Oil product state determination method and device, storage medium and electronic device

Abstract

The invention discloses an oil product state determination method and device, a storage medium and an electronic device.The method comprises the steps that oil product data corresponding to preset oil product evaluation indexes are determined from lubricating oil inspection data of a wind generating set, the preset oil product evaluation indexes at least comprise a reference index and a comparison index, and the reference index and the comparison index are determined according to the oil product data; the oil product data at least comprise reference data used for representing the wear state of the lubricating oil container and comparison data used for representing the performance of the lubricating oil, and one group of reference data corresponds to different groups of comparison data; the deterioration degree of the lubricating oil is calculated according to the target correlation degree of the reference data and the comparison data, and the deterioration degree of the lubricating oil is determined according to the weighted sum value of different deterioration degrees; and determining the oil product state of the lubricating oil based on the deterioration degree. By adopting the technical scheme, the problem that the oil product state of the lubricating oil of the wind generating set is difficult to accurately determine is solved.

Description

Technical Field

[0001] The present application relates to the technical field of wind power generation, and in particular to a method and device for determining an oil status, a storage medium and an electronic device. Background Art

[0002] As a highly automated and efficient energy conversion system, the mechanical equipment inside the wind turbine, especially the friction parts of the transmission system, bears a huge load when working and needs to transmit huge torque. During the operation of the wind turbine, the gearbox is the core component, and its internal moving parts such as bearings and gears are in a state of frequent start-stop and high-load continuous operation. In order to reduce the wear of these parts, extend their service life, and then ensure the operating accuracy and stability, it is essential to add high-quality gearbox lubricating oil.

[0003] However, long-term operation and harsh working environment will cause the physical and chemical properties of the wind turbine gearbox lubricant to deteriorate. Specifically, the lubricant is prone to oxidation under high temperature and high pressure conditions, producing harmful substances, which in turn affects its lubrication performance. In addition, the accumulation of impurities and wear particles in the lubricant will also reduce its cleanliness, thereby aggravating the loss of gears and other components. If the lubricant is replaced in time because these problems are not monitored, it may lead to poor lubrication of the gearbox, or even cause serious mechanical failures, causing damage to the wind turbine.

[0004] Therefore, accurately assessing the oil condition of wind turbine gearbox lubricants and establishing an effective replacement mechanism is a key technology to ensure efficient and safe operation of wind turbines. At present, the industry mainly conducts oil health assessment through regular sampling analysis and online monitoring systems, but these methods often have problems such as low monitoring frequency, poor real-time performance, and high costs, making it difficult to accurately determine the oil condition of wind turbine lubricants.

[0005] With regard to the problem in the related art that it is difficult to accurately determine the oil condition of the lubricating oil of the wind turbine generator set, no effective solution has been proposed so far.

[0006] Therefore, it is necessary to improve the related technology to overcome the above-mentioned defects in the related technology. Summary of the invention

[0007] The embodiments of the present application provide a method and device for determining the oil status, a storage medium and an electronic device, so as to at least solve the problem that it is difficult to accurately determine the oil status of the lubricating oil of a wind turbine generator set.

[0008] According to one aspect of an embodiment of the present application, a method for determining an oil quality status is provided, comprising: determining oil quality data corresponding to a preset oil quality evaluation index from lubricating oil inspection data of a wind turbine generator set, wherein the preset oil quality evaluation index includes at least a reference index and a comparison index, and the oil quality data includes at least reference data for indicating the wear status of a lubricating oil container and comparison data for indicating lubricating oil performance, wherein a group of reference data corresponds to different groups of comparison data; calculating the degree of deterioration of the lubricating oil according to a target correlation between the reference data and the comparison data, and determining the degree of deterioration of the lubricating oil according to a weighted sum of different degrees of deterioration; and determining the oil quality status of the lubricating oil based on the degree of deterioration.

[0009] In an exemplary embodiment, before calculating the degree of degradation of the lubricant according to the target correlation between the reference data and the comparison data, the method further includes: respectively constructing a first data sequence corresponding to a set of reference data and a second data sequence corresponding to a set of comparison data, wherein the first data sequence represents reference data at different moments, and the second data sequence represents comparison data at different moments; calculating the correlation coefficient between the first data sequence and the second data sequence at each moment, and calculating the sequence correlation between the first data sequence and the second data sequence according to the correlation coefficient at each moment; and determining the target correlation based on multiple sequence correlations corresponding to the first data sequence and multiple second data sequences.

[0010] In an exemplary embodiment, before calculating the correlation coefficient between the first data sequence and the second data sequence at each moment, the method further includes: performing dimensionless processing on the first data sequence and the second data sequence, wherein the dimensionless processing includes: performing data scaling on the first data sequence and the second data sequence according to a scaling rule, wherein the values ​​of the first data sequence and the second data sequence after the data scaling belong to a scaling range corresponding to the scaling rule.

[0011] In an exemplary embodiment, the sequence correlation of the first data sequence and the second data sequence is calculated based on the correlation coefficients at the various moments, including: obtaining the average coefficient value of the correlation coefficients at the various moments, and determining the average coefficient value as the sequence correlation; and / or determining the target correlation based on multiple sequence correlations corresponding to the first data sequence and multiple second data sequences, including: for the multiple sequence correlations, sorting the multiple sequence correlations in order of size to obtain a correlation sequence, and determining the target correlation according to the correlations in the correlation sequence.

[0012] In an exemplary embodiment, the degradation degree of the lubricant is calculated based on the target correlation between the reference data and the comparison data, including: respectively determining the indicator types corresponding to the reference indicator and the comparison indicator, wherein the indicator type includes at least one of the following: a smaller-is-better indicator, a larger-is-better indicator, and an intermediate indicator; using the degradation degree formula corresponding to the indicator type to calculate the reference data to obtain a first degradation degree of the lubricant, and using the degradation degree formula corresponding to the indicator type to calculate the comparison data to obtain a second degradation degree of the lubricant, wherein the degradation degree formula includes at least one of the following: a smaller-is-better indicator degradation degree formula, a larger-is-better indicator degradation degree formula, and an intermediate indicator degradation degree formula.

[0013] In an exemplary embodiment, before determining the degradation degree of the lubricating oil according to the weighted sum of different degradation degrees, the method further includes: determining the weight of the degradation degree by the following formula:

[0014] i=1,2,…,m; where W i is the weight, γ i represents the relevance of the i-th target, represents the sum of all target associations corresponding to the different groups of comparison data, and m is a positive integer.

[0015] According to another aspect of an embodiment of the present application, a device for determining an oil quality status is also provided, comprising: a first determination module, used to determine oil quality data corresponding to a preset oil quality evaluation index from lubricating oil inspection data of a wind turbine generator set, wherein the preset oil quality evaluation index includes at least a reference index and a comparison index, and the oil quality data includes at least reference data for indicating the wear status of a lubricating oil container and comparison data for indicating the performance of the lubricating oil, wherein a group of reference data corresponds to different groups of comparison data; a calculation module, used to calculate the degree of deterioration of the lubricating oil according to a target correlation between the reference data and the comparison data, and determine the degree of deterioration of the lubricating oil according to a weighted sum of different degrees of deterioration; and a second determination module, used to determine the oil quality status of the lubricating oil based on the degree of deterioration.

[0016] According to another aspect of the embodiment of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned method for determining the oil status when running.

[0017] According to another aspect of an embodiment of the present application, an electronic device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the method for determining the oil status through the computer program.

[0018] According to another aspect of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0019] Through the present application, the oil product data corresponding to the preset oil product evaluation index is determined from the lubricating oil inspection data of the wind turbine generator set, wherein the preset oil product evaluation index includes at least a reference index and a comparison index, and the oil product data includes at least reference data for indicating the wear state of the lubricating oil container and comparison data for indicating the performance of the lubricating oil, wherein a group of reference data corresponds to different groups of comparison data; the degradation degree of the lubricating oil is calculated according to the target correlation degree of the reference data and the comparison data, and the degradation degree of the lubricating oil is determined according to the weighted sum value of different degradation degrees; the oil product state of the lubricating oil is determined based on the degradation degree. It can be seen that the present application overcomes the deficiency of evaluating the health of the oil product by a single index by combining multiple key indicators to comprehensively analyze the oil product state of the lubricating oil, can effectively and accurately evaluate the health status of the lubricating oil of the wind turbine generator set, has important reference significance for the selection and replacement of the lubricating oil of the wind turbine gearbox, and thus solves the problem of difficulty in accurately determining the oil product state of the lubricating oil of the wind turbine generator set, realizes accurate determination of the oil product state of the lubricating oil of the wind turbine generator set, and improves the technical effect of the operation and maintenance efficiency and safety of the wind turbine generator set. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0021] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0022] Figure 1 It is a hardware structure block diagram of a computer terminal of a method for determining oil product status according to an embodiment of the present application;

[0023] Figure 2 is a flow chart of a method for determining oil status according to an embodiment of the present application;

[0024] Figure 3 is a flow chart of a method for determining the state of an oil product according to an embodiment of the present application;

[0025] Figure 4 It is a structural block diagram of a device for determining oil status according to an embodiment of the present application. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device of a wind turbine generator set. Taking running on a computer terminal as an example, Figure 1 1 is a hardware structure block diagram of a computer terminal of a method for determining oil product status according to an embodiment of the present application. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor (Central Processing Unit, MCU) or a programmable logic device (Field Programmable Gate Array, FPGA)) and a memory 104 for storing data, wherein the above-mentioned computer terminal may also include a transmission device 106 and an input and output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0029] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the method for determining the oil product status in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0030] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0031] In this embodiment, a method for determining the state of an oil product is provided. Figure 2 is a flow chart of a method for determining the state of an oil product according to an embodiment of the present application, such as Figure 2 As shown, the process includes the following steps:

[0032] Step S202: determining oil product data corresponding to a preset oil product evaluation index from the lubricating oil inspection data of the wind turbine generator set, wherein the preset oil product evaluation index includes at least a reference index and a comparison index, and the oil product data includes at least reference data for indicating the wear state of the lubricating oil container and comparison data for indicating the performance of the lubricating oil, wherein a group of reference data corresponds to different groups of comparison data;

[0033] It should be noted that lubricating oil is generally added to the mechanical contact points of various transmission gears and bearings of wind turbines to form an oil film on the contact surface to reduce friction and wear and extend the service life of the machinery. It also plays the role of cooling, rust and corrosion prevention, cleaning, sealing and buffering.

[0034] It is understandable that this application selects key physical and chemical indicators that reflect the health of lubricants as parameters to characterize lubricant performance based on the needs of lubricant performance evaluation. For example, the change of iron element reflects the wear of the main gearbox, so the reference indicator is set as iron element, and the comparison indicators are set as kinematic viscosity, acid value, water element, copper element, silicon element, phosphorus element and other indicators.

[0035] Step S204: calculating the degradation degree of the lubricating oil according to the target correlation between the reference data and the comparison data, and determining the degradation degree of the lubricating oil according to the weighted sum of different degradation degrees;

[0036] Step S206: Determine the oil quality state of the lubricating oil based on the degradation degree.

[0037] It should be noted that the oil condition indicates the health condition of the lubricant. The weighted sum of different degradation degrees indicates the degradation value. The higher the degradation value, the higher the degree of degradation, and the worse the health condition of the oil. Conversely, the health condition is better.

[0038] Through the above steps, the oil product data corresponding to the preset oil product evaluation index is determined from the lubricating oil inspection data of the wind turbine generator set, wherein the preset oil product evaluation index includes at least a reference index and a comparison index, and the oil product data includes at least reference data for indicating the wear state of the lubricating oil container and comparison data for indicating the performance of the lubricating oil, wherein a group of reference data corresponds to different groups of comparison data; the degradation degree of the lubricating oil is calculated according to the target correlation degree of the reference data and the comparison data, and the degradation degree of the lubricating oil is determined according to the weighted sum value of different degradation degrees; the oil product state of the lubricating oil is determined based on the degradation degree. It can be seen that the present application overcomes the deficiency of evaluating the health of the oil product with a single index by combining multiple key indicators to comprehensively analyze the oil product state of the lubricating oil, can effectively and accurately evaluate the health status of the lubricating oil of the wind turbine generator set, has important reference significance for the selection and replacement of the lubricating oil of the wind turbine gearbox, and thus solves the problem of difficulty in accurately determining the oil product state of the lubricating oil of the wind turbine generator set, realizes accurate determination of the oil product state of the lubricating oil of the wind turbine generator set, and improves the technical effect of the operation and maintenance efficiency and safety of the wind turbine generator set.

[0039] In an exemplary embodiment, before calculating the degree of degradation of the lubricant according to the target correlation between the reference data and the comparative data, the following technical solution is further proposed: construct a first data sequence corresponding to a set of reference data and a second data sequence corresponding to a set of comparative data, respectively, wherein the first data sequence represents reference data at different moments, and the second data sequence represents comparative data at different moments; calculate the correlation coefficient of the first data sequence and the second data sequence at each moment, and calculate the sequence correlation of the first data sequence and the second data sequence according to the correlation coefficient at each moment; determine the target correlation based on multiple sequence correlations corresponding to the first data sequence and multiple second data sequences. After selecting key physical and chemical indicators that reflect the health status of the lubricant as indicators for characterizing the performance of the lubricant, the present application realizes the correlation between different indicators by constructing data sequences corresponding to the indicators.

[0040] In an exemplary embodiment, before calculating the correlation coefficient between the first data sequence and the second data sequence at each moment, the first data sequence and the second data sequence may also be dimensionally non-dimensionalized, wherein the dimensionally non-dimensionalized processing method includes: performing data scaling on the first data sequence and the second data sequence according to a scaling rule, wherein the values ​​of the first data sequence and the second data sequence after the data scaling belong to the scaling range corresponding to the scaling rule.

[0041] It should be noted that due to the different physical meanings of various factors in wind turbines, the dimensions of the data are not necessarily the same, which makes it difficult to compare or to draw correct conclusions. Therefore, the grey correlation analysis method can be used for data analysis. Grey correlation analysis is a method used to determine the degree of correlation between various factors in a system. When processing data, it usually requires dimensionless processing of the data to eliminate the influence of different dimensions so that the data can be compared.

[0042] The scaling rules may include, for example, a minimum-maximum normalization rule, a Z-score normalization rule, and a decimal scaling normalization rule, but are not limited thereto.

[0043] Optionally, the dimensionless processing process is described by combining the following steps:

[0044] Step 1. Determine the dataset.

[0045] This dataset contains the values ​​of the various factors that we want to analyze.

[0046] Step 2: Standardization.

[0047] Standardization is the process of converting data into a dimensionless form for comparison. Commonly used standardization methods are as follows:

[0048] Min-Max Scaling: Scale the data to a fixed range, usually 0 to 1.

[0049] Z-score standardization: Convert the data to a distribution with a mean of 0 and a standard deviation of 1.

[0050] Decimal scaling normalization: multiply the data by a power of 10 so that the absolute value of the data falls between 1 and 1.

[0051] Step 3. Handle zero values.

[0052] If there are zero values ​​in the data set, special handling is required because zero values ​​may cause problems during the normalization process. One way to do this is to add a constant to all the data so that the minimum value is not zero.

[0053] Step 4. Handling negative values.

[0054] If there are negative values ​​in the data set, you can consider using absolute value or logarithmic transformation to deal with it.

[0055] Step 5. Check the data.

[0056] After dimensionless processing, check whether the data is reasonable and ensure that there is no data missing or error.

[0057] Step 6. Conduct correlation analysis.

[0058] The dimensionless data were used for grey relational analysis.

[0059] Step 7. Interpretation of results.

[0060] Based on the results of the correlation analysis, explain the degree of correlation between the factors.

[0061] Step 8. Verify.

[0062] If possible, use other methods or data sets to verify the results of dimensionless processing and association analysis. In practice, the choice of dimensionless method depends on the characteristics of the data and the purpose of the analysis. For example, if the data distribution is close to normal distribution, Zscore standardization may be more appropriate; if the range of the data is known, minimum and maximum standardization may be more appropriate.

[0063] In an exemplary embodiment, in order to better illustrate the process of calculating the sequence correlation of the first data sequence and the second data sequence based on the correlation coefficients at the various moments, the following implementation steps may also be proposed, specifically including: obtaining the average coefficient value of the correlation coefficients at the various moments, and determining the average coefficient value as the sequence correlation; and / or, determining the target correlation based on multiple sequence correlations corresponding to the first data sequence and multiple second data sequences, including: for the multiple sequence correlations, sorting the multiple sequence correlations in order of size to obtain a correlation sequence, and determining the target correlation according to the correlations in the correlation sequence.

[0064] For the calculation of the correlation degree, the correlation coefficient between the first data sequence and the second data sequence may be calculated first, for example, by using a grey correlation algorithm to calculate the grey correlation coefficient. The specific process is as follows:

[0065] For the reference sequence (corresponding to the first data sequence) X0, there are several comparison sequences (corresponding to the second data sequence), which can be used i Indicates. i is an n×m matrix, where n represents the sample size and m represents the number of indicators. The reference series and comparison series after normalization are and The correlation coefficient ξ(X i ) can be calculated by the following formula:

[0066]

[0067] Among them, ρ is the resolution coefficient, which is generally between 0 and 1, usually 0.5, and i, k, m, and n are all positive integers.

[0068] In this embodiment, considering the degree of association between factors, it is mainly described by the order of the magnitude of the association rather than just the magnitude of the association. The process of sorting the association degrees of multiple sequences is further explained. Specifically, the association degrees of m subsequences (corresponding to the second data sequence) to the same parent sequence (corresponding to the first data sequence) are arranged in order of magnitude to form an association sequence, denoted as {x}, which reflects the "superiority" relationship of each subsequence with respect to the parent sequence.

[0069] In an exemplary embodiment, the implementation process of calculating the degree of degradation of the lubricant according to the target correlation between the reference data and the comparison data specifically includes: respectively determining the indicator types corresponding to the reference indicator and the comparison indicator, wherein the indicator types include at least one of the following: a smaller-is-better indicator, a larger-is-better indicator, and an intermediate indicator; using the degree of degradation formula corresponding to the indicator type to calculate the reference data to obtain a first degree of degradation of the lubricant, and using the degree of degradation formula corresponding to the indicator type to calculate the comparison data to obtain a second degree of degradation of the lubricant, wherein the degree of degradation formula includes at least one of the following: a smaller-is-better indicator degree of degradation formula, a larger-is-better indicator degree of degradation formula, and an intermediate indicator degree of degradation formula.

[0070] Among them, the smaller the better the indicator degradation degree formula is as follows:

[0071]

[0072] Among them, x is the measured value of the indicator parameter (i.e., comparative data), [x min , x max ] is the normal range of indicator parameters.

[0073] The larger the better type indicator degradation degree formula is as follows:

[0074]

[0075] Among them, x is the measured value of the indicator parameter, [x min , x max ] is the normal range of indicator parameters.

[0076] The intermediate index degradation degree formula is as follows:

[0077]

[0078] Among them, x is the measured value of the indicator parameter, [x min , x max ] is the normal range of the indicator parameter, [x a , x h ] is the optimal operating range of the indicator parameters.

[0079] In an exemplary embodiment, before determining the degradation degree of the lubricating oil according to the weighted sum of different degradation degrees, the weight of the degradation degree may be further determined by the following formula:

[0080] i=1,2,…,m; where W i is the weight, γ i represents the relevance of the i-th target, represents the sum of all target associations corresponding to the different groups of comparison data, and m is a positive integer.

[0081] Optionally, in other embodiments, the reference indicator and the comparison indicator can be analyzed using a hierarchical analysis method, and the reference indicator and the comparison indicator can be weighted according to the analysis results to obtain a first weight for the reference indicator and a second weight for the comparison indicator; the second weight is determined as the weight corresponding to the degree of degradation.

[0082] Obviously, the embodiments described above are only some embodiments of the present application, not all embodiments. In order to better understand the above method, the above process is described below in conjunction with the embodiments, but it is not intended to limit the technical solutions of the embodiments of the present application, specifically:

[0083] In an alternative embodiment, by Figure 3 The process shown in the figure illustrates the process of evaluating the oil condition of lubricating oil in this application. Figure 3 As shown, the specific steps are as follows:

[0084] Step 1: Determine a reference number sequence (first data sequence) and a comparison number sequence (second data sequence), and input the reference number sequence and the comparison number sequence.

[0085] The reference series represents the data sequence composed of factors reflecting the system behavior characteristics, and the comparison series represents the data sequence composed of factors affecting the system behavior characteristics.

[0086] Since the change of iron element reflects the wear of the main gearbox, this application uses iron element as the main variable for grey correlation analysis and constructs a reference sequence corresponding to the data of iron element. At the same time, the data of six indicators, namely kinematic viscosity, acid value, moisture, copper element, silicon element and phosphorus element, are used as influencing factors to construct six comparative series.

[0087] In this step, according to the needs of lubricant performance evaluation, key physical and chemical indicators reflecting the health status of the lubricant can be selected from the oil inspection data as parameters to characterize the lubricant performance.

[0088] Step 2: Non-dimensionalize the reference series and comparison series.

[0089] Since the physical meanings of various factors in the system are different, the dimensions of the data may not be the same, which makes it difficult to compare or to get the correct conclusion when comparing. Therefore, dimensionless data processing is generally required when performing grey relational analysis.

[0090] Step 3: Find the reference sequence X0 and the comparison sequence X i The grey relational coefficient ξ(X i ).

[0091] For the reference sequence X0, there are several comparison sequences, which can be used i Indicates. i is an n×m matrix, where n represents the sample size and m represents the number of indicators. The reference series and comparison series after normalization are and The correlation coefficient ξ(X i ) can be calculated by the following formula:

[0092]

[0093] Where ρ is the resolution coefficient, which is generally between 0 and 1, and is usually 0.5.

[0094] Step 4: Find the correlation γ i .

[0095] Because the correlation coefficient is the correlation degree value between the comparison series and the reference series at each moment (i.e. each point in the curve), it has more than one value, and the information is too scattered to make an overall comparison. Therefore, it is necessary to concentrate the correlation coefficients at each moment (i.e. each point in the curve) into one value, that is, to find its average value, as a quantitative representation of the correlation degree between the comparison series and the reference series.

[0096] Correlation γ i The formula is as follows:

[0097]

[0098] γ i Represents the comparison sequence X i The grey correlation degree of the reference series X0 is also called sequence correlation degree, average correlation degree, and line correlation degree. i The closer the value is to 1, the better the correlation is.

[0099] Step 5: Sort by relevance.

[0100] The degree of association between factors is mainly described by the order of the association, not just the size of the association. The association of m subsequences to the same parent sequence can be arranged in order of size to form an association order, denoted as {x}, which reflects the "superiority" relationship of each subsequence to the parent sequence.

[0101] Through the above steps, grey correlation analysis can be used to calculate the correlation between key physical and chemical index parameters and the wear of the wind turbine gearbox.

[0102] Step 6: Use the hierarchical analysis method to assign weights and obtain the corresponding weights of the indicators, so as to facilitate the subsequent combination of degradation degree and weight to quantify the health of the lubricant.

[0103] Specifically, the analytic hierarchy process is used to allocate the weights of seven indicators, namely, kinematic viscosity, acid value, moisture, iron, copper, silicon and phosphorus. Then, the constant weights of the indicators are calculated according to the grey correlation degree, and the calculation formula is as follows.

[0104]

[0105] The indicator weight result is obtained by normalizing the grey correlation degree of the indicator.

[0106] Step 7: Calculate the degradation degree.

[0107] According to the standard operating state of lubricating oil, the fuzzy linear normalization method is used to calculate the relative deterioration degree of each index.

[0108] Since the lubrication health evaluation indicators of wind turbines each have their own physical meaning and value range, in order to eliminate the influence of units, it is necessary to standardize the lubrication status evaluation indicators of wind turbines.

[0109] The degree of degradation is defined as the difference between the measured value of the lubricant index and the normal working threshold. The degradation degree can take any value in the range of [0-1]. 0 means that the measured value is basically infinitely close to the theoretical value and the performance is excellent. 1 means that the lubricant has failed.

[0110] Mainly calculate the deterioration degree of three types of indicators: the smaller the better type of indicators, the larger the better type of indicators, and the intermediate type of indicators.

[0111] The normalized calculation formula for the smaller the better type index (i.e. the above-mentioned degradation degree formula) is as follows:

[0112]

[0113] The normalized calculation formula for the larger the better type index (i.e. the above-mentioned degradation degree formula) is as follows:

[0114]

[0115] The normalized calculation formula for the intermediate index (i.e. the above-mentioned degradation degree formula) is as follows:

[0116]

[0117] Among them, x is the measured value of the indicator parameter (i.e., comparative data), [x min , x max ] is the normal range of the indicator parameter, [x a , x h ] is the optimal operating range of the indicator parameters.

[0118] Step 8: Finally, by multiplying the degradation degree of each indicator by the weight and summing them up, the comprehensive evaluation result of the degradation degree of the wind turbine can be obtained.

[0119] It should be noted that the present application proposes a method for comprehensively analyzing the health of lubricating oil based on the grey correlation degree of lubricating oil under multiple indicators. Specifically, multiple indicators (such as kinematic viscosity, acid value, moisture, iron, copper, silicon, phosphorus) that can characterize the basic performance, oxidation degree, wear degree, pollution degree and additive loss degree of lubricating oil are selected from the oil inspection data. Then, the weight of each indicator is calculated by using grey relational analysis (GRA), and then the relative degree of degradation of each indicator is calculated by using fuzzy linear transformation to evaluate the health status of different lubricating oils. This overcomes the deficiency of evaluating the health of oil products with a single indicator, improves the accuracy and effectiveness of the evaluation of the health status of different lubricating oils, and has important reference significance for the selection and replacement of lubricating oil for wind turbines.

[0120] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0121] In this embodiment, a device for determining the state of oil is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made are not repeated here. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0122] Figure 4 : is a structural block diagram of a device for determining oil product status according to an embodiment of the present application, the device comprising:

[0123] A first determination module 42 is used to determine oil product data corresponding to a preset oil product evaluation index from the lubricating oil inspection data of the wind turbine generator set, wherein the preset oil product evaluation index at least includes a reference index and a comparison index, and the oil product data at least includes reference data for indicating the wear state of the lubricating oil container and comparison data for indicating the performance of the lubricating oil, wherein a group of reference data corresponds to different groups of comparison data;

[0124] A calculation module 44, configured to calculate the degradation degree of the lubricating oil according to the target correlation between the reference data and the comparison data, and determine the degradation degree of the lubricating oil according to the weighted sum of different degradation degrees;

[0125] The second determination module 46 is configured to determine the oil quality state of the lubricating oil based on the degradation degree.

[0126] Through the above device, the oil product data corresponding to the preset oil product evaluation index is determined from the lubricating oil inspection data of the wind turbine generator set, wherein the preset oil product evaluation index includes at least a reference index and a comparison index, and the oil product data includes at least reference data for indicating the wear state of the lubricating oil container and comparison data for indicating the performance of the lubricating oil, wherein a group of reference data corresponds to different groups of comparison data; the degradation degree of the lubricating oil is calculated according to the target correlation degree of the reference data and the comparison data, and the degradation degree of the lubricating oil is determined according to the weighted sum value of different degradation degrees; the oil product state of the lubricating oil is determined based on the degradation degree. It can be seen that the present application overcomes the deficiency of evaluating the health of the oil product by a single index by combining multiple key indicators to comprehensively analyze the oil product state of the lubricating oil, can effectively and accurately evaluate the health status of the lubricating oil of the wind turbine generator set, has important reference significance for the selection and replacement of the lubricating oil of the wind turbine gearbox, and thus solves the problem of difficulty in accurately determining the oil product state of the lubricating oil of the wind turbine generator set, realizes accurate determination of the oil product state of the lubricating oil of the wind turbine generator set, and improves the technical effect of the operation and maintenance efficiency and safety of the wind turbine generator set.

[0127] In an exemplary embodiment, the first determination module 42 is also used to construct a first data sequence corresponding to a set of reference data and a second data sequence corresponding to a set of comparison data, respectively, before calculating the degradation degree of the lubricant according to the target correlation between the reference data and the comparison data, wherein the first data sequence represents the reference data at different moments, and the second data sequence represents the comparison data at different moments; calculate the correlation coefficient between the first data sequence and the second data sequence at each moment, and calculate the sequence correlation between the first data sequence and the second data sequence according to the correlation coefficient at each moment; determine the target correlation based on multiple sequence correlations corresponding to the first data sequence and multiple second data sequences.

[0128] In an exemplary embodiment, the first determination module 42 is also used to perform dimensionless processing on the first data sequence and the second data sequence before calculating the correlation coefficient of the first data sequence and the second data sequence at each time, wherein the dimensionless processing method includes: performing data scaling on the first data sequence and the second data sequence according to a scaling rule, wherein the values ​​of the first data sequence and the second data sequence after the data scaling belong to the scaling range corresponding to the scaling rule.

[0129] In an exemplary embodiment, the first determination module 42 is further used to: obtain the average coefficient value of the correlation coefficient at each moment, and determine the average coefficient value as the sequence correlation degree; and / or determine the target correlation degree based on multiple sequence correlation degrees corresponding to the first data sequence and multiple second data sequences, including: for the multiple sequence correlation degrees, sorting the multiple sequence correlation degrees in order of size to obtain a correlation degree sequence, and determining the target correlation degree according to the correlation degrees in the correlation degree sequence.

[0130] In an exemplary embodiment, the calculation module 44 is also used to: respectively determine the indicator types corresponding to the reference indicator and the comparison indicator, wherein the indicator types include at least one of the following: a smaller the better indicator, a larger the better indicator, and an intermediate indicator; use the degradation degree formula corresponding to the indicator type to calculate the reference data to obtain a first degradation degree of the lubricant, and use the degradation degree formula corresponding to the indicator type to calculate the comparison data to obtain a second degradation degree of the lubricant, wherein the degradation degree formula includes at least one of the following: a smaller the better indicator degradation degree formula, a larger the better indicator degradation degree formula, and an intermediate indicator degradation degree formula.

[0131] In an exemplary embodiment, the calculation module 44 is further configured to: before determining the degradation degree of the lubricating oil according to the weighted sum of different degradation degrees, determine the weight of the degradation degree by the following formula:

[0132] i=1,2,…,m; where W i is the weight, γ i represents the relevance of the i-th target, represents the sum of all target associations corresponding to the different groups of comparison data, and m is a positive integer.

[0133] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when run.

[0134] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0135] S1, determining oil product data corresponding to a preset oil product evaluation index from lubricating oil inspection data of a wind turbine generator set, wherein the preset oil product evaluation index includes at least a reference index and a comparison index, and the oil product data includes at least reference data for indicating a wear state of a lubricating oil container and comparison data for indicating lubricating oil performance, wherein a group of reference data corresponds to different groups of comparison data;

[0136] S2, calculating the degradation degree of the lubricating oil according to the target correlation degree between the reference data and the comparison data, and determining the degradation degree of the lubricating oil according to the weighted sum values ​​of different degradation degrees;

[0137] S3, determining the oil condition of the lubricating oil based on the degree of degradation.

[0138] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0139] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0140] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0141] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0142] S1, determining oil product data corresponding to a preset oil product evaluation index from lubricating oil inspection data of a wind turbine generator set, wherein the preset oil product evaluation index includes at least a reference index and a comparison index, and the oil product data includes at least reference data for indicating a wear state of a lubricating oil container and comparison data for indicating lubricating oil performance, wherein a group of reference data corresponds to different groups of comparison data;

[0143] S2, calculating the degradation degree of the lubricating oil according to the target correlation degree between the reference data and the comparison data, and determining the degradation degree of the lubricating oil according to the weighted sum values ​​of different degradation degrees;

[0144] S3, determining the oil condition of the lubricating oil based on the degree of degradation.

[0145] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0146] An embodiment of the present application further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0147] An embodiment of the present application further provides another computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0148] An embodiment of the present application also provides a computer program, which includes computer instructions stored in a computer-readable storage medium; a processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps in any one of the above method embodiments.

[0149] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0150] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0151] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for determining the state of an oil product, characterized in that: include: Determining oil product data corresponding to a preset oil product evaluation index from lubricating oil inspection data of the wind turbine generator set, wherein the preset oil product evaluation index includes at least a reference index and a comparison index, and the oil product data includes at least reference data for indicating a wear state of a lubricating oil container and comparison data for indicating lubricating oil performance, wherein a group of reference data corresponds to different groups of comparison data; Calculating the degradation degree of the lubricating oil according to the target correlation degree between the reference data and the comparison data, and determining the degradation degree of the lubricating oil according to the weighted sum values ​​of different degradation degrees; The oil condition of the lubricating oil is determined based on the degree of degradation.

2. The method according to claim 1, characterized in that Before calculating the degree of degradation of the lubricating oil according to the target correlation between the reference data and the comparison data, the method further includes: Constructing a first data sequence corresponding to a set of reference data and a second data sequence corresponding to a set of comparison data respectively, wherein the first data sequence represents reference data at different times, and the second data sequence represents comparison data at different times; Calculating the correlation coefficient between the first data sequence and the second data sequence at each time, and calculating the sequence correlation degree between the first data sequence and the second data sequence according to the correlation coefficient at each time; The target correlation degree is determined based on a plurality of sequence correlation degrees corresponding to the first data sequence and a plurality of second data sequences.

3. The method according to claim 2, characterized in that Before calculating the correlation coefficients of the first data sequence and the second data sequence at each time, the method further includes: The first data sequence and the second data sequence are dimensionally processed, wherein the dimensionally processed method includes: The first data sequence and the second data sequence are data scaled according to a scaling rule, wherein values ​​of the first data sequence and the second data sequence after data scaling belong to a scaling range corresponding to the scaling rule.

4. The method according to claim 2, characterized in that: Calculating the sequence correlation degree of the first data sequence and the second data sequence according to the correlation coefficients at each moment includes: Obtaining an average coefficient value of the correlation coefficients at each moment, and determining the average coefficient value as the sequence correlation degree; And / or, determining the target correlation degree based on multiple sequence correlation degrees corresponding to the first data sequence and multiple second data sequences, including: sorting the multiple sequence correlation degrees in order of size to obtain a correlation degree sequence, and determining the target correlation degree according to the correlation degrees in the correlation degree sequence.

5. The method according to claim 1, characterized in that Calculating the degree of lubricant degradation according to the target correlation between the reference data and the comparison data includes: Determine the indicator types corresponding to the reference indicator and the comparison indicator respectively, wherein the indicator type includes at least one of the following: a smaller-is-better indicator, a larger-is-better indicator, and an intermediate indicator; The reference data is calculated using the degradation degree formula corresponding to the indicator type to obtain a first degradation degree of the lubricant, and the comparison data is calculated using the degradation degree formula corresponding to the indicator type to obtain a second degradation degree of the lubricant, wherein the degradation degree formula includes at least one of the following: a smaller-is-better indicator degradation degree formula, a larger-is-better indicator degradation degree formula, and an intermediate indicator degradation degree formula.

6. The method according to claim 1, characterized in that Before determining the degradation degree of the lubricating oil according to the weighted sum values ​​of different degradation degrees, the method further includes: The weight of the degradation degree is determined by the following formula: Among them, W i is the weight, γ i represents the relevance of the i-th target, represents the sum of all target associations corresponding to the different groups of comparison data, and m is a positive integer.

7. A device for determining the state of oil, characterized in that: include: A first determination module, used to determine the oil product data corresponding to the preset oil product evaluation index from the lubricating oil inspection data of the wind turbine generator set, wherein the preset oil product evaluation index includes at least a reference index and a comparison index, and the oil product data includes at least reference data for indicating the wear state of the lubricating oil container and comparison data for indicating the lubricating oil performance, wherein a group of reference data corresponds to different groups of comparison data; a calculation module, used to calculate the deterioration degree of the lubricating oil according to the target correlation degree of the reference data and the comparison data, and determine the deterioration degree of the lubricating oil according to the weighted sum value of different deterioration degrees; The second determination module is used to determine the oil condition of the lubricating oil based on the degradation degree.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method according to any one of claims 1 to 6 when executed.

9. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 6 through the computer program.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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