monitoring and maintenance decision support system, characterized in that: include: Data construction module: used to collect temperature fluctuation index, comprehensive vibration index, comprehensive wear and health index and comprehensive environmental adaptability index; Data analysis module: used to calculate the comprehensive health index of key components through temperature fluctuation index, comprehensive vibration index, comprehensive wear health index and comprehensive environmental adaptability index, and to perform correlation analysis on the comprehensive health index of each key component to obtain the comprehensive health index of the wind turbine; Status assessment module: used to assess the current status of the wind turbine according to the comprehensive health index, determine whether the equipment is abnormal, and generate equipment status signals; Abnormal alarm module: used to trigger the alarm mechanism according to the equipment status signal and generate abnormal assessment report.

2. A wind turbine condition monitoring and maintenance decision support system according to claim 1, characterized in that: The temperature fluctuation index is collected in the following ways: In a monitoring cycle, the monitoring cycle is equally divided into n moments, and the temperature data of each key component in the monitoring cycle at each moment is collected using thermocouples and arranged in time sequence to obtain a time series array; Among them, key components include the wind turbine's main shaft, bearings, generator, hydraulic brake assembly and gearbox; Set a time window with a step size of wt. For any moment t on the time series array, calculate the average value in the time window before moment t, record it as the moving average temperature, then obtain the absolute difference between the moving average temperature and the temperature data at moment t, and record the absolute difference as the temperature fluctuation index at moment t.

3. A wind turbine condition monitoring and maintenance decision support system according to claim 2, characterized in that: The comprehensive vibration index collection method includes: Step S31: taking the start time of the monitoring period as the initial time, using the accelerometer to collect the vibration signal data of each key component in real time, and preprocessing the vibration signal data between the initial time and time t, including using median filtering to remove high-frequency noise and normalization; Step S32: using a high-pass filter and a low-pass filter to perform convolution processing on the preprocessed vibration signal data, and obtaining a first-level low-pass filtering result Yd1[n] and a first-level high-pass filtering result Yg1[n] respectively, the first-level low-pass filtering result Yd1[n] and the first-level high-pass filtering result Yg1[n] are the filtered vibration signal data, and still maintain the same length sequence as the preprocessed vibration signal data; Where n is a discrete time index, representing the nth sampling point in the vibration signal data; Step S33: sampling Yd1[n] and Yg1[n] respectively, including taking a sample from every other sampling point in Yd1[n] to obtain an approximate coefficient sequence Ad1, and taking a sample from every other sampling point in Yg1[n] to obtain a detail coefficient sequence Dg1; Among them, a sample represents the data at a sampling point; Step S34: Use a high-pass filter and a low-pass filter to perform convolution processing on the approximate coefficient sequence Ad1 to obtain a secondary high-pass filtering result Yg2[n] and a secondary low-pass filtering result Yd2[n]; Yd2[n] and Yg2[n] are processed using the same sampling method as step S33 to obtain an approximate coefficient sequence Ad2 and a detail coefficient sequence Dg2; Step S35: Repeat the iteration step S34, use the high-pass filter and the low-pass filter to convolve and sample the approximate coefficient sequence Adh-1 of the previous level until the set number of times H is reached, and obtain the approximate coefficient sequence AdH and detail coefficient sequence DgH of each level, where h represents the level; Step S36: For each level h, calculate the energy distribution of detail coefficients at each level Among them, Dg h [f] is the detail coefficient at the hth level, N h Indicates the number of detail coefficients at level h, E h represents the total energy at the hth level; Take the mean of the total energy at all levels as the time-frequency domain feature of the key component at time t; Step S37: Arranging the preprocessed vibration signal data in time sequence to form a vibration time sequence array; The average of the absolute values ​​of all data in the vibration time series array is taken as the absolute average value, and the maximum absolute value in the vibration time series array is calculated by ratio with the absolute average value to obtain the vibration pulse index of the key component at time t; Step S38: For each key component, the comprehensive vibration index CVI of the key component is created by using the time-frequency domain characteristics and the vibration pulse index: CVI=ln(WFD+1)+ln(TFF+1), where WFD represents the time-frequency domain characteristics and TFF represents the vibration pulse index.

4. A wind turbine condition monitoring and maintenance decision support system according to claim 3, characterized in that: The comprehensive wear health index is collected in the following ways: Use magnetic probes to absorb metal chips in the oil of key components, and use magnetic induction sensors to measure the change in magnetic field on the magnetic probe at time t. The magnetic induction sensors output corresponding current signals, which are compared with the calibration curve to obtain the corresponding metal element concentration. Among them, the standard curve is the relationship curve between the current signal and the magnetic field change established in the laboratory; The calibration curve is drawn as follows: Prepare a series of standard oil samples with known metal element concentrations, and place a magnetic probe into each standard oil sample to adsorb magnetic particles; A magnetic induction sensor is used to record the magnetic field changes on the magnetic probe after each experiment and output the corresponding current signal; Record the metal element concentration of each standard oil sample and its corresponding current signal, and draw a standard curve with the metal element concentration as the abscissa and the current signal as the ordinate; The difference in metal element concentration between any time t and the previous time t-1 within the monitoring period is taken as the concentration difference, and arranged in time sequence to obtain a concentration difference array; Using a time window with a step size of wt, for any time t on the concentration difference array, calculate the average value in the time window before time t as the wear metal content trend of the key component at time t; The comprehensive wear health index OPLI is constructed by the wear metal content trend of key components = (WMCT) α ×(OHI) (1-α) , where WMCT represents the trend of wear metal content, OHI represents the oil health index of key components at time t, and α is a preset fixed proportional factor, α∈(0,1); The collection methods of oil health indicators include: Use a viscometer to measure the viscosity of the oil in each key component at time t, and take the ratio of the viscosity of the oil at time t to the viscosity of the oil at the initial time as the change in the viscosity of the oil in each key component; The number of pollutants in the oil of each key component at time t is measured by a particle counter to obtain the oil contamination degree at time t, and the ratio of the oil contamination degree at time t to the oil contamination degree at the initial time is taken as the oil contamination degree change; Use acid-base titration method to measure the acid value of the oil in each key component at time t, and take the ratio of the acid value of the oil at time t to the acid value of the new oil as the oil oxidation amount; The oil health index of each key component at time t is obtained by multiplying the change in oil viscosity, oil contamination and oil oxidation.

5. A wind turbine condition monitoring and maintenance decision support system according to claim 4, characterized in that: The collection method of the comprehensive environmental adaptability index includes: Step S51: collecting environmental data of the environment where the wind turbine is located at each moment during the monitoring period through the meteorological station, including temperature, humidity, wind speed, particulate matter, salt spray, rainfall and air pressure, to form an original environmental data set; Step S52: Standardize each environmental factor in the original environmental data set. ij , calculate its probability distribution Among them, i represents the i-th environmental factor, j is the index of the moment in the monitoring period, and n represents the number of moments in the monitoring period; Step S53: Calculate the information entropy of each environmental factor according to the probability distribution of each environmental factor in, is the normalization coefficient; Step S54: Calculate the weight of each environmental factor based on the information entropy of each environmental factor Where m is the number of environmental factors; Step S55: According to each environmental factor and the corresponding weight, a weighted sum formula is used to calculate the comprehensive environmental adaptability index of the environment in which the wind turbine generator system is located at time t.

6. A wind turbine condition monitoring and maintenance decision support system according to claim 5, characterized in that: The method of calculating the health index of key components by using the temperature fluctuation index, the comprehensive vibration index, the comprehensive wear health index and the comprehensive environmental adaptability index includes: Step S61: standardizing the temperature fluctuation index, comprehensive vibration index, comprehensive wear health index and comprehensive environmental adaptability index at each time t to form an index data set; Step S62: For each indicator type, set an ideal optimal indicator value as a reference indicator; For each indicator u at time t, calculate the absolute difference Δ between it and the reference indicator 0u (t)=|z u (t)-z0(t)|, where z u (t) is the value of the uth indicator at time t, z0(t) is the value of the reference indicator at time t; According to the absolute difference of each indicator at time t, find the minimum value of the absolute difference min s Δ 0s (t) and maximum value max s Δ 0s (t); Step S63: Calculate the correlation coefficient of each indicator at time t based on the minimum and maximum absolute differences Among them, γ 0u (t) represents the correlation coefficient of the u-th indicator at time t; Take the mean correlation coefficient of all indicators at time t as the health index of each key component at time t.

7. A wind turbine condition monitoring and maintenance decision support system according to claim 6, characterized in that: The method of performing correlation analysis on the health index of each key component to obtain the comprehensive health index of the wind turbine generator system includes: Step S71: taking every two key components as a key component pair, and obtaining the mechanical connection relationship between each key component pair; For key component pairs that have no mechanical connection relationship, obtain their functional dependencies; Step S72: constructing a graph structure, taking each key component as a node in the graph, and taking the mechanical connection or functional dependency between the key components as the edge between the nodes; Step S73: taking the comprehensive health index of each key component at time t as the initial feature of the corresponding node; For each node, calculate the average of the initial features of the node and all adjacent nodes as the initial new feature representation; Step S74: For each node v, perform a linear transformation z according to the new feature representation v =QZ (q) ×XL v +pz (q) , among which, XL v For the new feature representation, QZ (q) represents the characteristic coefficient, q represents the current number of iterations, pz (q) represents the bias term; According to the current linear transformation, the ReLU activation function is applied to obtain the new feature representation of the node. =ReLU(z v ), where ReLU(z v ) represents the ReLU activation function; Step S75: iterate step S4 until the difference between the new feature representation obtained in the current iteration and the new feature representation obtained in the previous iteration is less than a preset feature difference threshold, and then stop iterating; Step S76: Based on the new feature representations of all nodes, the comprehensive health index of the wind turbine is calculated by weighted average method; Characteristic coefficient QZ (q) and the bias term pz (q) The way to obtain is: Collect training data sets, including historical health indexes of key components of wind turbines and corresponding real comprehensive health indexes; Use Xavier method to separate QZ (q) and pz (q) Assign an initial value; According to the training data set, the initial characteristic coefficients and bias terms are applied to calculate a temporary predicted comprehensive health index; The mean square error is used as the loss function to evaluate the difference between the temporary predicted comprehensive health index and the true comprehensive health index. The gradient of the loss function relative to the feature coefficient and the bias term is calculated through the back propagation algorithm. According to the calculated gradient, the gradient descent method is used to update the feature coefficient and the bias term to minimize the loss function. This process is repeated until the value of the loss function no longer changes, and the optimal feature coefficient and bias term are obtained.

8. A wind turbine condition monitoring and maintenance decision support system according to claim 7, characterized in that: The method of evaluating the current state of the wind turbine according to the comprehensive health index, determining whether the equipment is abnormal, and generating the equipment state signal includes: The equipment status signal includes the equipment normal signal and the equipment abnormal signal; A normal operating threshold of the comprehensive health index is set. If the comprehensive health index is less than the normal operating threshold, the wind turbine is judged to be in a normal state and a normal equipment signal is generated. If the comprehensive health index is greater than or equal to the normal operating threshold, the wind turbine is determined to be in an abnormal state and an equipment abnormality signal is generated.

9. A wind turbine condition monitoring and maintenance decision support system according to claim 8, characterized in that: The method for setting the normal operation threshold of the comprehensive health index includes: According to the comprehensive health index CHI at time t-1 t-1 , calculate the reference value J t-1 =a×CHI t-1 +(1-a)×(H t-2 -J t-2 ); Where a represents the smoothing factor of the reference value, J t-2 It represents the reference value of the comprehensive health index at time t-2, and J0 is defined as the average value of the comprehensive health index of the wind turbine in the first h moments during the monitoring period. t-2 Represents CHI t The rate of change at time t-2, H0 is defined as the estimated rate of change of the comprehensive health index of the wind turbine in the first h moments during the monitoring period CHI0 represents the comprehensive health index of the wind turbine at the first moment in the monitoring period, t-1 Represents CHI t-1 The baseline value at time t-1; According to CHI t-1 The baseline value at time t-1, calculate CHI t-1 The rate of change H at time t-1 t-1 =b×(J t-1 -J t-2 )+(1-b)×H t-2 , where b represents the smoothing factor of the rate of change; Take J t-1 With H t-1 The sum of is taken as the normal operation threshold of the comprehensive health index of the wind turbine at time t.

10. A wind turbine condition monitoring and maintenance decision support system according to claim 9, characterized in that: The method of triggering the alarm mechanism according to the device status signal and generating an abnormality assessment report at the same time includes: If an abnormal signal of the equipment is detected, the alarm mechanism is triggered, and the time of the alarm, CHI value, health index of each key component and the specific value of each indicator are recorded. Through the fault tree analysis method, the CHI value higher than the normal state threshold is taken as the top event, and the sub-events that cause the CHI rise are decomposed to identify the basic events that affect the CHI rise; With time as the horizontal axis and the CHI value at each moment as the vertical axis, draw a CHI change trend graph; The CHI change trend chart, the alarm time, the health index of each key component, the specific value of each indicator and the basic events are integrated into an abnormal assessment report, which is sent to the maintenance personnel in the form of an alarm notification.

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