Bearing temperature anomaly determination method and device, electronic equipment, storage medium and program product

Abstract

The embodiment of the invention discloses a bearing temperature anomaly determination method and device, electronic equipment, a storage medium and a program product. Comprising the steps that working condition data and target standard deviation obtained by monitoring the working condition of a bearing at the current time point are obtained, and the target standard deviation is obtained through calculation according to target real-time data corresponding to the working condition data in historical real-time data associated with the bearing temperature of the bearing under the normal operation working condition; on the basis of the working condition data, prediction data associated with the bearing temperature of the bearing under the normal operation working condition are obtained through prediction, and on the basis of the working condition data, current real-time data associated with the bearing temperature of the bearing at the current time point are obtained through calculation; and obtaining a confidence interval based on the prediction data and the target standard deviation, and determining the abnormal condition of the bearing temperature of the bearing at the current time point based on the numerical relationship between the current real-time data and the confidence interval. According to the technical scheme provided by the embodiment of the invention, the temperature abnormity of the bearing can be accurately determined.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of automatic control, and in particular to a method, device, electronic device, storage medium and program product for determining abnormal bearing temperature. Background Art

[0002] As one of the important equipment in power plants, the operating status of rotating machinery directly affects the production safety of power plants.

[0003] At present, by monitoring whether the bearing temperature of the bearings in the rotating machinery is abnormal, abnormal conditions in the operation of the rotating machinery can be discovered in time and measures can be taken to deal with them, thereby avoiding unnecessary losses caused by the shutdown of the rotating machinery due to faults, and at the same time, the service life of the rotating machinery can be extended.

[0004] However, there are many factors that affect bearing temperature, and it is currently impossible to accurately determine whether the bearing temperature is abnormal, which needs to be improved. Summary of the invention

[0005] The embodiments of the present invention provide a method, device, electronic device, storage medium and program product for determining abnormal bearing temperature, so as to achieve accurate determination of abnormal bearing temperature.

[0006] According to one aspect of the present invention, a method for determining abnormal bearing temperature is provided, comprising:

[0007] Acquire operating condition data obtained by operating condition monitoring of the bearing at a current time point, and obtain a target standard deviation, wherein the target standard deviation is calculated based on target actual data of corresponding operating condition data in historical actual data associated with a bearing temperature of the bearing under normal operating conditions;

[0008] Based on the operating condition data, predictive data associated with the bearing temperature of the bearing under normal operating conditions is obtained, and based on the operating condition data, current actual data associated with the bearing temperature of the bearing at a current time point is calculated;

[0009] Based on the predicted data and the target standard deviation, a confidence interval is obtained, and based on the numerical relationship between the current actual data and the confidence interval, the abnormality of the bearing temperature at the current time point is determined.

[0010] According to another aspect of the present invention, there is provided a device for determining abnormal bearing temperature, comprising:

[0011] A target standard deviation acquisition module is used to acquire working condition data obtained by monitoring the working condition of the bearing at a current time point, and to acquire a target standard deviation, wherein the target standard deviation is calculated based on target actual data of corresponding working condition data in historical actual data associated with the bearing temperature of the bearing under normal operating conditions;

[0012] A current live data calculation module, used to predict and obtain prediction data associated with the bearing temperature of the bearing under normal operating conditions based on the operating condition data, and to calculate and obtain current live data associated with the bearing temperature of the bearing at a current time point based on the operating condition data;

[0013] The abnormal situation determination module is used to obtain a confidence interval based on the predicted data and the target standard deviation, and to determine the abnormal situation of the bearing temperature at the current time point based on the numerical relationship between the current actual data and the confidence interval.

[0014] According to another aspect of the present invention, there is provided an electronic device, comprising:

[0015] at least one processor; and a memory in communication with the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by at least one processor. The computer program is executed by at least one processor so that the at least one processor executes any method for determining abnormal bearing temperature provided by any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, on which computer instructions are stored, and the computer instructions are used to enable a processor to implement any method for determining bearing temperature abnormality provided by any embodiment of the present invention when executed.

[0018] According to another aspect of the present invention, a computer program product is provided, comprising: a computer program, which, when executed by a processor, implements any method for determining abnormal bearing temperature provided by any embodiment of the present invention.

[0019] The technical solution of the embodiment of the present invention obtains the working condition data obtained by monitoring the working condition of the bearing at the current time point, and obtains the target standard deviation, wherein the target standard deviation is calculated based on the target actual data of the corresponding working condition data in the historical actual data associated with the bearing temperature of the bearing under normal operating conditions, and the target standard deviation can be used to accurately characterize the discrete situation of the target actual data in the historical actual data, so as to further use the target standard deviation to improve the accuracy of determining the abnormal situation of the bearing temperature; based on the working condition data, the predicted data associated with the bearing temperature of the bearing under normal operating conditions is predicted, and based on the working condition data, the current actual data associated with the bearing temperature of the bearing at the current time point is calculated, and based on the predicted data under normal operating conditions, whether the bearing temperature at the current time point is abnormal can be more intuitively judged; based on the predicted data and the target standard deviation, a confidence interval is obtained, and based on the numerical relationship between the current actual data and the confidence interval, the abnormal situation of the bearing temperature of the bearing at the current time point is determined, and compared with the fixed threshold judgment, the determination of the abnormal situation of the bearing temperature through a dynamic confidence interval can improve the accuracy of the judgment. The above technical solution can accurately determine the abnormality of the bearing temperature by using the calculated confidence interval to determine the abnormality of the bearing temperature at the current time point.

[0020] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 is a flow chart of a method for determining abnormal bearing temperature according to an embodiment of the present invention;

[0023] Figure 2 is a flow chart of another method for determining abnormal bearing temperature according to an embodiment of the present invention;

[0024] Figure 3 is a bearing temperature abnormality determination flow chart of a specific example in another bearing temperature abnormality determination method provided according to an embodiment of the present invention;

[0025] Figure 4 is a structural block diagram of a device for determining abnormal bearing temperature according to an embodiment of the present invention;

[0026] Figure 5 It is a structural schematic diagram of an electronic device for implementing the method for determining abnormal bearing temperature according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention 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 invention described herein can be implemented in an order other than those illustrated or described herein. The situations of "target", "original", etc. are similar and will not be repeated here. 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 that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] Figure 1 This is a flow chart of a method for determining a bearing temperature anomaly provided by an embodiment of the present invention. This embodiment is applicable to the case of determining a bearing temperature anomaly. The method can be executed by a bearing temperature anomaly determination device provided by an embodiment of the present invention, which can be implemented by software and / or hardware, and can be integrated on an electronic device, which can be various user terminals or servers.

[0030] See also Figure 1 The method of the embodiment of the present invention specifically includes the following steps:

[0031] S110. Acquire operating condition data obtained by monitoring the operating condition of the bearing at the current time point, and acquire a target standard deviation, wherein the target standard deviation is calculated based on target actual data of corresponding operating condition data in historical actual data associated with the bearing temperature of the bearing under normal operating conditions.

[0032] Among them, the operating condition data can be understood as data that characterizes the working conditions of the bearing and the rotating machinery in which the bearing is located. Optionally, the operating condition data can include multiple different types of data, and each type of operating condition data can be obtained through different types of sensors. Optionally, the operating condition data includes the input current of the rotating machinery in which the bearing is located, the ambient temperature of the working environment in which the rotating machinery is located, the measured temperature measured by at least two temperature measurement points in the bearing, the working medium temperature of the working medium corresponding to the bearing, and at least one of the cooling medium temperature of the cooling medium corresponding to the bearing. The operating condition of the bearing and the rotating machinery in which it is located can be intuitively reflected through the operating condition data, so as to facilitate its monitoring.

[0033] The historical live data can be understood as data associated with the bearing temperature of the bearing under normal operating conditions in the historical time period. For example, data associated with the bearing temperature of the bearing under normal operating conditions include the input current of the rotating machinery where the bearing is located, the ambient temperature of the working environment where the rotating machinery is located, and the working medium temperature of the working medium corresponding to the bearing. Optionally, the historical raw data of the rotating machinery can be collected, cleaned, and the data set under normal operating conditions can be screened and normalized to obtain the historical live data.

[0034] The target actual data can be understood as the data corresponding to the working condition data in the historical actual data. Optionally, it can be the data that is the same as the working condition data in the historical actual data. Optionally, there can be multiple target actual data corresponding to the working condition data in the historical actual data, and the target standard deviation can be calculated based on the multiple target actual data to reflect the discrete situation of the target actual data, so as to further determine the normal range that the working condition data should be in under normal operating conditions.

[0035] S120. Based on the operating condition data, predict data associated with the bearing temperature of the bearing under normal operating conditions are predicted, and based on the operating condition data, current actual data associated with the bearing temperature of the bearing at the current time point are calculated.

[0036] The predicted data may be understood as data associated with the bearing temperature predicted based on the operating condition data, such as the temperature difference between the bearing temperature and the cooling medium temperature of the bearing cooling medium.

[0037] Based on the operating condition data, the prediction data associated with the bearing temperature of the bearing under normal operating conditions can be predicted. Optionally, the prediction can be made through a model or through exponential smoothing method. The current actual data associated with the bearing temperature of the bearing at the current time point can also be calculated based on the operating condition data. For example, when the prediction data is the temperature difference between the bearing temperature and the cooling medium temperature of the bearing cooling medium, the temperature difference between the bearing temperature at the current time point and the cooling medium temperature of the bearing cooling medium can be calculated based on the operating condition data as the current actual data.

[0038] S130. Obtain a confidence interval based on the predicted data and the target standard deviation, and determine an abnormal condition of the bearing temperature at the current time point based on a numerical relationship between the current actual data and the confidence interval.

[0039] The confidence interval can be understood as the interval in which the current actual data should be under normal operating conditions. Optionally, the confidence interval can be calculated using the predicted data, the target standard deviation, and a preset confidence level, for example, the confidence interval is obtained by adding / subtracting the product of the target standard deviation and the preset confidence level to the predicted data.

[0040] After obtaining the confidence interval, the abnormality of the bearing temperature at the current time point can be determined based on the numerical relationship between the current live data and the confidence interval. Optionally, the bearing temperature can be determined to be normal when the current live data is within the confidence interval, and the bearing temperature can be determined to be abnormal when the current live data is greater than the upper limit of the confidence interval.

[0041] The technical solution of the embodiment of the present invention obtains the working condition data obtained by monitoring the working condition of the bearing at the current time point, and obtains the target standard deviation, wherein the target standard deviation is calculated based on the target actual data of the corresponding working condition data in the historical actual data associated with the bearing temperature of the bearing under normal operating conditions, and the target standard deviation can be used to accurately characterize the discrete situation of the target actual data in the historical actual data, so as to further use the target standard deviation to improve the accuracy of determining the abnormal situation of the bearing temperature; based on the working condition data, the predicted data associated with the bearing temperature of the bearing under normal operating conditions is predicted, and based on the working condition data, the current actual data associated with the bearing temperature of the bearing at the current time point is calculated, and based on the predicted data under normal operating conditions, whether the bearing temperature at the current time point is abnormal can be more intuitively judged; based on the predicted data and the target standard deviation, a confidence interval is obtained, and based on the numerical relationship between the current actual data and the confidence interval, the abnormal situation of the bearing temperature of the bearing at the current time point is determined, and compared with the fixed threshold judgment, the determination of the abnormal situation of the bearing temperature through a dynamic confidence interval can improve the accuracy of the judgment. The above technical solution can accurately determine the abnormality of the bearing temperature by using the calculated confidence interval to determine the abnormality of the bearing temperature at the current time point.

[0042] An optional technical solution, a method for determining bearing temperature abnormality, also includes: after determining the abnormal conditions corresponding to each current time point within the target time period, obtaining the corresponding abnormal conditions within the target time period as a first number of current time points characterizing bearing temperature abnormality, and a second number of current time points within the target time period; when the proportional relationship between the first number and the second number is greater than a preset proportional threshold, a temperature abnormality warning is issued.

[0043] Among them, there can be a second number of current time points within the target time period. After determining the abnormal conditions corresponding to each current time point within the target time period, the abnormal conditions corresponding to the first number of current time points of abnormal bearing temperature within the target time period can be obtained. When the proportional relationship between the first number and the second number is greater than the preset proportional threshold, a temperature abnormality warning is issued.

[0044] The above technical solution can avoid abnormal temperature rise of the bearing in a normal state due to special circumstances, which may indicate a suspected fault, and reduce false alarms by triggering an alarm when the abnormal situation in the target time period is a certain proportion of the current time point that characterizes the abnormal bearing temperature.

[0045] On this basis, optionally, a temperature abnormality warning is issued, including: determining the over-limit value of the last current time point among the first number of current time points, wherein the over-limit value is the value of the current actual data corresponding to the last current time point exceeding the upper limit of the confidence interval corresponding to the last current time point; determining a warning level based on the over-limit value, and issuing a temperature abnormality warning based on the warning level.

[0046] Among them, the value of the current actual data corresponding to the last current time point exceeding the upper limit of the confidence interval corresponding to the last current time point can be calculated, and the warning level can be divided according to the size of the excess value. For example, the warning level can be divided into four levels, such as exceeding the limit of 5, 10, 15 and 20°C, and temperature abnormality warning can be issued based on the warning level.

[0047] The above technical solution, by dividing the warning levels, can issue a warning when the limit value is not exceeded much, compared to issuing a warning when the accident has already occurred, thereby providing sufficient time margin for accident prevention through early warning.

[0048] Another optional technical solution is to predict the bearing temperature of a bearing under normal operating conditions based on the operating condition data, including: obtaining a pre-trained target prediction model; inputting the operating condition data into the target prediction model to predict the bearing temperature of the bearing under normal operating conditions.

[0049] Among them, when predicting the prediction data based on the working condition data, the working condition data can be input into the pre-trained target prediction model to predict the prediction data associated with the bearing temperature of the bearing under normal operating conditions. Optionally, the target training model can be obtained in the following way: obtain a pre-built initial prediction model, a training data set and a test data set, wherein the training data set and the test data set are obtained based on historical working condition data; train the initial prediction model based on the training data set to obtain an intermediate prediction model; test the intermediate prediction model based on the test data set, and when the root mean square error between the prediction data output by the intermediate prediction model and the actual output data in the test data set is less than a preset error threshold, the tested intermediate prediction model is used as the target prediction model. Through the training set and the test set, the initial prediction model can be trained and tested, and the intermediate prediction model that meets the requirements after testing can be determined as the target prediction model, and the prediction data can be accurately predicted through the target prediction model.

[0050] The above technical solution can obtain accurate prediction data by predicting the prediction data through the trained target prediction model.

[0051] Figure 2It is a flowchart of another method for determining bearing temperature anomaly provided in an embodiment of the present invention. This embodiment is optimized based on the above-mentioned technical solutions. In this embodiment, optionally, the working condition data includes working condition data of at least two parts on the bearing, and based on the working condition data, the predicted data associated with the bearing temperature of the bearing under normal operating conditions is predicted, and based on the working condition data, the current actual data associated with the bearing temperature of the bearing at the current time point is calculated, including: for the working condition data of each part, based on the working condition data, the predicted data associated with the bearing temperature of the part on the bearing under normal operating conditions is predicted, and based on the working condition data, the current actual data associated with the bearing temperature of the part on the bearing at the current time point is calculated; based on the predicted data and the target standard deviation, a confidence interval is obtained, and based on the numerical relationship between the current actual data and the confidence interval, the abnormality of the bearing temperature of the part on the bearing at the current time point is determined, including: for each part, based on the predicted data of the part and the target standard deviation, a confidence interval is obtained, and based on the numerical relationship between the current actual data of the part and the confidence interval, the abnormality of the bearing temperature of the part on the bearing at the current time point is determined. The explanations of the terms that are the same as or corresponding to the above embodiments are not repeated here.

[0052] See also Figure 2 The method of this embodiment may specifically include the following steps:

[0053] S210. Obtain operating condition data obtained by monitoring the operating conditions of at least two locations on the bearing at the current time point, and obtain a target standard deviation, wherein the target standard deviation is calculated based on target actual data of corresponding operating condition data in historical actual data associated with the bearing temperature of the bearing under normal operating conditions.

[0054] S220. For the operating condition data of each part, based on the operating condition data, predict the predicted data associated with the bearing temperature of the part on the bearing under normal operating conditions, and based on the operating condition data, calculate the current actual data associated with the bearing temperature of the part on the bearing at the current time point.

[0055] Among them, when the bearing is relatively long, the operating data of various parts of the bearing, such as the front, middle and rear, may be different. Therefore, it is necessary to obtain the operating data of at least two parts of the bearing, and for the operating data of each part, obtain the predicted data and current actual data corresponding to the part respectively.

[0056] S230. For each part, a confidence interval is obtained based on the predicted data of the part and the target standard deviation, and based on the numerical relationship between the current actual data of the part and the confidence interval, the abnormal condition of the bearing temperature of the part on the bearing at the current time point is determined.

[0057] It is also necessary to obtain the confidence interval corresponding to each part based on the predicted data of the part and the target standard deviation, and determine the abnormal situation of the bearing temperature of the part at the current time point based on the numerical relationship between the current actual data of the part and the confidence interval.

[0058] The technical solution of the embodiment of the present invention can further determine the abnormal parts and classify the abnormal conditions by separately determining the abnormal conditions of the bearing temperature at multiple parts of the bearing, thereby improving the accuracy of determining the abnormal bearing temperature.

[0059] On this basis, optionally, for each part, the predicted data of the part includes a predicted temperature difference between the average temperature of the part and the cooling medium temperature of the cooling medium corresponding to the bearing, and a predicted temperature standard deviation between the measured temperatures respectively measured at at least two temperature measurement points on the part; based on the predicted data and the target standard deviation, a confidence interval is obtained, and based on the numerical relationship between the current actual data and the confidence interval, the abnormality of the bearing temperature of the bearing at the current time point is determined, including: for each part, based on the predicted temperature difference of the part and the first standard deviation corresponding to the predicted temperature difference in the target standard deviation, a temperature difference confidence interval is obtained, and based on the numerical relationship between the current temperature difference data corresponding to the predicted temperature difference in the current actual data and the temperature difference confidence interval, the abnormality of the temperature difference of the part at the current time point is determined; and, for each part, based on the predicted temperature standard deviation of the part and the second standard deviation corresponding to the predicted temperature standard deviation in the target standard deviation, a temperature standard deviation confidence interval is obtained, and based on the numerical relationship between the current standard deviation data corresponding to the predicted temperature standard deviation in the current actual data and the temperature standard deviation confidence interval, the abnormality of the temperature standard difference of the part at the current time point is determined.

[0060] The prediction data may include the predicted temperature difference between the average temperature of each part and the cooling medium temperature of the cooling medium corresponding to the bearing. Optionally, the cooling medium may be cooling oil, cooling water, cooling air, etc. according to the actual cooling method of the bearing. And the predicted temperature standard deviation between the measured temperatures respectively measured at at least two temperature measurement points on the part; by predicting the temperature difference, it can be determined whether the cooling system is faulty and the degree of the fault, and by predicting the temperature standard deviation, it can be determined whether the sensor used to collect temperature at each temperature measurement point is faulty.

[0061] Furthermore, for each part, by respectively calculating the temperature difference confidence interval corresponding to the predicted temperature difference and the standard deviation confidence interval corresponding to the predicted standard deviation, the temperature difference normal situation and the temperature standard difference normal situation corresponding to each part can be determined.

[0062] The above technical solution calculates the normal temperature difference and the normal temperature standard difference of each part of the bearing respectively, and can further judge the cause of the fault when the abnormality is determined.

[0063] On this basis, optionally, the method for determining bearing temperature abnormality also includes: performing classified warning of bearing temperature abnormality based on normal temperature difference conditions and normal temperature standard difference conditions at at least two parts; and determining target processing suggestions corresponding to each warning category in the classified warning from preset bearing temperature abnormality processing suggestions.

[0064] Among them, after determining the abnormal temperature difference and the abnormal temperature standard difference of each of at least two parts, classified warning can be carried out according to the number of abnormal parts and the specific abnormal type of the abnormal part (normal temperature difference and / or abnormal temperature standard difference). Optionally, the warning level can also be determined according to the size of the excess value, and the target processing suggestions corresponding to each warning category in the classified warning can be determined from the preset bearing temperature abnormality processing suggestions. Optionally, the target processing suggestion can be an expert suggestion based on expert experience.

[0065] In order to better understand the above-mentioned technical solutions, the following is an exemplary description with reference to specific examples. In this specific example, the bearing temperature abnormality determination flow chart is as follows: Figure 3 As shown, the specific steps are as follows:

[0066] Step 1: Obtain relevant data from the Supervisory Information System in Plant Level (SIS)

[0067] The system acquires all the measuring point data related to the rotating machinery of the power plant SIS system in real time, and stores, processes and calculates them.

[0068] Step 2: Data Cleansing

[0069] Based on the above related data, the average value and standard deviation of multiple temperature measurement points in each of the front, middle and rear parts of the rotating machinery bearing at each historical time point are calculated; the average value of the front, middle and rear parts of the bearing and the temperature difference of the corresponding cooling medium are calculated; the average value of the front, middle and rear parts of the bearing and the temperature difference of the corresponding working medium are calculated. The above related data and the data obtained by the secondary calculation are cleaned, the data set under normal operating conditions is selected, and normalized to obtain historical real-time data.

[0070] Step 3: Build a target prediction model

[0071] A target prediction model is established for each part of the front, middle and rear of the bearing. For each target prediction model, the input data is the input current of the rotating machinery where the bearing is located, the ambient temperature of the working environment where the rotating machinery is located, the average temperature of each measuring point in the part and the temperature difference of the medium temperature of the corresponding working medium. The output data of the target prediction model is the average temperature of each measuring point in the part and the temperature difference of the corresponding cooling medium and the standard deviation of the temperature measured by multiple temperature measuring points in the part. The target prediction model is trained and verified using historical live data, and the root mean square error of the target prediction model is calculated to determine whether the model is trained. At the same time, since there are multiple identical input data in the historical live data, the output data corresponding to each input data may be different, so it is necessary to calculate the target standard deviation between multiple output data under the same input data.

[0072] Step 4: Determine abnormal bearing temperature

[0073] Using the current real-time data in the relevant data, calculate the average values ​​T1, T2, T3 and the current standard deviation data σ1, σ2, σ3 of the multiple temperature measurement points in each of the front, middle and rear parts of the bearing at the current time point; respectively calculate the current temperature difference data T11, T21, T31 of the average values ​​T1, T2, T3 of the front, middle and rear parts of the bearing and the corresponding cooling medium; respectively calculate the temperature differences T12, T22, T32 of the average values ​​T1, T2, T3 of the front, middle and rear parts of the bearing and the corresponding working medium. For each target prediction model, the input current of the rotating machinery where the bearing is located, the ambient temperature of the working environment where the rotating machinery is located, the temperature difference between the average value of the temperature measured at each measurement point in the part and the medium temperature of the corresponding working medium, are input into the target prediction model to obtain the predicted average value of the temperature measured at each measurement point in the part and the predicted temperature difference of the corresponding cooling medium and the predicted standard deviation between the temperatures measured at multiple temperature measurement points in the part. Calculate the temperature difference confidence interval based on the predicted temperature difference and the target standard deviation, and calculate the standard deviation confidence interval based on the predicted standard deviation and the target standard deviation. The calculated current temperature difference data is compared with the upper limit of the temperature difference confidence interval to determine whether the temperature difference is normal at the current time point; the calculated current standard deviation data is compared with the upper limit of the standard deviation confidence interval to determine whether the standard deviation difference is normal at the current time point.

[0074] Step 5: Set up warning rules

[0075] For each part of the bearing, the warning rule is set as the proportion of the current time points exceeding the upper limit in a continuous fixed-length time period (i.e., the target time period) to all current time points. When a certain proportion threshold is reached, a warning is triggered. It is divided into 4 levels according to the degree of exceeding the limit, such as exceeding the limit by 5, 10, 15, and 20°C. At the same time, the abnormal temperature difference warning and the abnormal standard difference warning are set separately.

[0076] Step 6: Comprehensive alarm rule setting based on expert experience

[0077] Introducing expert experience, a comprehensive early warning setting is made for the abnormal bearing temperature in different parts of the bearing, as shown in the following table:

[0078] Table 1

[0079]

[0080] In this specific example, for each part of the bearing, the abnormal condition of the bearing temperature at the current time point is determined by using the calculated confidence interval, so that the bearing temperature abnormality can be accurately determined.

[0081] Figure 4 This is a structural block diagram of a bearing temperature abnormality determination device provided in an embodiment of the present invention. The device is used to execute the bearing temperature abnormality determination method provided in any of the above embodiments. The device and the bearing temperature abnormality determination method of the above embodiments belong to the same inventive concept. For details not described in detail in the embodiment of the bearing temperature abnormality determination device, refer to the embodiment of the bearing temperature abnormality determination method. Figure 4 The device may specifically include: a target standard deviation acquisition module 310, a current live data calculation module 320 and an abnormal situation determination module 330, wherein:

[0082] A target standard deviation acquisition module 310 is used to acquire operating condition data obtained by monitoring the operating condition of the bearing at a current time point, and to acquire a target standard deviation, wherein the target standard deviation is calculated based on target actual data of corresponding operating condition data in historical actual data associated with the bearing temperature of the bearing under normal operating conditions;

[0083] A current live data calculation module 320, configured to predict and obtain prediction data associated with a bearing temperature of a bearing in a normal operating condition based on the operating condition data, and to calculate and obtain current live data associated with a bearing temperature of the bearing at a current time point based on the operating condition data;

[0084] The abnormality determination module 330 is used to obtain a confidence interval based on the predicted data and the target standard deviation, and determine the abnormality of the bearing temperature at the current time point based on the numerical relationship between the current actual data and the confidence interval.

[0085] Optionally, the device for determining abnormal bearing temperature further includes:

[0086] A second quantity obtaining module is used to obtain, after determining the abnormal conditions corresponding to each current time point in the target time period, a first quantity of current time points corresponding to the abnormal conditions in the target time period, which indicate abnormal bearing temperature, and a second quantity of current time points in the target time period;

[0087] The first temperature anomaly warning module is used to issue a temperature anomaly warning when the ratio between the first quantity and the second quantity is greater than a preset ratio threshold.

[0088] On this basis, optionally, the first temperature abnormality warning module includes:

[0089] An over-limit value determination submodule, used to determine an over-limit value of a last current time point among the first number of current time points, wherein the over-limit value is a value at which the current live data corresponding to the last current time point exceeds an upper limit of a confidence interval corresponding to the last current time point;

[0090] The temperature abnormality warning module submodule is used to determine the warning level based on the over-limit value and to issue a temperature abnormality warning based on the warning level.

[0091] Alternatively, the operating condition data includes operating condition data of at least two locations on the bearing, and the current real-time data calculation module 320 includes:

[0092] A current live data calculation submodule is used for predicting the bearing temperature of a part on the bearing under normal operating conditions based on the working condition data of each part, and calculating the current live data associated with the bearing temperature of the part on the bearing at the current time point based on the working condition data;

[0093] The abnormal situation determination module 330 includes:

[0094] The abnormal situation determination submodule is used to obtain a confidence interval for each part based on the predicted data of the part and the target standard deviation, and to determine the abnormal situation of the bearing temperature of the part on the bearing at the current time point based on the numerical relationship between the current actual data of the part and the confidence interval.

[0095] On this basis, optionally, for each part, the predicted data of the part includes a predicted temperature difference between an average temperature of the part and a cooling medium temperature of a cooling medium corresponding to the bearing, and a predicted temperature standard deviation between measured temperatures respectively measured at at least two temperature measurement points on the part; the abnormal situation determination module 330 includes:

[0096] a temperature difference normal condition determination submodule, for obtaining a temperature difference confidence interval for each part based on the predicted temperature difference of the part and a first standard deviation in the target standard deviation corresponding to the predicted temperature difference, and determining the temperature difference normal condition of the part at the current time point based on a numerical relationship between current temperature difference data corresponding to the predicted temperature difference in current actual data and the temperature difference confidence interval; and

[0097] The temperature standard deviation normal situation determination submodule is used to obtain the temperature standard deviation confidence interval for each part based on the predicted temperature standard deviation of the part and the second standard deviation in the target standard deviation corresponding to the predicted temperature standard deviation, and determine the temperature standard deviation normal situation of the part at the current time point based on the numerical relationship between the current standard deviation data corresponding to the predicted temperature standard deviation in the current actual data and the temperature standard deviation confidence interval.

[0098] On this basis, optionally, the bearing temperature abnormality determination device further includes:

[0099] A classification warning module is used to provide a classification warning of abnormal bearing temperature based on the abnormal temperature difference of at least two parts and the abnormal temperature standard difference;

[0100] The target processing suggestion determination module is used to determine the target processing suggestions corresponding to each warning category in the classified warning from the preset bearing temperature abnormality processing suggestions.

[0101] Alternatively, the current live data calculation module 320 includes:

[0102] A target prediction model acquisition submodule is used to acquire a pre-trained target prediction model;

[0103] The prediction data prediction submodule is used to input the operating condition data into the target prediction model to predict the prediction data associated with the bearing temperature of the bearing under normal operating conditions.

[0104] On this basis, optionally, the bearing temperature abnormality determination device further includes:

[0105] A test data set acquisition module is used to acquire a pre-built initial prediction model, a training data set and a test data set, wherein the training data set and the test data set are obtained based on historical operating condition data;

[0106] An intermediate prediction model obtaining module is used to train the initial prediction model based on the training data set to obtain an intermediate prediction model;

[0107] The target prediction model obtaining module is used to test the intermediate prediction model based on the test data set, and when the root mean square error between the predicted data output by the intermediate prediction model and the actual output data in the test data set is less than a preset error threshold, the tested intermediate prediction model is used as the target prediction model.

[0108] Alternatively, the operating condition data includes the input current of the rotating machinery where the bearing is located, the ambient temperature of the working environment where the rotating machinery is located, the measured temperatures respectively measured at at least two temperature measurement points in the bearing, the working medium temperature of the working medium corresponding to the bearing, and at least one of the cooling medium temperature of the cooling medium corresponding to the bearing.

[0109] The device for determining bearing temperature anomaly provided in an embodiment of the present invention obtains operating condition data obtained by operating condition monitoring of the bearing at the current time point through a target standard deviation acquisition module, and obtains a target standard deviation, wherein the target standard deviation is calculated based on target actual data of the corresponding operating condition data in the historical actual data associated with the bearing temperature of the bearing under normal operating conditions. The target standard deviation can be used to accurately characterize the discreteness of the target actual data in the historical actual data, so as to further use the target standard deviation to improve the accuracy of determining the abnormality of the bearing temperature; through the current actual data calculation module, based on the operating condition data, the target standard deviation is predicted to be related to the normal operating condition. The prediction data associated with the bearing temperature of the bearing under normal operating conditions, and the current actual data associated with the bearing temperature of the bearing at the current time point calculated based on the operating data, can more intuitively determine whether the bearing temperature at the current time point is abnormal based on the prediction data under normal operating conditions; through the abnormal situation determination module, based on the prediction data and the target standard deviation, a confidence interval is obtained, and based on the numerical relationship between the current actual data and the confidence interval, the abnormal situation of the bearing temperature at the current time point is determined. Compared with the fixed threshold judgment, the determination of the abnormal situation of the bearing temperature through a dynamic confidence interval can improve the accuracy of the judgment. The above-mentioned bearing temperature abnormality determination device can accurately determine the bearing temperature abnormality by using the calculated confidence interval to determine the abnormal situation of the bearing temperature at the current time point.

[0110] The bearing temperature anomaly determination device provided in the embodiment of the present invention can execute the bearing temperature anomaly determination method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0111] It is worth noting that in the above-mentioned embodiment of the bearing temperature anomaly determination device, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0112] Figure 5 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0113] like Figure 5 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, ROM 12 and RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0114] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0115] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a bearing temperature abnormality determination method.

[0116] In some embodiments, the bearing temperature abnormality determination method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the bearing temperature abnormality determination method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the bearing temperature abnormality determination method in any other appropriate manner (e.g., by means of firmware).

[0117] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0118] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0119] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0120] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0121] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0122] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0123] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0124] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for determining abnormal bearing temperature, characterized in that: include: Acquire operating condition data obtained by monitoring the operating condition of the bearing at a current time point, and acquire a target standard deviation, wherein the target standard deviation is calculated based on target actual data corresponding to the operating condition data in historical actual data associated with the bearing temperature of the bearing under normal operating conditions; Based on the operating condition data, predicting data associated with the bearing temperature of the bearing under the normal operating condition, and based on the operating condition data, calculating current actual data associated with the bearing temperature of the bearing at the current time point; A confidence interval is obtained based on the predicted data and the target standard deviation, and an abnormal condition of the bearing temperature of the bearing at the current time point is determined based on a numerical relationship between the current actual data and the confidence interval.

2. The method according to claim 1, characterized in that Also includes: After determining the abnormal conditions corresponding to each current time point in the target time period, obtaining a first number of the current time points corresponding to the abnormal conditions in the target time period that represent abnormal bearing temperature, and a second number of the current time points in the target time period; When the ratio between the first number and the second number is greater than a preset ratio threshold, a temperature abnormality warning is issued.

3. The method according to claim 2, characterized in that The abnormal temperature warning includes: Determine an over-limit value of a last current time point among the first number of current time points, wherein the over-limit value is a value at which the current live data corresponding to the last current time point exceeds an upper limit of the confidence interval corresponding to the last current time point; A warning level is determined based on the over-limit value, and a temperature abnormality warning is performed based on the warning level.

4. The method according to claim 1, characterized in that: The operating condition data includes operating condition data of at least two locations on the bearing, and the prediction data associated with the bearing temperature of the bearing under the normal operating condition is predicted based on the operating condition data, and the current actual data associated with the bearing temperature of the bearing at the current time point is calculated based on the operating condition data, including: For each of the working condition data of the part, based on the working condition data, predict data associated with the bearing temperature of the part on the bearing under the normal operating condition, and calculate current actual data associated with the bearing temperature of the part on the bearing at the current time point based on the working condition data; The step of obtaining a confidence interval based on the predicted data and the target standard deviation, and determining an abnormal condition of the bearing temperature of the bearing at the current time point based on a numerical relationship between the current actual data and the confidence interval, comprises: For each of the parts, a confidence interval is obtained based on the predicted data of the part and the target standard deviation, and based on the numerical relationship between the current actual data of the part and the confidence interval, the abnormal condition of the bearing temperature of the part on the bearing at the current time point is determined.

5. The method according to claim 4, characterized in that For each of the parts, the predicted data of the part includes a predicted temperature difference between an average temperature of the part and a cooling medium temperature of a cooling medium corresponding to the bearing, and a predicted temperature standard deviation between measured temperatures respectively measured at at least two temperature measurement points on the part; The step of obtaining a confidence interval based on the predicted data and the target standard deviation, and determining an abnormal condition of the bearing temperature of the bearing at the current time point based on a numerical relationship between the current actual data and the confidence interval, comprises: For each of the parts, based on the predicted temperature difference of the part and the first standard deviation in the target standard deviation corresponding to the predicted temperature difference, a temperature difference confidence interval is obtained, and based on the numerical relationship between the current temperature difference data corresponding to the predicted temperature difference in the current actual data and the temperature difference confidence interval, the normal temperature difference of the part at the current time point is determined; and For each of the parts, a temperature standard deviation confidence interval is obtained based on the predicted temperature standard deviation of the part and a second standard deviation in the target standard deviation corresponding to the predicted temperature standard deviation, and based on the numerical relationship between the current standard deviation data corresponding to the predicted temperature standard deviation in the current actual data and the temperature standard deviation confidence interval, the abnormal temperature standard deviation of the part at the current time point is determined.

6. The method according to claim 5, characterized in that Also includes: Based on the temperature difference abnormality of at least two of the parts and the temperature standard difference abnormality, a classified warning of bearing temperature abnormality is performed; From the preset suggestions for handling abnormal bearing temperatures, target handling suggestions corresponding to the warning categories in the classified warning are determined.

7. The method according to claim 1, characterized in that The predicting, based on the operating condition data, to obtain prediction data associated with the bearing temperature of the bearing under the normal operating condition comprises: Obtain the pre-trained target prediction model; The operating condition data is input into the target prediction model to predict and obtain prediction data associated with the bearing temperature of the bearing under the normal operating condition.

8. The method according to claim 7, characterized in that Also includes: Obtaining a pre-built initial prediction model, a training data set, and a test data set, wherein the training data set and the test data set are obtained based on the historical operating condition data; Training the initial prediction model based on the training data set to obtain an intermediate prediction model; The intermediate prediction model is tested based on the test data set, and when the root mean square error between the predicted data output by the intermediate prediction model and the actual output data in the test data set is less than a preset error threshold, the tested intermediate prediction model is used as the target prediction model.

9. The method according to claim 1, characterized in that: The operating condition data includes at least one of the input current of the rotating machinery where the bearing is located, the ambient temperature of the working environment where the rotating machinery is located, the measured temperatures respectively measured at at least two temperature measurement points in the bearing, the working medium temperature of the working medium corresponding to the bearing, and the cooling medium temperature of the cooling medium corresponding to the bearing.

10. A device for determining abnormal bearing temperature, characterized in that: include: a target standard deviation acquisition module, used to acquire operating condition data obtained by monitoring the operating condition of the bearing at a current time point, and to acquire a target standard deviation, wherein the target standard deviation is calculated based on target actual data corresponding to the operating condition data in historical actual data associated with the bearing temperature of the bearing under normal operating conditions; a current live data calculation module, configured to predict, based on the operating condition data, predicted data associated with the bearing temperature of the bearing under the normal operating condition, and calculate, based on the operating condition data, current live data associated with the bearing temperature of the bearing at the current time point; The abnormal situation determination module is used to obtain a confidence interval based on the predicted data and the target standard deviation, and determine the abnormal situation of the bearing temperature of the bearing at the current time point based on the numerical relationship between the current actual data and the confidence interval.

11. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor executes the bearing temperature abnormality determination method according to any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for determining an abnormal bearing temperature as described in any one of claims 1 to 9 when executed.

13. A computer program product, characterized in that include: A computer program, which, when executed by a processor, implements the method for determining a bearing temperature abnormality according to any one of claims 1 to 9.

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