Determination of degree of deterioration of lubricating oil, device, storage medium, electronic device, and computer program product

Abstract

The invention discloses a lubricating oil degradation degree determination method and device, a storage medium, an electronic device and a computer program product, and relates to the field of lubricating oil detection.The lubricating oil degradation degree determination method comprises the steps that N index values of target lubricating oil on N indexes are determined, the ith index value in the N index values is the index value of the ith index in the N indexes, the N indexes comprise a first index and a second index, the first index is an index used for reflecting the abrasion condition of the gearbox, the target lubricating oil is located in the gearbox, the second index is an index used for influencing the abrasion condition of the gearbox, and N is a positive integer larger than or equal to 2; performing normalization processing on each index value in the N index values to obtain N normalized index values; n weight values corresponding to the N indexes are used for conducting weighted summation on the N normalized index values, a target reference value is obtained, and the target reference value is used for reflecting the degradation degree of the target lubricating oil.

Description

Technical Field

[0001] The present application relates to the field of lubricating oil detection, and more specifically, to a method for determining the degree of deterioration of lubricating oil and a device, a storage medium, an electronic device, and a computer program product. Background Art

[0002] Wind turbines are highly automated energy conversion mechanical systems that require extremely high operating accuracy and stability of mechanical equipment. The gearbox of a wind turbine is an important component of a wind turbine. Gearboxes are prone to tooth surface wear and pitting during high-speed rotation, and are important lubrication parts of wind turbines. Lubricating oil can form an oil film at the mechanical contact points of various transmission gears and bearings of wind turbines to reduce friction and wear and extend the service life of the machinery. It also plays a role in cooling, rust and corrosion prevention, cleaning, sealing and buffering. Statistical data show that abnormal wear and failure of equipment caused by the deterioration of lubricating oil is one of the main causes of failure of the transmission system of wind turbines.

[0003] In the related art, methods for evaluating the degree of deterioration of lubricating oils include biodegradability and stability tests, antioxidant tests, and film thickness tests, but most methods for detecting the degree of deterioration of lubricating oils have shortcomings such as too long analysis cycles and inaccurate test results.

[0004] In view of the problem that the conventional method for detecting the degree of lubricant degradation in the related art has low detection efficiency and accuracy, no effective solution has been proposed so far.

[0005] Therefore, it is necessary to improve the related technology to overcome the above-mentioned defects in the related technology. Summary of the invention

[0006] The embodiments of the present application provide a method and device for determining the degree of degradation of a lubricating oil, a storage medium, an electronic device, and a computer program product, so as to at least solve the problem of low detection efficiency and accuracy of the traditional method for detecting the degree of degradation of the lubricating oil.

[0007] According to one aspect of an embodiment of the present application, there is provided a method for determining the degree of degradation of a lubricant, comprising: determining N index values ​​of a target lubricant on N indexes, wherein the i-th index value among the N index values ​​is the index value of the i-th index among the N indexes, the N indexes comprising a first index and a second index, the first index being an index for reflecting the wear condition of a gearbox, the target lubricant being located in the gearbox, the second index being an index for influencing the wear condition of the gearbox, and N being a positive integer greater than or equal to 2; normalizing each of the N index values ​​to obtain N normalized index values; and weighting and summing the N normalized index values ​​using N weight values ​​corresponding to the N indexes to obtain a target reference value, wherein the target reference value is used to reflect the degree of degradation of the target lubricant.

[0008] In an exemplary embodiment, normalizing each of the N indicator values ​​includes: normalizing the i-th indicator value among the N indicator values ​​in the following manner to normalize each of the N indicator values, wherein i=1, 2, ..., N: determining an indicator type of the i-th indicator, wherein the indicator types include: a first type, a second type, and a third type; in the case where the i-th indicator is an indicator of the first type, the smaller the i-th indicator value is, the lower the degree of deterioration of the lubricant; in the case where the i-th indicator is an indicator of the first type, the smaller the i-th indicator value is, the lower the degree of deterioration of the lubricant is; When the i-th indicator is an indicator of the second type, the larger the i-th indicator value is, the lower the degree of deterioration of the lubricant oil is; when the i-th indicator is an indicator of the third type, when the i-th indicator value is within the preset interval, the degree of deterioration of the lubricant oil is lower than when the i-th indicator value is not within the preset interval; when the indicator type of the i-th indicator is the first type, the i-th indicator value is normalized by the following formula: g(x) is the normalized i-th indicator value, x is the i-th indicator value, [x min , x max ] is the normal range of the i-th indicator value of the corresponding type: When the indicator type of the i-th indicator is the second type, the i-th indicator value is normalized by the following formula 2: When the indicator type of the i-th indicator is the third type, the i-th indicator value is normalized by the following formula 3, where [x a , x h ] is the preset interval:

[0009] In an exemplary embodiment, before performing weighted summation on the normalized N indicator values ​​using N weight values ​​corresponding to the N indicators, the method further includes: when the N indicators include one first indicator and N-1 second indicators, using a grey correlation analysis method to determine the correlation between each indicator in the N-1 second indicators and the first indicator; using the following formula 4 to determine the weight value of the i-th indicator in the N indicators to obtain the N weight values: Among them, W i is the weight value of the i-th indicator, i=1, 2, ..., N. When the i-th indicator is the first indicator, γ i is equal to 1, when the i-th indicator is the second indicator, γ i is equal to the correlation between the i-th indicator and the first indicator.

[0010] In an exemplary embodiment, the method further comprises: determining a first sequence, wherein the first sequence X (0) =(x (0) (t1),x (0) (t2),…x (0) (t n )), x (0) (t n ) is the target lubricant in the historical t n The index value of the target index corresponding to the time, t n Time later than t n-1 moment, the N indicators include the target indicator; a least squares estimation parameter column is determined according to the first sequence; and an index value of the target indicator corresponding to the target lubricant at the second moment is predicted according to the index value of the target indicator corresponding to the target lubricant at the first moment and the least squares estimation parameter column, wherein the first sequence has the index value of the target indicator corresponding to the target lubricant at the first moment, and the second moment is a moment after the current moment.

[0011] In an exemplary embodiment, determining a least squares estimation parameter sequence according to the first sequence includes: determining a second sequence according to the first sequence, wherein the second sequence X (1) =(x (1) (t1),x (1) (t n ),…x (1) (t n )), A third sequence is determined based on the second sequence, wherein the third sequence M (1) =(m (1) (t1),m (1) (t2),…m(1) (t n )), m (1) (t k )=(x (1) (t k-1 )+x (1) (t k )) / 2, k=2,3,…,n; determining a target matrix and a target vector according to the first sequence and the third sequence, wherein the target matrix The target vector The least squares estimation parameter column is determined according to the target matrix and the target vector, wherein the least squares estimation parameter column

[0012] In an exemplary embodiment, predicting the index value of the target index corresponding to the target lubricant at the second moment according to the index value of the target index corresponding to the target lubricant at the first moment and the least squares estimation parameter column includes: when the first moment is moment, predicting the index value of the target index corresponding to the target lubricant at the second moment by the following formula 5: Formula 5: Where, t p is the second moment, x (0) (t p ) is the index value of the target index corresponding to the target lubricant at the second moment.

[0013] According to another aspect of an embodiment of the present application, a device for determining the degree of deterioration of a lubricant is also provided, comprising: a determination module, for determining N index values ​​of a target lubricant on N indexes, wherein the i-th index value among the N index values ​​is the index value of the i-th index among the N indexes, the N indexes include a first index and a second index, the first index is an index for reflecting the wear condition of a gearbox, the target lubricant is located in the gearbox, the second index is an index for affecting the wear condition of the gearbox, and N is a positive integer greater than or equal to 2; a processing module, for normalizing each of the N index values ​​to obtain the normalized N index values; a calculation module, for weighted summing the normalized N index values ​​using N weight values ​​corresponding to the N indexes to obtain a target reference value, wherein the target reference value is used to reflect the degree of deterioration of the target lubricant.

[0014] According to another aspect of the embodiment of the present application, a computer-readable storage medium is also provided, the computer-readable storage medium comprising a stored program, wherein the program is configured to perform the above-mentioned determination of the degree of deterioration of the lubricating oil when running.

[0015] According to another aspect of the embodiment of the present application, there is also provided an electronic device, including a memory and a processor, wherein the memory stores a computer program, wherein the processor is configured to execute the determination of the degree of deterioration of the lubricating oil through the computer program.

[0016] According to another aspect of the embodiment of the present application, a computer program product is also provided, including a computer program, and when the computer program is executed by a processor, the above-mentioned determination of the degree of degradation of the lubricating oil is performed.

[0017] In the present application, in the process of determining the degree of deterioration of the lubricant, both the index value of the indicator that can reflect the wear condition of the gearbox and the index value of the indicator that can affect the wear condition of the gearbox are determined, and the N index values ​​of the determined target lubricant are normalized to eliminate the dimension effect, and then the N weight values ​​corresponding to the N indicators are used to perform weighted summation on the normalized N index values, so as to more accurately obtain the target reference value reflecting the degree of deterioration of the target lubricant. At the same time, the process of determining the degree of deterioration of the lubricant is simplified, the detection efficiency is improved, and the problem of low detection efficiency and accuracy of the traditional method of detecting the degree of deterioration of the lubricant is solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] 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.

[0019] 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.

[0020] Figure 1 This is a hardware structure block diagram of a mobile terminal for determining the degree of degradation of lubricating oil according to an embodiment of the present application;

[0021] Figure 2 is a flow chart for determining the degree of degradation of a lubricating oil according to an embodiment of the present application;

[0022] Figure 3 is an optional flow chart for detecting the degree of deterioration of lubricating oil according to an embodiment of the present application;

[0023] Figure 4 is a flow chart of evaluating the degree of deterioration of lubricating oil based on an optional grey relational analysis method according to an embodiment of the present application;

[0024] Figure 5is an optional flow chart for predicting the degree of deterioration of lubricating oil according to an embodiment of the present application;

[0025] Figure 6 It is a structural block diagram of a device for determining the degree of deterioration of lubricating oil 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. Taking running on a mobile terminal as an example, Figure 1 1 is a hardware structure block diagram of a mobile terminal for determining the degree of lubricant degradation in an embodiment of the present application. Figure 1 As shown, the mobile 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 (MP) or a programmable logic device (FPGA)) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / 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 mobile terminal. Figure 1More 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 degree of deterioration of lubricating oil 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 may be connected to the mobile 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 the mobile 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 order to solve the above problem, the present embodiment provides a method for determining the degree of deterioration of lubricating oil, including but not limited to being applied to the above mobile terminal. Figure 2 is a flow chart for determining the degree of degradation of a lubricating oil according to an embodiment of the present application, such as Figure 2 As shown, the process includes the following steps S202-S206:

[0032] Step S202: determining N index values ​​of the target lubricant on N indexes, wherein the i-th index value among the N index values ​​is the index value of the i-th index among the N indexes, the N indexes include a first index and a second index, the first index is an index for reflecting the wear condition of the gearbox, the target lubricant is located in the gearbox, the second index is an index for affecting the wear condition of the gearbox, and N is a positive integer greater than or equal to 2;

[0033] Optionally, the first indicator is iron content, and the second indicator is kinematic viscosity, acid value, moisture, copper content, silicon content, and phosphorus content. Step S204: normalizing each of the N indicator values ​​to obtain normalized N indicator values;

[0034] Step S206: performing weighted summation on the normalized N index values ​​using N weight values ​​corresponding to the N indexes to obtain a target reference value, wherein the target reference value is used to reflect the degree of degradation of the target lubricant.

[0035] Optionally, when N=3, after normalizing each of the three indicator values, the normalized indicator values ​​are N1 (N1=0.2), N2 (N2=0.3), and N3 (N3=0.5), respectively, where the weight corresponding to N1 is a (a=20%); the weight corresponding to N2 is b (b=30%); the weight corresponding to N3 is c (c=50%), then the target reference value is N1*a+N2*b+N3*c=0.2*20%+0.3*30%+0.5*50%=0.38.

[0036] It should be noted that, the larger the target reference value of the target lubricant, the greater the degree of degradation of the target lubricant; and the smaller the target reference value of the target lubricant, the smaller the degree of degradation of the target lubricant.

[0037] In the above steps, in the process of determining the degree of deterioration of the lubricant, both the index value of the indicator that can reflect the wear condition of the gearbox and the index value of the indicator that can affect the wear condition of the gearbox are determined, and the N index values ​​of the determined target lubricant are normalized to eliminate the dimension effect, and then the N weight values ​​corresponding to the N indicators are used to perform weighted summation on the normalized N index values, so as to more accurately obtain the target reference value reflecting the degree of deterioration of the target lubricant, and at the same time, simplify the process of determining the degree of deterioration of the lubricant, improve the detection efficiency, and thus solve the problem of low detection efficiency and accuracy of the traditional method of detecting the degree of deterioration of the lubricant.

[0038] In an exemplary embodiment, normalizing each of the N indicator values ​​may be achieved by the following steps: normalizing the i-th indicator value of the N indicator values ​​by the following steps S1 to S4, so as to normalize each of the N indicator values, wherein steps S2 to S4 are performed under different circumstances, without a specific execution order, i=1, 2, ..., N:

[0039] Step S1: determining the indicator type of the ith indicator, wherein the indicator types include: a first type, a second type, and a third type; when the ith indicator is an indicator of the first type, the smaller the ith indicator value is, the lower the degree of deterioration of the lubricant oil is; when the ith indicator is an indicator of the second type, the larger the ith indicator value is, the lower the degree of deterioration of the lubricant oil is; when the ith indicator is an indicator of the third type, when the ith indicator value is within a preset interval, the degree of deterioration of the lubricant oil is lower than when the ith indicator value is not within the preset interval;

[0040] It should be noted that, when the i-th indicator is an indicator of the third type, if the i-th indicator value is less than the preset interval, it means that the lubricating oil does not play a lubricating role, the lubricating oil is easily oxidized, and the degree of deterioration of the lubricating oil will be higher than the degree of deterioration of the lubricating oil when the i-th indicator value is within the preset interval; if the i-th indicator value is greater than the preset interval, it means that the lubricating oil is more worn, and the degree of deterioration of the lubricating oil will be higher than the degree of deterioration of the lubricating oil when the i-th indicator value is within the preset interval.

[0041] Step S2: When the indicator type of the i-th indicator is the first type, the i-th indicator value is normalized by the following formula: g(x) is the normalized i-th indicator value, x is the i-th indicator value, [x min , x max ] is the normal range of the i-th indicator value of the corresponding type:

[0042]

[0043] Step S3: When the indicator type of the i-th indicator is the second type, the i-th indicator value is normalized by the following formula 2:

[0044]

[0045] Step S4: When the indicator type of the i-th indicator is the third type, the i-th indicator value is normalized by the following formula 3, where [x a , x h ] is the preset interval:

[0046]

[0047] It should be noted that the above steps effectively solve the problem that different indicators of the target lubricant are difficult to calculate directly due to differences in dimensions and magnitudes. By distinguishing the indicator types (first type, second type, third type) and using different normalization formulas, the degree of deterioration of the lubricant under different indicators can be accurately reflected, thereby providing a standardized and comparable data basis for the subsequent determination of the degree of deterioration of the lubricant and the prediction of the deterioration trend.

[0048] Specifically, after normalization, the N index values ​​of the target lubricant on N indexes are converted into dimensionless values ​​in the interval [0-1], which is convenient for calculating the comprehensive degree of deterioration. For the first type of index (such as kinematic viscosity, water content, etc.), the smaller the index value, the lower the degree of deterioration of the lubricant, Formula 1 ensures that the smaller the index value, the closer its normalized result is to 0, indicating that the lubricant has better performance; for the second type of index (such as acid value, metal element content, etc.), the larger the index value, the lower the degree of deterioration of the lubricant, Formula 2 ensures that the larger the index value, the closer its normalized result is to 0, indicating that the lubricant has better performance; and for the third type of index (such as temperature, pressure, etc. that need to be maintained within a specific range), Formula 3 sets the interval [x a , x h ], ensure that the indicator value is within [x a , x h ], its normalized result is close to 0, indicating that the lubricant is in good condition. Once the indicator value exceeds the optimal range, its normalized result will approach 1, indicating that the lubricant may begin to deteriorate.

[0049] In an exemplary embodiment, before performing weighted summation on the normalized N indicator values ​​using the N weight values ​​corresponding to the N indicators, the method further includes the following steps S11-S12:

[0050] Step S11: when the N indicators include one first indicator and N-1 second indicators, use a grey correlation analysis method to determine the correlation between each indicator in the N-1 second indicators and the first indicator;

[0051] Optionally, the first indicator is iron content, and the second indicator is kinematic viscosity, acid value, moisture, copper content, silicon content, and phosphorus content.

[0052] Optionally, refer to Figure 4After dimensionless data processing of the index value of the first index (iron content) and the index values ​​of the six second indexes (kinematic viscosity, acid value, moisture, copper content, silicon content, and phosphorus content), a reference series is constructed according to the normalized first index value and six comparison series are constructed according to the normalized six second index values, wherein the kth comparison series is constructed according to the kth second index value among the six second index values.

[0053] Optionally, the degree of association between the kth second indicator among the six second indicator values ​​and the first indicator is determined according to the degree of association between the kth comparison number sequence and the reference number sequence.

[0054] Step S12: Determine the weight value of the i-th indicator among the N indicators using the following formula 4 to obtain the N weight values:

[0055]

[0056] Among them, W i is the weight value of the i-th indicator, i=1, 2, ..., N. When the i-th indicator is the first indicator, γ i is equal to 1, when the i-th indicator is the second indicator, γ i is equal to the correlation between the i-th indicator and the first indicator.

[0057] In an exemplary embodiment, the method further comprises the following steps S21-S23:

[0058] Step S21: Determine a first sequence, wherein the first sequence X (0) =(x (0) (t1),x (0) (t2),…x (0) (t n )), x (0) (t n ) is the target lubricant in the historical t n The index value of the target index corresponding to the time, t n Time later than t n-1 At the time, the N indicators include the target indicator;

[0059] Optionally, the lubricating oil is sampled at a preset time interval and elemental analysis is performed to obtain the target lubricating oil in the historical t n The indicator value of the target indicator corresponding to the time.

[0060] It should be noted that the reference Figure 5 , the first sequence is an equally spaced sequence.

[0061] Step S22: determining a least squares estimation parameter sequence according to the first sequence;

[0062] Step S23: predicting the index value of the target index corresponding to the target lubricant at the second moment based on the index value of the target index corresponding to the target lubricant at the first moment and the least squares estimation parameter sequence, wherein the first sequence has the index value of the target index corresponding to the target lubricant at the first moment, and the second moment is a moment after the current moment.

[0063] In an exemplary embodiment, determining the least squares estimation parameter sequence according to the first sequence can be implemented by the following steps S31-S34:

[0064] Step S31: Determine a second sequence based on the first sequence, wherein the second sequence X (1) =(x (1) (t1),x (1) (t n ),…x (1) (t n )),

[0065] It should be noted that the reference Figure 5 , the second sequence is a cumulative sequence, that is, the data in the same sequence are added one by one to generate new data, and the accumulated sequence is a cumulative sequence.

[0066] Step S32: Determine a third sequence based on the second sequence, wherein the third sequence M (1) =(m (1) (t1),m (1) (t2),…m (1) (t n )), m (1) (t k )=(x (1) (t k-1 )+x (1) (t k )) / 2, k = 2, 3, ..., n;

[0067] It should be noted that the reference Figure 5 , the third sequence is the neighboring mean sequence.

[0068] Step S33: Determine a target matrix and a target vector according to the first sequence and the third sequence, wherein the target matrix The target vector

[0069] Step S34: Determine the least squares estimation parameter column according to the target matrix and the target vector, wherein the least squares estimation parameter column

[0070] In an exemplary embodiment, predicting the index value of the target index corresponding to the target lubricant at the second moment according to the index value of the target index corresponding to the target lubricant at the first moment and the least squares estimation parameter column can be achieved by the following steps: when the first moment is moment, predicting the index value of the target index corresponding to the target lubricant at the second moment by the following formula 5:

[0071]

[0072] Among them, t p is the second moment, x (0) (t p ) is the index value of the target index corresponding to the target lubricant at the second moment.

[0073] It should be noted that, through the above steps, the target index value of the target lubricant at a certain moment in the future can be predicted, so as to determine the deterioration direction of the target lubricant, and then remind the staff to replace the target lubricant in time to ensure the working efficiency and equipment safety of the machinery corresponding to the target lubricant.

[0074] Obviously, the above-described embodiments are only a part of the embodiments of the present invention, rather than all the 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 invention. Figure 3 The overall flow chart of determining the degree of degradation of a lubricating oil in an embodiment of the application is shown, specifically:

[0075] 1. Conduct laboratory performance testing and analysis of wind turbine lubricant oil;

[0076] 1. Carry out comparative test experiments on the physical and chemical properties of lubricating oils, and complete tests on physical and chemical indicators including viscosity, viscosity index, pour point, flash point, foam characteristics, anti-emulsification, moisture, liquid phase corrosion, copper corrosion, acid value, etc.;

[0077] 2. Carry out comparative test experiments on the tribological performance of lubricating oils, and complete tests on tribological performance indicators including seizure load, sintering load, friction coefficient, wear spot diameter, and comprehensive wear value;

[0078] 3. Carry out tests on other lubricant performance indicators such as antioxidant properties, iron spectrum, infrared spectrum, PQ index, etc.

[0079] It should be noted that in order to avoid the result errors caused by the measuring instruments and measuring methods, the same instrument was used to test different competing lubricants according to the standard method. Repeatability tests were used to measure each indicator multiple times to avoid operational errors.

[0080] 2. Carry out performance testing and analysis of wind turbine lubricant oil;

[0081] 1. Conduct research on the detection and analysis technology of metal particles in lubricating oil, and monitor the wear condition of gearbox in real time through the changes in the number and state of metal particles;

[0082] 2. For units with obvious changes in metal particles and possible severe wear, carry out gearbox endoscope inspection and compare and analyze the gearbox wear conditions under different oil products;

[0083] 3. Carry out lubricating oil sampling and offline analysis tests regularly, and complete comparative analysis of the degree and rate of performance degradation of in-service oil in the idle state based on the test results and change trends.

[0084] It should be noted that the metal particles in the lubricating oil reflect the wear condition of the gearbox. The materials of gear bearings, bearing rings, gear shafts and other components are all iron elements. Generally, the changes in iron elements can be used to reflect the wear condition of the main gearbox.

[0085] 3. Carry out lubricant performance status assessment and deterioration trend prediction;

[0086] 1. Selection of key physical and chemical indicators. According to the needs of lubricant performance evaluation, key physical and chemical indicators that reflect the health status of lubricants are selected as parameters to characterize lubricant performance;

[0087] 2. Use grey correlation analysis to calculate the correlation of key physical and chemical index parameters. Optionally, select seven indicators: kinematic viscosity, acid value, moisture, iron content, copper content, silicon content, and phosphorus content.

[0088] (1) Grey relational analysis is as follows:

[0089] 1) Determine the reference sequence that reflects the system behavior characteristics and the comparison sequence that affects the system behavior. The data sequence that reflects the system behavior characteristics is called the reference sequence. The data sequence composed of factors that affect the system behavior is called the comparison sequence;

[0090] Since the change of iron element reflects the wear of the main gearbox, the iron element is used as the main variable for grey correlation analysis and a reference sequence is constructed. The six indicators of kinematic viscosity, acid value, moisture, copper content, silicon content and phosphorus content are used as influencing factors, and six comparative series can be constructed.

[0091] 2) Perform dimensionless processing on the reference series and the comparison series;

[0092] 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.

[0093] 3) Calculate the grey correlation coefficient ξ(Xi) between the reference series and the comparison series;

[0094] For the reference sequence X0, there are several comparison sequences, which can be represented by Xi. Xi is an n×m matrix, where n represents the sample size and m represents the number of indicators. The reference sequence after normalization is and comparison series The correlation coefficient ξ(Xi) between each comparison series and the reference series at each moment can be calculated by the following formula:

[0095]

[0096] Among them, ρ is the resolution coefficient, which is generally between 0 and 1, and is usually 0.5.

[0097] 4) Find the correlation degree;

[0098] 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. The correlation formula is as follows:

[0099]

[0100] γ i The gray correlation degree of the comparison series to the reference series, also known as the sequence correlation degree, average correlation degree, line correlation degree, γ i The closer the value is to 1, the better the correlation is.

[0101] 5) Relevance ranking;

[0102] Arrange the association degrees of the m subsequences to the same parent sequence in order of size to form an association sequence, denoted as {x}, which reflects the "superiority" relationship of each comparison sequence with respect to the reference sequence.

[0103] 3. Use the analytic hierarchy process to assign weights and obtain the weights of key physical and chemical index parameters on the health of lubricants; combine the degradation degree with the analytic hierarchy process to quantify the health of lubricants;

[0104] (2) The analytic hierarchy process is used to assign weights and quantitatively evaluate the health of lubricants as follows:

[0105] 1) Calculate the degree of deterioration: 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. The degree of deterioration is defined as the difference between the measured value of the lubricant index and the normal working threshold. The degree of deterioration 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, and 1 means that the lubricant has failed. Mainly calculate the degree of deterioration of three types of indicators: the smaller the better indicator, the larger the better indicator, and the intermediate indicator;

[0106] It should be noted that the normalized calculation formula for the smaller the better type of indicators is as shown in Formula 1 above, the normalized calculation formula for the larger the better type of indicators is as shown in Formula 2 above, and the normalized calculation formula for the intermediate type indicators is as shown in Formula 3 above.

[0107] 4. Based on the results of grey correlation analysis, the indicator weights are allocated. According to the weight allocation results of the seven indicators of kinematic viscosity, acid value, moisture, iron, copper, silicon and phosphorus, the constant weights of the indicators are calculated according to the grey correlation degree. The grey correlation degree calculation results of the indicators are normalized to obtain the indicator weight results;

[0108]

[0109] Finally, by multiplying the relative degradation vector of each indicator by the variable weight vector, the comprehensive evaluation result of the degradation degree of the wind turbine can be obtained.

[0110] 5. Predict the deterioration degree of lubricating oil;

[0111] (1) Sampling the lubricating oil at regular intervals and performing elemental analysis to obtain the iron element data that changes over time. Construct the original sequence X of the iron element content (0) =(x (0) (t1),x (0) (t2),…x (0) (t n ));

[0112] (2) Construct a cumulative sequence, X (1) =(x (1) (t1),x (1) (t n ),…x (1) (t n )),

[0113] (3) Construct the neighbor mean sequence, M (1) =(m (1) (t1),m (1) (t2),…m (1) (tn )), m (1) (t k )=(x (1) (t k-1 )+x (1) (t k )) / 2, k = 2, 3, ..., n;

[0114] (4) Construct the data matrix B and vector Y,

[0115] (5) Calculate the least squares estimated parameter list

[0116] (6) Substitute the prediction formula into the iron element series Input any time in the future to predict the corresponding iron content, determine the wear state of the gear based on the element content, and formulate corresponding operation and maintenance measures.

[0117] 4. Formulate oil selection indicators for wind power equipment in different regions;

[0118] 1. According to the different climate characteristics and wind resources, analyze the differences in climate and the requirements for lubrication performance in wind turbine design, and formulate lubrication product quality standards that meet the needs of different regions and different models.

[0119] It should be noted that based on the results of laboratory analysis tests and hanging machine analysis tests, the comprehensive performance of wind turbine lubrication products is systematically evaluated from two aspects: physical and chemical analysis of oil products and equipment wear conditions, and oil selection indicators for wind power equipment in different regions are formulated.

[0120] It should be noted that the present application also has the following advantages: by combining laboratory analysis and testing with on-hook verification, and by regularly analyzing offline sampling data, a database for on-hook testing and analysis of competing gearbox lubricants is established; the comprehensive performance of wind turbine lubrication products is systematically evaluated from two aspects: physical and chemical analysis of the oil and equipment wear conditions; through regular offline analysis and performance testing of the lubricant, the performance trend of the lubricant is analyzed, and the degree and rate of deterioration of the lubricant performance under service are evaluated; the correlation analysis between the physical and chemical indicators of the lubricant and the comprehensive performance of the lubricant is carried out, and the deterioration trend of the oil is predicted; on the basis of relevant standards and specifications, a grading and classification method is adopted to formulate lubrication detection indicators that meet the requirements of different regions and different models, and to compile guidance on the selection of lubricants for wind power equipment.

[0121] 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.

[0122] In this embodiment, a device for determining the degree of degradation of lubricating oil is also provided, which is used to implement the above-mentioned embodiments and preferred embodiments, and will not be repeated hereafter. As used below, the term "module" may 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.

[0123] Figure 6 : is a structural block diagram of a device for determining the degree of degradation of lubricating oil according to an embodiment of the present application, the device comprising:

[0124] A determination module 602 is used to determine N index values ​​of the target lubricant on N indexes, wherein the i-th index value among the N index values ​​is the index value of the i-th index among the N indexes, the N indexes include a first index and a second index, the first index is an index for reflecting the wear condition of the gearbox, the target lubricant is located in the gearbox, the second index is an index for affecting the wear condition of the gearbox, and N is a positive integer greater than or equal to 2;

[0125] A processing module 604 is used to normalize each of the N indicator values ​​to obtain normalized N indicator values;

[0126] The calculation module 606 is used to perform weighted summation on the normalized N index values ​​using N weight values ​​corresponding to the N indexes to obtain a target reference value, wherein the target reference value is used to reflect the degree of degradation of the target lubricant.

[0127] The above-mentioned device, in the process of determining the degree of deterioration of the lubricating oil, not only determines the index value of the index that can reflect the wear condition of the gearbox, but also determines the index value of the index that can affect the wear condition of the gearbox, and normalizes the N index values ​​of the determined target lubricating oil to eliminate the dimension effect, and then uses N weight values ​​corresponding to the N indexes to perform weighted summation on the normalized N index values, so as to more accurately obtain the target reference value that reflects the degree of deterioration of the target lubricating oil. At the same time, it simplifies the process of determining the degree of deterioration of the lubricating oil, improves the detection efficiency, and thus solves the problem of low detection efficiency and accuracy of the traditional method of detecting the degree of deterioration of the lubricating oil.

[0128] In an exemplary embodiment, the processing module 604 is further used to normalize the ith indicator value among the N indicator values ​​in the following manner to normalize each indicator value among the N indicator values, wherein i=1, 2, ..., N: determine the indicator type of the ith indicator, wherein the indicator type includes: a first type, a second type, and a third type; when the ith indicator is an indicator of the first type, the smaller the ith indicator value, the lower the degree of deterioration of the lubricant; when the ith indicator is the ith indicator, the smaller the ith indicator value, the lower the degree of deterioration of the lubricant; In the case of the second type of indicator, the larger the i-th indicator value is, the lower the degree of deterioration of the lubricant oil is; in the case of the i-th indicator being the third type of indicator, the degree of deterioration of the lubricant oil when the i-th indicator value is within the preset interval is lower than the degree of deterioration of the lubricant oil when the i-th indicator value is not within the preset interval; in the case of the indicator type of the i-th indicator being the first type, the i-th indicator value is normalized by the following formula: g(x) is the normalized i-th indicator value, x is the i-th indicator value, [x min , x max ] is the normal range of the i-th indicator value of the corresponding type: When the indicator type of the i-th indicator is the second type, the i-th indicator value is normalized by the following formula 2: When the indicator type of the i-th indicator is the third type, the i-th indicator value is normalized by the following formula 3, where [x a , x h ] is the preset interval:

[0129] In an exemplary embodiment, the calculation module 606 is further used to determine the correlation between each indicator in the N-1 second indicators and the first indicator using a grey correlation analysis method before performing weighted summation on the normalized N indicator values ​​using the N weight values ​​corresponding to the N indicators, when the N indicators include one first indicator and N-1 second indicators; and determine the weight value of the i-th indicator in the N indicators using the following formula 4 to obtain the N weight values: Among them, W i is the weight value of the i-th indicator, i=1, 2, ..., N. When the i-th indicator is the first indicator, γ i is equal to 1, when the i-th indicator is the second indicator, γ i is equal to the correlation between the i-th indicator and the first indicator.

[0130] In an exemplary embodiment, the apparatus further comprises: a prediction module, configured to determine a first sequence, wherein the first sequence X (0) =(x (0) (t1),x (0) (t2),…x (0) (t n )), x (0) (t n ) is the target lubricant in the historical t n The index value of the target index corresponding to the time, t n Time later than t n-1 moment, the N indicators include the target indicator; a least squares estimation parameter column is determined according to the first sequence; and an index value of the target indicator corresponding to the target lubricant at the second moment is predicted according to the index value of the target indicator corresponding to the target lubricant at the first moment and the least squares estimation parameter column, wherein the first sequence has the index value of the target indicator corresponding to the target lubricant at the first moment, and the second moment is a moment after the current moment.

[0131] In an exemplary embodiment, the prediction module is further configured to determine a second sequence according to the first sequence, wherein the second sequence X (1) =(x (1) (t1),x (1) (t n ),…x (1) (t n )), A third sequence is determined based on the second sequence, wherein the third sequence M (1) =(m (1) (t1),m (1) (t2),…m(1) (t n )), m (1) (t k )=(x (1) (t k-1 )+x (1) (t k )) / 2, k=2,3,…,n; determining a target matrix and a target vector according to the first sequence and the third sequence, wherein the target matrix The target vector The least squares estimation parameter column is determined according to the target matrix and the target vector, wherein the least squares estimation parameter column

[0132] In an exemplary embodiment, the prediction module is further configured to predict the index value of the target index corresponding to the target lubricant at the second moment by using the following formula 5 when the first moment is the moment: Formula 5: Where, t p is the second moment, x (0) (t p ) is the index value of the target index corresponding to the target lubricant at the second moment.

[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 running.

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

[0135] S1, determining N index values ​​of a target lubricant on N indexes, wherein an i-th index value among the N index values ​​is an index value of an i-th index among the N indexes, the N indexes include a first index and a second index, the first index is an index for reflecting a wear condition of a gearbox, the target lubricant is located in the gearbox, the second index is an index for affecting the wear condition of the gearbox, and N is a positive integer greater than or equal to 2;

[0136] S2, normalizing each of the N indicator values ​​to obtain normalized N indicator values;

[0137] S3, using N weight values ​​corresponding to the N indicators to perform weighted summation on the normalized N indicator values ​​to obtain a target reference value, wherein the target reference value is used to reflect the degree of degradation of the target lubricant.

[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 a computer program product, 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 performed.

[0141] 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.

[0142] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0143] S1, determining N index values ​​of a target lubricant on N indexes, wherein an i-th index value among the N index values ​​is an index value of an i-th index among the N indexes, the N indexes include a first index and a second index, the first index is an index for reflecting a wear condition of a gearbox, the target lubricant is located in the gearbox, the second index is an index for affecting the wear condition of the gearbox, and N is a positive integer greater than or equal to 2;

[0144] S2, normalizing each of the N indicator values ​​to obtain normalized N indicator values;

[0145] S3, using N weight values ​​corresponding to the N indicators to perform weighted summation on the normalized N indicator values ​​to obtain a target reference value, wherein the target reference value is used to reflect the degree of degradation of the target lubricant.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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 degree of deterioration of lubricating oil, characterized in that: include: Determine N index values ​​of the target lubricant on N indexes, wherein the i-th index value among the N index values ​​is the index value of the i-th index among the N indexes, the N indexes include a first index and a second index, the first index is an index for reflecting the wear condition of the gearbox, the target lubricant is located in the gearbox, the second index is an index for affecting the wear condition of the gearbox, and N is a positive integer greater than or equal to 2; Normalizing each of the N index values ​​to obtain normalized N index values; The normalized N index values ​​are weighted and summed using N weight values ​​corresponding to the N indexes to obtain a target reference value, wherein the target reference value is used to reflect the degree of degradation of the target lubricant.

2. The method according to claim 1, characterized in that Normalizing each of the N indicator values ​​includes: The i-th indicator value among the N indicator values ​​is normalized in the following manner to normalize each indicator value among the N indicator values, wherein i=1, 2, ..., N: Determine the indicator type of the ith indicator, wherein the indicator type includes: a first type, a second type, and a third type; when the ith indicator is an indicator of the first type, the smaller the ith indicator value is, the lower the degree of deterioration of the lubricant oil is; when the ith indicator is an indicator of the second type, the larger the ith indicator value is, the lower the degree of deterioration of the lubricant oil is; when the ith indicator is an indicator of the third type, when the ith indicator value is within a preset interval, the degree of deterioration of the lubricant oil is lower than when the ith indicator value is not within the preset interval; When the indicator type of the i-th indicator is the first type, the i-th indicator value is normalized by the following formula: g(x) is the normalized i-th indicator value, x is the i-th indicator value, [x min , x max ] is the normal range of the i-th indicator value of the corresponding type: When the indicator type of the i-th indicator is the second type, the i-th indicator value is normalized by the following formula 2: When the indicator type of the i-th indicator is the third type, the i-th indicator value is normalized by the following formula 3, where [x a , x h ] is the preset interval:

3. The method according to claim 1, characterized in that Before performing weighted summation on the normalized N indicator values ​​using N weight values ​​corresponding to the N indicators, the method further includes: In the case where the N indicators include one first indicator and N-1 second indicators, a grey correlation analysis method is used to determine the correlation between each indicator in the N-1 second indicators and the first indicator; The weight value of the i-th indicator among the N indicators is determined using the following formula 4 to obtain the N weight values: Among them, W i is the weight value of the i-th indicator, i=1, 2, ..., N. When the i-th indicator is the first indicator, γ i is equal to 1, when the i-th indicator is the second indicator, γ i is equal to the correlation between the i-th indicator and the first indicator.

4. The method according to claim 1, characterized in that: The method further comprises: Determine a first sequence, wherein the first sequence X (0) =(x (0) (t1),x (0) (t2),…x (0) (t n )), x (0) (t n ) is the target lubricant in the historical t n The index value of the target index corresponding to the time, t n Time later than t n-1 At the time, the N indicators include the target indicator; Determine a least squares estimation parameter sequence based on the first sequence; The index value of the target index corresponding to the target lubricant at the first moment is predicted based on the index value of the target index corresponding to the target lubricant at the first moment and the least squares estimation parameter sequence, wherein the first sequence has the index value of the target index corresponding to the target lubricant at the first moment, and the second moment is a moment after the current moment.

5. The method according to claim 4, characterized in that Determining a least squares estimation parameter sequence according to the first sequence includes: A second sequence is determined based on the first sequence, wherein the second sequence A third sequence is determined based on the second sequence, wherein the third sequence M (1) =(m (1) (t1),m (1) (t2),…m (1) (t n )), m (1) (t k )=(x (1) (t k-1 )+x (1) (t k )) / 2, k = 2, 3, ..., n; Determine a target matrix and a target vector according to the first sequence and the third sequence, wherein the target matrix The target vector The least squares estimation parameter column is determined according to the target matrix and the target vector, wherein the least squares estimation parameter column 6. The method according to claim 4, characterized in that Predicting the index value of the target index corresponding to the target lubricant at the second moment according to the index value of the target index corresponding to the target lubricant at the first moment and the least squares estimation parameter sequence includes: When the first moment is t1, the index value of the target index corresponding to the target lubricant at the second moment is predicted by the following formula 5: Among them, t p is the second moment, x (0) (t p ) is the index value of the target index corresponding to the target lubricant at the second moment.

7. A device for determining the degree of deterioration of lubricating oil, characterized in that: include: a determination module, configured to determine N index values ​​of a target lubricant on N indexes, wherein an i-th index value among the N index values ​​is an index value of an i-th index among the N indexes, the N indexes include a first index and a second index, the first index is an index for reflecting a wear condition of a gearbox, the target lubricant is located in the gearbox, the second index is an index for affecting the wear condition of the gearbox, and N is a positive integer greater than or equal to 2; A processing module, used for normalizing each of the N indicator values ​​to obtain normalized N indicator values; A calculation module is used to use N weight values ​​corresponding to the N indicators to perform weighted summation on the normalized N indicator values ​​to obtain a target reference value, wherein the target reference value is used to reflect the degree of deterioration of the target lubricant.

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 method according to any one of claims 1 to 6 is implemented.

Images