Abnormal early warning system for equipment health monitoring

Abstract

The invention is suitable for the technical field of computers, and provides an abnormity early warning system for equipment health monitoring, comprising a data acquisition module which determines an acquisition mode by acquiring initial operation data of to-be-monitored equipment, can ensure that the acquisition process is highly matched with equipment characteristics, greatly improves the pertinence of data acquisition, and improves the accuracy of data acquisition; loss or redundancy of key data caused by an improper acquisition mode is avoided, and the data quality of the acquired target operation data is improved. Afterwards, the data analysis module analyzes the target operation data based on the current working condition and the current environment, and can comprehensively and truly reflect the health condition of the equipment, so that the health monitoring result is more reliable and practical. And meanwhile, the data analysis module outputs early warning information in time when detecting that the health monitoring result is abnormal, so that equipment management personnel and maintenance personnel can know the abnormal condition of the equipment in time so as to take corresponding measures to protect the equipment and prevent further deterioration of faults.

Description

Technical Field

[0001] The present application belongs to the field of computer technology, and in particular, relates to an abnormal warning system for equipment health monitoring. Background Art

[0002] In modern industrial production, mechanical equipment (such as motors, pumps, fans, etc.) is a key power transmission and fluid conveying equipment, which is widely used in many industries such as chemical industry, electric power, metallurgy, mining, etc. The stable operation of mechanical equipment is directly related to the continuity and stability of the entire production process. Therefore, it is necessary to perform real-time health monitoring on these mechanical equipment. However, the existing technology usually simply inputs the collected equipment data into the deep learning model for processing to obtain health monitoring results, which is not comprehensive enough and difficult to meet actual needs. Summary of the invention

[0003] The embodiment of the present application provides an abnormal warning system for equipment health monitoring, which can solve the problem that the prior art is not comprehensive enough and difficult to meet actual needs.

[0004] In a first aspect, an embodiment of the present application provides an abnormal warning system for equipment health monitoring, including: A data acquisition module, used to obtain initial operation data of the device to be monitored, determine an acquisition method for the device to be monitored based on the initial operation data, and acquire target operation data of the device to be monitored based on the acquisition method; The data analysis module is connected to the data acquisition module and is used to analyze the target operation data based on the current working conditions and the current environment to obtain the health monitoring results of the equipment to be monitored, and output warning information when the health monitoring results are detected to be abnormal.

[0005] Optionally, the data acquisition module is specifically used for: Acquiring the initial operation data, and determining abnormal points of the initial operation data; Performing anomaly detection on the abnormal point based on a trend recognition algorithm to obtain an abnormality level of the initial operation data; the trend recognition algorithm refers to an algorithm for analyzing the changing trend of the abnormal point with the set variable; Determining the collection method based on the abnormality level; Based on the collection method, data is collected on the device to be monitored to obtain the target operation data.

[0006] Optionally, determining the collection mode based on the abnormality level includes: If the abnormality level is the first level, the collection mode is determined to be a numerical collection mode; the numerical collection mode refers to collecting only the operating value of the device to be monitored; If the abnormality level is the second level, the acquisition mode is determined to be an additional waveform acquisition mode; the additional waveform acquisition means that the acquisition of waveform data is increased under the numerical acquisition mode; the abnormality level of the second level is higher than the abnormality level of the first level; If the abnormality level is the third level, it is determined that the acquisition method is a multi-point synchronous waveform acquisition method; the multi-point synchronous waveform acquisition means that under the numerical acquisition method, multiple measuring points of the monitored equipment are added to simultaneously acquire waveforms; the abnormality degree of the third level is higher than the abnormality degree of the second level.

[0007] Optionally, the data acquisition module is further used for: determining a density type of the target operating data; Analyze the target operation data based on the density type to obtain indicator data corresponding to the monitoring device; The target operation data and the indicator data are sent to the data analysis module.

[0008] Optionally, the analyzing the target operation data based on the density type to obtain the indicator data corresponding to the monitoring device includes: If the density type is the first density type, the traditional waveform index corresponding to the device to be monitored is calculated based on the target operation data; the first density type is used to describe that the target operation data is low-density data, and the low density is used to characterize that the target operation data contains a small amount of information per unit time, and the traditional waveform index is used to describe signal characteristics; If the density type is the second density type, the traditional waveform indicator and the set fault indicator corresponding to the monitored equipment are calculated based on the target operation data; the second density type is used to describe that the target operation data is high-density data, and the high density is used to characterize that the target operation data contains a large amount of information per unit time; the set fault indicator is different from the traditional waveform indicator.

[0009] Optionally, the data analysis module is specifically used for: Extracting features of the target operating data based on the current operating condition and the current environment to obtain feature information of the device to be monitored; Analyze the characteristic information based on the constructed equipment performance degradation knowledge graph to obtain performance degradation information of the equipment to be monitored; generating the health monitoring result based on the performance degradation information; If the health monitoring result is abnormal, the warning information is output.

[0010] Optionally, the extracting features of the target operating data based on the current operating condition and the current environment to obtain feature information of the device to be monitored includes: Based on the current environment and the current operating condition, determining a dictionary learning algorithm corresponding to the target operating data; The target operation data is subjected to feature extraction based on the dictionary learning algorithm to obtain the feature information.

[0011] Optionally, before analyzing the characteristic information based on the constructed equipment performance degradation knowledge graph to obtain the performance degradation trend information of the equipment to be monitored, the method further includes: Acquire a historical operation data set of the device to be monitored; the historical operation data set includes historical data of different data types when the device to be monitored is continuously operated under different working conditions and different environments, and historical data of different data types when the device to be monitored fails under different working conditions and different environments; Extracting features from each historical data in the historical operation data set to obtain a plurality of historical feature parameters; Analyze the plurality of characteristic parameters to obtain a data degradation trajectory of the device to be monitored; Performing correlation analysis on each historical data of the different data types to obtain the degree of interaction between each historical data of the different data types; Based on the historical operation data set, the data degradation trajectory and the degree of interaction, the equipment performance degradation knowledge graph is constructed.

[0012] Optionally, the performance degradation information includes a plurality of the data degradation trajectories and a degree of interaction between different operating data; and generating the health monitoring result based on the performance degradation information includes: Determining multiple health monitoring data based on the device type of the device to be monitored; Based on the degree of interaction between the different operating data, calculating the weight value of each of the multiple health monitoring data; Determining a target degradation trajectory corresponding to each of the plurality of health monitoring data from the plurality of data degradation trajectories; The health monitoring result is generated based on the weight values ​​of each of the plurality of health monitoring data and the target degradation trajectory.

[0013] Optionally, the data analysis module is further used for: Performing time domain analysis on the target operation data to obtain statistical features corresponding to the target operation data; Performing a correlation analysis on the target operation data and the indicator data to obtain a correlation degree between the target operation data and the indicator data; Inputting the statistical features and the indicator data into a health monitoring model for processing respectively, to obtain a first monitoring result corresponding to the target operation data and a second monitoring result of the indicator data; Based on the degree of correlation between the target operating data and the indicator data, the current operating condition and the current environment, the first monitoring result and the second monitoring result are adjusted to obtain the health monitoring result.

[0014] The abnormal warning system for equipment health monitoring provided by the embodiment of the present application has the following beneficial effects compared with the prior art: the data acquisition module determines the acquisition method by obtaining the initial operating data of the equipment to be monitored, which can ensure that the acquisition process is highly consistent with the equipment characteristics, greatly improves the pertinence of data acquisition, avoids the loss or redundancy of key data due to improper acquisition methods, and improves the data quality of the target operation data collected. The data analysis module analyzes the target operation data based on the current working conditions and the current environment, which can comprehensively and truly reflect the health status of the equipment, making the health monitoring results more reliable and practical. At the same time, the data analysis module outputs warning information in a timely manner when it detects that the health monitoring results are abnormal, so that equipment managers and maintenance personnel can understand the abnormal conditions of the equipment in a timely manner, so as to take corresponding measures to protect the equipment and prevent further deterioration of the fault. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0016] Figure 1 It is a structural diagram of an abnormal warning system for equipment health monitoring provided by an embodiment of the present application; Figure 2 is a structural diagram of an abnormal warning system for equipment health monitoring provided by another embodiment of the present application; Figure 3 This is a specific implementation flow chart of a data acquisition module in an abnormal warning system for equipment health monitoring provided by an embodiment of the present application; Figure 4 This is a specific implementation flow chart of a data acquisition module in an abnormal warning system for equipment health monitoring provided by another embodiment of the present application; Figure 5This is a specific implementation flow chart of a data analysis module in an abnormal warning system for equipment health monitoring provided by an embodiment of the present application; Figure 6 It is a specific implementation flow chart of the data analysis module in the abnormal warning system for equipment health monitoring provided by another embodiment of the present application. DETAILED DESCRIPTION

[0017] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0018] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0019] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0020] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0021] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0022] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0023] See also Figure 1 , Figure 1 This is a schematic diagram of the structure of an abnormal warning system for equipment health monitoring provided by an embodiment of the present application. For ease of explanation, only the parts related to this embodiment are shown, which are described in detail as follows: like Figure 1 As shown, the abnormal warning system 1 for equipment health monitoring includes: a data acquisition module 11 and a data analysis module 12, and the data acquisition module 11 is communicatively connected with the data analysis module 12. The above communication connection mode can be a wired communication connection or a wireless communication connection, which is not limited here.

[0024] Specifically, the data acquisition module 11 is used to obtain initial operation data of the device to be monitored, determine the acquisition method for the device to be monitored based on the initial operation data, and acquire target operation data of the device to be monitored based on the acquisition method.

[0025] The data analysis module 12 is used to analyze the target operating data based on the current working conditions and the current environment to obtain the health monitoring results of the equipment to be monitored, and output warning information when the health monitoring results are detected to be abnormal.

[0026] It should be noted that the equipment to be monitored includes but is not limited to motors, pumps, fans and other mechanical equipment.

[0027] In the embodiment of the present application, the data acquisition module 11 can continuously and in real time acquire the initial operation data of the monitored device during operation, wherein the initial operation data includes but is not limited to: temperature data, vibration data, sound data and other data.

[0028] In one implementation of the present application, please refer to Figure 2 , Figure 2 FIG. 1 is a schematic diagram of the structure of an abnormal warning system for equipment health monitoring provided by another embodiment of the present application. Figure 2 As shown, the abnormal warning system 1 for equipment health monitoring may further include a data sensing module 13. The data sensing module 13 is in communication connection with the data acquisition module 11.

[0029] In this embodiment, the data sensing module 13 is used to sense in real time the initial operation data generated by the monitored device during operation, and send the initial operation data to the data acquisition module 11 .

[0030] Based on this, the data acquisition module 11 can obtain the initial operation data of the device to be monitored in real time through the data perception module 13 set at the device to be monitored.

[0031] It should be noted that the data sensing module 13 can be installed on the bearing seat surface of the monitored equipment or other equipment surfaces that need to be monitored and are easy to install. The installation methods of the data sensing module 13 include but are not limited to: the combined action of magnetic attraction and adhesive, and the method of punching and fixing.

[0032] In practical applications, the data sensing module 13 can be various types of sensors, such as a temperature sensor, a vibration sensor, and a sound sensor.

[0033] In the embodiment of the present application, since the fault types of the equipment to be monitored include but are not limited to slow-changing types, fast-changing types, and sudden changes, in order to accurately obtain the operating data corresponding to each type of fault, the data acquisition module 11 can determine the collection frequency of the initial operating data corresponding to each fault type according to each fault type, so that the data acquisition module 11 obtains the corresponding initial operating data based on each collection frequency.

[0034] It should be noted that a slowly changing type of fault refers to a fault that develops slowly, with fault characteristic parameters gradually changing over time, and is usually a fault that becomes apparent only over a long time scale (such as months or years). For example, a fault caused by long-term wear and aging of equipment parts is a slowly changing type of fault.

[0035] Fast-changing faults refer to faults that change relatively quickly, with fault characteristic parameters changing significantly in a short period of time (such as a few minutes to a few hours). For example, faults caused by intermittent faults inside the equipment, sensor failure, or short-term abnormalities in the control system.

[0036] Sudden failure refers to a sudden failure of the equipment during operation, in which the characteristic parameters of the failure change dramatically in an instant (almost zero time interval), and the performance or state of the equipment changes from normal to abnormal or even completely fails immediately. Such failures include severe external impact, sudden breakage of key internal components, electrical short circuit, etc.

[0037] In an embodiment of the present application, the collection frequency of initial operating data corresponding to a slowly changing type of fault is lower than the collection frequency of initial operating data corresponding to a rapidly changing type of fault, and the collection frequency of initial operating data corresponding to a rapidly changing type of fault is lower than the collection frequency of initial operating data corresponding to a sudden change type of fault.

[0038] In the embodiment of the present application, after acquiring the initial operation data of the device to be monitored, the data acquisition module 11 can calculate the statistical characteristics (such as mean, standard deviation and peak value, etc.) corresponding to the initial operation data in each operation cycle, and determine the operation state of the device to be monitored at this time according to the statistical characteristics. Among them, the operation cycle can be determined according to actual needs and is not limited here.

[0039] Specifically, the data collection module 11 can input the corresponding statistical features in each operation cycle into the trained analysis model for processing to obtain data change information corresponding to the initial operation data, wherein the data change information is used to describe the change law of the initial operation data.

[0040] It should be noted that the above-mentioned analysis model can be obtained by training a pre-constructed neural network model based on a preset sample set. Among them, each sample data in the preset sample set includes the statistical characteristics of the sample operation data and the sample data change information corresponding to the statistical characteristics of the sample operation data. When training the pre-constructed neural network model, the statistical characteristics of the sample operation data in each sample data are used as the input of the neural network model, and the sample data change information corresponding to the statistical characteristics of the sample operation data in each sample data is used as the output of the neural network model. Through training, the neural network model can learn the corresponding relationship between the statistical characteristics of all possible sample operation data and the sample data change information, and use the trained neural network model as the analysis model.

[0041] Afterwards, the data acquisition module 11 can determine the operating status of the device to be monitored according to the data change information.

[0042] Specifically, when the data acquisition module 11 detects that the data fluctuation of the initial operating data is small and the changes are stable, it can be determined that the operating state of the equipment to be monitored is stable operation; when the data acquisition module 11 detects that the data fluctuation of the initial operating data is large and irregular, it can be determined that the operating state of the equipment to be monitored is unstable operation.

[0043] In the embodiment of the present application, the data collection module 11 may determine a method for collecting the operating data of the device to be monitored according to the operating status of the device to be monitored.

[0044] Specifically, when the data acquisition module 11 detects that the operating state of the monitored device is stable, it means that the probability of abnormality of the monitored device is small. Therefore, the data acquisition module 11 can determine that the acquisition mode of the monitored device is to collect data based on the first acquisition frequency. The first acquisition frequency can be determined according to actual needs and is not limited here.

[0045] When the data acquisition module 11 detects that the operating state of the monitored device is not stable, it means that the probability of abnormality of the monitored device is relatively high. Therefore, the data acquisition module 11 can determine that the collection method of the monitored device is to collect data based on the second collection frequency. The second collection frequency can be determined according to actual needs and is not limited here.

[0046] It should be noted that the second acquisition frequency is higher than the first acquisition frequency.

[0047] Based on this, the data acquisition module 11 can obtain the target operation data of the device to be monitored according to the determined acquisition mode set, and send the target operation data to the data analysis module 12. The target operation data includes but is not limited to: temperature data, vibration data, sound data, etc.

[0048] In the embodiment of the present application, after obtaining the target operation data, the data analysis module 12 may perform data preprocessing on the target operation data to improve the data quality, wherein the data preprocessing includes but is not limited to: data cleaning and data normalization.

[0049] Afterwards, the data analysis module 12 can analyze the target operating data after data preprocessing based on the current working conditions and the current environment to obtain the health monitoring results of the equipment to be monitored.

[0050] The current working condition includes but is not limited to the workload and working mode of the equipment to be monitored. The current specifically refers to the moment when the data acquisition module 11 acquires the target operating data.

[0051] In the embodiment of the present application, the data analysis module 12 can input the target operation data into the trained health analysis model for processing to obtain the initial monitoring results of the device to be monitored, wherein the initial monitoring results include but are not limited to normal and abnormal.

[0052] Normal is used to describe that the monitored device is in a healthy state, and abnormal is used to describe that the monitored device is in an abnormal state.

[0053] It should be noted that the health analysis model can be obtained by training a pre-constructed first deep learning model based on a preset sample set. Among them, each sample data in the preset sample set includes sample operation data and sample monitoring results corresponding to the sample operation data. When training the pre-constructed first deep learning model, the sample operation data in each sample data is used as the input of the first deep learning model, and the sample monitoring results corresponding to the sample operation data in each sample data are used as the output of the first deep learning model. Through training, the first deep learning model can learn the correspondence between all possible sample operation data and sample monitoring results, and use the trained first deep learning model as the health analysis model.

[0054] In actual applications, when the monitored equipment is in a high-load condition, the temperature, vibration and other data ranges of its normal operation may be increased accordingly. Therefore, in the embodiment of the present application, in order to improve the accuracy of health monitoring of the monitored equipment, the data analysis module 12 can obtain the historical operation data set of the monitored equipment, and build a correlation model between the working condition, environment and health monitoring results based on the historical operation data set, such as a multivariate linear regression model. Among them, the historical operation data set includes historical data of different data types and historical monitoring results of the monitored equipment under different working conditions and different environments when the monitored equipment continues to operate under different working conditions and different environments.

[0055] It should be noted that the multiple linear regression model is a statistical analysis method used to study the linear relationship between multiple independent variables and a dependent variable. Its core is to quantify the influence of variables through mathematical models and evaluate the effectiveness of the model based on hypothesis testing.

[0056] Afterwards, the data analysis module 12 can simultaneously input the initial monitoring results, the current working conditions and the current environment into the above-mentioned correlation model for fusion analysis to obtain the predicted monitoring results, and adjust the initial monitoring results according to the predicted monitoring results to obtain the final health monitoring results.

[0057] In the embodiments of the present application, health monitoring results include but are not limited to normal and abnormal.

[0058] Normal is used to describe that the monitored device is in a healthy state, and abnormal is used to describe that the monitored device is in an abnormal state.

[0059] It should be noted that when the health monitoring result is abnormal, the health monitoring result may also carry the abnormal type and abnormal cause, wherein the abnormal type includes but is not limited to: sub-health, failure, and danger.

[0060] Among them, sub-health is used to describe that the equipment to be monitored has early or mid-term damage, but the operating status is stable and can continue to operate. Fault is used to describe that the equipment to be monitored has mid- or late-term damage, or the deterioration speed is very fast, and short-term monitoring operation is required. Danger is used to describe that the equipment to be monitored has serious damage, and there is a risk of continuing to operate, and it needs to be stopped for inspection and repair as soon as possible.

[0061] Based on this, when the data analysis module 12 detects that the health monitoring result of the device to be monitored is abnormal, it can output warning information including the abnormality type and the cause of the abnormality.

[0062] From the above, it can be seen that the abnormal warning system for equipment health monitoring provided by this embodiment determines the collection method by obtaining the initial operation data of the equipment to be monitored through the data collection module, which can ensure that the collection process is highly consistent with the equipment characteristics, greatly improves the pertinence of data collection, avoids the loss or redundancy of key data due to improper collection methods, and improves the data quality of the target operation data collected. The data analysis module analyzes the target operation data based on the current working conditions and the current environment, which can comprehensively and truly reflect the health status of the equipment, making the health monitoring results more reliable and practical. Finally, the early warning module outputs early warning information in a timely manner when it detects that the health monitoring results are abnormal, so that equipment managers and maintenance personnel can understand the abnormal situation of the equipment in a timely manner, so as to take corresponding measures to protect the equipment and prevent further deterioration of the fault.

[0063] See also Figure 3 , Figure 3 This is a specific implementation flow chart of the data acquisition module in the abnormal warning system for equipment health monitoring provided by an embodiment of the present application. Figure 3 As shown, the data acquisition module can specifically acquire the target operation data by executing steps S101 to S104, as described in detail as follows: In S101, the initial operation data is acquired, and abnormal points of the initial operation data are determined.

[0064] In S102, anomaly detection is performed on the abnormal point based on a trend recognition algorithm to obtain an abnormality level of the initial operation data; the trend recognition algorithm refers to an algorithm for analyzing a trend of an abnormal point changing with a set variable.

[0065] In S103, the collection method is determined based on the abnormality level.

[0066] In S104, data is collected from the device to be monitored based on the collection method to obtain the target operation data.

[0067] In this embodiment, after the data acquisition module 11 obtains the initial operation data, since the initial operation data includes multiple different types of data (such as temperature data, vibration data, and sound data, etc.), the data acquisition module 11 can construct data curves of different types of data changing over time based on different types of data. Afterwards, the data acquisition module 11 can determine the data points in the different types of data curves that are greater than the corresponding set thresholds or suddenly change as abnormal points, thereby obtaining the abnormal points corresponding to the initial operation data. Among them, the set thresholds of different types can be determined according to actual needs, and are not limited here.

[0068] After obtaining the abnormal points of the initial operation data, the data acquisition module 11 can perform abnormality detection on the abnormal points based on the trend recognition algorithm, that is, determine the trend characteristics of the abnormal points, so as to obtain the abnormal level of the initial operation data. The trend recognition algorithm refers to an algorithm for analyzing the trend of abnormal points changing with the set variable. The set variable can be time.

[0069] In practical applications, trend identification algorithms include but are not limited to: exponentially weighted moving average method, Holt-Winters method, and Kalman filter algorithm.

[0070] In this embodiment, the trend feature is used to describe the degree of deviation between the abnormal point and the set value, including but not limited to: mild abnormality (i.e. mild deviation), moderate abnormality (i.e. moderate deviation) and severe abnormality (i.e. severe deviation). The set value can be determined according to actual needs and is not limited here.

[0071] In some possible embodiments, the set value may be determined based on the average value of a data group consisting of a set window size with the abnormal point as the center point, wherein the set window size may be determined based on actual needs and is not limited here.

[0072] It should be noted that the abnormality levels include but are not limited to the first level, the second level and the third level, wherein the abnormality level of the second level is higher than the abnormality level of the first level, and the abnormality level of the third level is higher than the abnormality level of the second level.

[0073] In this embodiment, the data acquisition module 11 can set the abnormality level of the abnormal point with a trend characteristic of mild abnormality to the first level, the abnormality level of the abnormal point with a trend characteristic of moderate abnormality to the second level, and the abnormality level of the abnormal point with a trend characteristic of severe abnormality to the third level.

[0074] In this embodiment, after determining the abnormality level of the abnormal point, the data collection module 11 may determine a collection method for the operating data of the monitored device according to the abnormality level.

[0075] It should be noted that the acquisition method includes but is not limited to a numerical acquisition method, an additional waveform acquisition method, and a multi-point synchronous additional waveform acquisition method. Among them, the numerical acquisition method refers to only acquiring the operating values ​​of the equipment to be monitored, that is, the actual values ​​corresponding to the operating data. The additional waveform acquisition refers to the acquisition of additional waveform data of the equipment to be monitored under the numerical acquisition method. The multi-point synchronous additional waveform acquisition refers to the simultaneous acquisition of waveforms at multiple measuring points of the equipment to be monitored under the numerical acquisition method.

[0076] In this embodiment, when the data acquisition module 11 detects that the abnormality level of the abnormal point is the first level, it means that the monitored device has a slight abnormality at this time, but can still continue to run. Therefore, in order to avoid occupying memory, the data acquisition module 11 can determine that the collection method for the monitored device at this time is a numerical collection method.

[0077] When the data acquisition module 11 detects that the abnormality level is the second level, it means that the monitored device has a moderate abnormality. Although it can continue to operate, it may be prone to failure. Therefore, in order to improve the accuracy of health monitoring of the monitored device, the data acquisition module 11 can determine that the acquisition method is the waveform acquisition method.

[0078] When the data acquisition module 11 detects that the abnormal level is the third level, it means that the monitored equipment has a serious abnormality and there is a risk of continuing to operate, so it needs to be stopped for inspection as soon as possible. Therefore, in order to improve the accuracy of health monitoring of the monitored equipment and enable subsequent maintenance personnel to perform corresponding maintenance on the monitored equipment, the data acquisition module 11 can determine that the acquisition method is a multi-point synchronous waveform acquisition method.

[0079] In this embodiment, after determining the collection method for the operating data of the monitored device, the data collection module 11 can directly collect data from the monitored device according to the collection method to obtain the final target operating data.

[0080] From the above, it can be seen that the data acquisition module provided by this embodiment obtains the initial operation data and determines the abnormal points of the initial operation data; performs abnormal detection on the abnormal points based on the trend recognition algorithm to obtain the abnormal level of the initial operation data; the trend recognition algorithm refers to an algorithm used to analyze the changing trend of the abnormal points with the set variables; determines the collection method based on the abnormal level; and collects data from the monitored equipment based on the collection method to obtain the target operation data. This embodiment can dynamically adjust the collection method according to the actual condition of the equipment by detecting the abnormal points of the initial operation data and classifying the abnormal levels. At the same time, the abnormal level is determined based on the trend recognition algorithm, and the changing trend of the abnormal points with the set variables is fully considered. Compared with single data judgment, it can more comprehensively and accurately evaluate the degree of equipment abnormality.

[0081] See also Figure 4 , Figure 4 This is a specific implementation flow chart of the data acquisition module in the abnormal warning system for equipment health monitoring provided by another embodiment of the present application. Figure 4 As shown, after obtaining the target operation data, the data acquisition module can also execute steps S201 to S203 to acquire the target operation data, as described in detail as follows: In S201 , the density type of the target operating data is determined.

[0082] In S202, the target operation data is analyzed based on the density type to obtain indicator data corresponding to the monitoring device.

[0083] In S203, the target operation data and the indicator data are sent to the data analysis module.

[0084] It should be noted that the target operation data includes data of various different data types (such as temperature, vibration, and sound, etc.).

[0085] In this embodiment, the data acquisition module 11 can record the collection frequencies corresponding to data of multiple different data types while collecting the target operation data. Therefore, the data acquisition module 11 can determine the density types of data of multiple different data types in the target operation data based on the collection frequencies of data of multiple different data types in the target operation data.

[0086] The density type includes but is not limited to: a first density type and a second density type. The first density type is used to describe that the target operation data is low-density data, and low density is used to characterize that the target operation data contains a small amount of information per unit time. The second density type is used to describe that the target operation data is high-density data, and high density is used to characterize that the target operation data contains a large amount of information per unit time.

[0087] It can be understood that a small amount of information means that when the frequency of data collection is low, the amount of data collected per unit time is small, and the details and changes of the operating status of the equipment to be monitored that can be reflected by these data are relatively limited, so it is called containing a small amount of information. For example, if a certain data type collects data only once every 10 minutes, and each collection only has a simple value or a few parameters, then within a unit time (such as 1 hour), only 6 data points of this data type are obtained, and the information carried by each data point is limited, and it is impossible to fully and carefully describe the operating status of the equipment to be monitored. This is low-density data containing a small amount of information.

[0088] A large amount of information means that when the frequency of data collection is high, a large number of data points can be collected per unit time, and each data point may contain parameters of multiple dimensions or complex content. These data can more comprehensively and accurately reflect the various states and changes of the operation of the monitored equipment, that is, contain a large amount of information. For example, for another data type, 100 data points can be collected per second, and each data point contains multiple parameters, such as temperature, humidity, pressure, etc., then there will be 360,000 data points in 1 hour. These large number of data points can present various information in the target operation process in detail, which belongs to high-density data containing a large amount of information.

[0089] For any data type of the target operation data, when the data acquisition module 11 detects that the acquisition frequency of the data of the data type is less than or equal to the first threshold, it can determine that the density type of the data of the data type is the first density type; when the data acquisition module 11 detects that the acquisition frequency of the data of the data type is greater than the first threshold, it can determine that the density type of the data of the data type is the second density type. The first threshold can be determined according to actual needs and is not limited here.

[0090] In this embodiment, after determining the density type of the target operation data, the data acquisition module 11 can analyze the target operation data based on the density type to obtain the index data corresponding to the monitoring device, wherein the index data includes but is not limited to traditional waveform index and set fault index.

[0091] It should be noted that traditional waveform indicators are used to describe signal characteristics, such as peak value, valley value, average value, period, phase, etc.

[0092] The set fault indicators include but are not limited to bearing fault indicators and loose fault indicators.

[0093] Bearing fault indicators include but are not limited to: vibration indicators (such as acceleration, velocity, and displacement) and temperature indicators (such as bearing temperature and temperature change rate).

[0094] Looseness fault indicators include but are not limited to: vibration amplitude and vibration frequency, etc.

[0095] In this embodiment, when the data acquisition module 11 detects that the density type of the target operating data is the first density type, it means that its collection frequency is low, that is, the probability of an abnormality in the monitored equipment is small at this time, so there is no need to frequently collect the operating data of the monitored equipment. Therefore, in order to improve work efficiency, the data acquisition module 11 only needs to calculate the traditional waveform indicators corresponding to the monitored equipment based on the target operating data.

[0096] When the data acquisition module 11 detects that the density type of the target operating data is the second density type, it means that its collection frequency is relatively high. That is to say, the probability of an abnormality occurring in the monitored equipment is relatively high at this time, which requires frequent collection of the operating data of the monitored equipment. Therefore, in order to improve the accuracy of health monitoring, the data acquisition module 11 can calculate the traditional waveform indicators and set fault indicators corresponding to the monitored equipment based on the target operating data.

[0097] In this embodiment, after obtaining the target operating data and indicator data, the data acquisition module 11 can send the target operating data and indicator operating data to the data analysis module 12, so that the data analysis module 12 can obtain more accurate health monitoring results based on the target operating data and indicator data.

[0098] From the above, it can be seen that after obtaining the target operation data, the data acquisition module provided in this embodiment can determine the density type of the target operation data; analyze the target operation data based on the density type to obtain the indicator data corresponding to the monitoring equipment; and send the target operation data and the indicator data to the data analysis module, thereby improving the accuracy of the health monitoring results obtained by the subsequent data analysis module combining the target operation data and the indicator data.

[0099] See also Figure 5 , Figure 5 This is a specific implementation flow chart of the data analysis module in the abnormal warning system for equipment health monitoring provided by an embodiment of the present application. Figure 5 As shown, the data analysis module can specifically output warning information by executing steps S301 to S304, which are described in detail as follows: In S301, feature extraction is performed on the target operating data based on the current operating condition and the current environment to obtain feature information of the device to be monitored.

[0100] In this embodiment, the data analysis module 12 can determine the feature extraction method that matches the environment of the monitored device according to the current environment. The feature extraction methods include but are not limited to: feature extraction methods based on wavelet transform and feature extraction methods based on short-time Fourier transform.

[0101] The data analysis module 12 can determine the set features that match the working conditions of the equipment to be monitored at this time according to the current working conditions. The set features can be determined according to actual needs and are not limited here.

[0102] Therefore, in this embodiment, the data analysis module 12 can extract set features matching the current working conditions from the target operating data according to a feature extraction method matching the current environment, thereby obtaining feature information of the equipment to be monitored.

[0103] In one embodiment of the present application, the data analysis module may also perform the following steps S301, which are described in detail as follows: Based on the current environment and the current operating condition, determining a dictionary learning algorithm corresponding to the target operating data; The target operation data is subjected to feature extraction based on the dictionary learning algorithm to obtain the feature information.

[0104] In this embodiment, the data analysis module 12 pre-stores the correspondence between different working conditions, different environments and different dictionary learning algorithms. Therefore, the data analysis module 12 can determine the dictionary learning algorithm corresponding to the target operating data based on the current environment and current working condition of the equipment to be monitored and the above correspondence. Afterwards, the data analysis module 12 can extract features from the target operating data based on the dictionary learning algorithm to obtain feature information.

[0105] Exemplarily, assuming that the equipment to be monitored is in a situation where the working conditions are changeable, the data analysis module 12 can determine a dictionary learning algorithm that can quickly adapt to data changes.

[0106] In practical applications, dictionary learning algorithms include but are not limited to: online dictionary learning algorithms and dictionary learning algorithms based on sparse coding. Among them, the online dictionary learning algorithm has the ability to update the dictionary in real time, which is suitable for situations where data changes in real time and working conditions are unstable, and can quickly adapt to new data features. Sparse coding-based dictionary learning algorithm: emphasizes the sparsity of data, learns the dictionary by minimizing the sparse representation error, and is suitable for scenarios where sparse features of data need to be extracted.

[0107] In S302, the characteristic information is analyzed based on the constructed equipment performance degradation knowledge graph to obtain performance degradation information of the equipment to be monitored.

[0108] In this embodiment, the equipment performance degradation knowledge graph includes information such as failure modes, performance indicators, working environment factors, and their interrelationships. For example, the equipment performance degradation knowledge graph records that under high temperature and high humidity conditions, the bearings of rolling equipment are prone to wear failures, and bearing wear will lead to changes in performance indicators such as increased vibration amplitude and increased temperature.

[0109] In one embodiment of the present application, the data analysis module 12 can specifically construct and obtain a knowledge graph of equipment performance degradation according to the following steps, which are described in detail as follows: Acquire a historical operation data set of the device to be monitored; the historical operation data set includes historical data of different data types when the device to be monitored is continuously operated under different working conditions and different environments, and historical data of different data types when the device to be monitored fails under different working conditions and different environments; Extracting features from each historical data in the historical operation data set to obtain a plurality of historical feature parameters; Analyze the plurality of characteristic parameters to obtain a data degradation trajectory of the device to be monitored; Performing correlation analysis on each historical data of the different data types to obtain the degree of interaction between each historical data of the different data types; Based on the historical operation data set, the data degradation trajectory and the degree of interaction, the equipment performance degradation knowledge graph is constructed.

[0110] In this embodiment, after obtaining the historical operation data set, the data analysis module 12 can extract time domain features from any historical data in the historical operation data set, that is, calculate and obtain time domain feature parameters of any historical data, wherein the time domain feature parameters include but are not limited to: mean and peak value.

[0111] Afterwards, the data analysis module 12 can convert the target operation data into frequency domain data according to a frequency domain analysis method (such as Fourier transform and wavelet transform, etc.), and extract its corresponding frequency domain characteristic parameters. The frequency domain characteristic parameters include but are not limited to: power spectrum density, amplitude and phase of harmonic frequency, etc.

[0112] Afterwards, the data analysis module 12 may also obtain characteristic parameters of the target operation data in the time-frequency joint domain according to a time-frequency analysis method (such as short-time Fourier transform, wavelet packet transform, etc.).

[0113] Based on this, the data analysis module 12 can determine the above-mentioned time domain characteristic parameters, frequency domain characteristic parameters and characteristic parameters of the time-frequency joint domain as multiple historical characteristic parameters.

[0114] In this embodiment, the data analysis module 12 can divide the above multiple historical characteristic parameters according to the collection time, working conditions and fault occurrence time corresponding to each historical data of the monitored equipment to obtain a first data set of different equipment states. Among them, the equipment state includes: normal operation, early failure, medium-term failure and serious failure).

[0115] Exemplarily, the data analysis module 12 may group the historical characteristic parameters of the monitored equipment in stable operation over a period of time into a group and mark them as “normal operation”; and group the historical characteristic parameters of the monitored equipment in a period of time before a failure occurs into a group and mark them as “early failure”.

[0116] Afterwards, for historical data of the same data type in the first data set of any different device states, the data analysis module 12 can input the characteristic parameters of the same data type into the trained degradation analysis model for analysis to obtain the data degradation trajectory corresponding to the characteristic parameters of the same data type.

[0117] Based on this, the data analysis module 12 can obtain multiple data degradation trajectories corresponding to the historical operation data set, and determine all of the above data degradation trajectories as data degradation trajectories of the equipment to be monitored.

[0118] It should be noted that the degradation analysis model can be obtained by training a pre-constructed second deep learning model based on a preset sample set. Each sample data in the preset sample set includes a sample feature parameter and a sample degradation trajectory corresponding to the sample feature parameter. When training the pre-constructed second deep learning model, the sample feature parameters in each sample are used as the input of the second deep learning model, and the sample degradation trajectory corresponding to the sample feature parameters in each sample is used as the output of the second deep learning model. Through training, the second deep learning model can learn the correspondence between all possible sample feature parameters and sample degradation trajectories, and the trained second deep learning model is used as the degradation analysis model.

[0119] In this embodiment, the data analysis module 12 may divide the historical operation data set into second data sets of different data types, wherein each second data set only includes historical data of the same data type.

[0120] Afterwards, the data analysis module 12 can perform correlation analysis on each historical data of different data types, that is, each second data set of different data types, according to the correlation analysis method to obtain the degree of interaction between each historical data of different data types. The degree of interaction is used to describe the degree of association between each historical data of different data types.

[0121] In practical applications, correlation analysis methods include but are not limited to: Pearson correlation coefficient, Spearman rank correlation coefficient, and mutual information.

[0122] It should be noted that when the correlation analysis method is the Pearson correlation coefficient, the degree of interaction is the actual value corresponding to the Pearson correlation coefficient; when the correlation analysis method is the Spearman rank correlation coefficient, the degree of interaction is the actual value corresponding to the Spearman rank correlation coefficient; when the correlation analysis method is the mutual information, the degree of interaction is the actual value corresponding to the mutual information.

[0123] In this embodiment, the data analysis module 12 can determine the basic framework of the knowledge graph, including nodes and edges. Among them, the nodes may include various equipment components of the equipment to be monitored (such as bearings, gears, motors, etc.), failure modes (such as wear, fatigue, fracture, etc.), performance indicators (such as vibration amplitude, current effective value, temperature, etc.), working environment factors (such as load, ambient temperature, humidity, etc.) and time nodes, etc. Edges are used to represent the relationship between different nodes, such as "leading to" (indicating the causal relationship between the failure mode and the performance indicator), "influence" (indicating the influence relationship between the working environment factors and the equipment components or performance indicators), "association" (indicating the correlation relationship between different performance indicators), "occurrence in" (indicating the time relationship between the failure mode and the time node), etc.

[0124] Afterwards, the data analysis module 12 can extract nodes and edges of the knowledge graph from the historical operating data set of the equipment to be monitored, the data degradation trajectory, and the degree of interaction between each historical data of different data types. For nodes, equipment components, failure modes, performance indicators, operating environment factors, etc. are added to the knowledge graph as entity nodes, and each node is assigned a unique identifier and related attributes (such as the model of the equipment component, the description of the failure mode, the numerical range of the performance indicator, etc.). For edges, the relationship between different nodes is determined according to the data degradation trajectory and the degree of interaction, and the corresponding edges are added. For example, according to the data degradation trajectory, it is found that bearing wear causes an increase in vibration amplitude, then a "cause" edge is created in the knowledge graph from the "bearing wear" node to the "increase in vibration amplitude" node.

[0125] In this embodiment, the data analysis module 12 can match the feature information obtained through feature extraction with the nodes and relationships in the constructed equipment performance degradation knowledge graph one by one to determine the performance degradation information matching the feature information. The performance degradation information includes the data degradation trajectory associated with different feature information and the degree of interaction between different operating data associated with different feature information.

[0126] In S303, the health monitoring result is generated based on the performance degradation information.

[0127] In S304, if the health monitoring result is abnormal, the warning information is output.

[0128] In this embodiment, the data analysis module 12 may input the above performance degradation information into a trained first monitoring model for processing to obtain a health monitoring result of the device to be monitored.

[0129] It should be noted that the first monitoring model can be obtained by training a pre-constructed third deep learning model based on a preset sample set. Among them, each sample data in the preset sample set includes sample performance degradation information and a sample monitoring result corresponding to the sample performance degradation information. When training the pre-constructed third deep learning model, the sample performance degradation information in each sample is used as the input of the third deep learning model, and the sample monitoring result corresponding to the sample performance degradation information in each sample is used as the output of the third deep learning model. Through training, the third deep learning model can learn the correspondence between all possible sample performance degradation information and sample monitoring results, and use the trained third deep learning model as the first monitoring model.

[0130] In one embodiment of the present application, when the performance degradation information includes multiple data degradation trajectories and the degree of interaction between different operating data, the data analysis module 12 may specifically generate the health monitoring result according to the following steps, which are described in detail as follows: Determining multiple health monitoring data based on the device type of the device to be monitored; Based on the degree of interaction between the different operating data, calculating the weight value of each of the multiple health monitoring data; Determining a target degradation trajectory corresponding to each of the plurality of health monitoring data from the plurality of data degradation trajectories; The health monitoring result is generated based on the weight values ​​of each of the plurality of health monitoring data and the target degradation trajectory.

[0131] In actual applications, the health monitoring data corresponding to different types of equipment are not exactly the same. For example, for rotating equipment (such as motors, fans, etc.), vibration, speed, temperature and other data are important health monitoring data; for electrical equipment (such as transformers, distribution cabinets, etc.), current, voltage, power factor and other data are important health monitoring data. Therefore, in this embodiment, the data analysis module 12 can pre-store the corresponding relationship between equipment of different types and health monitoring data.

[0132] In this embodiment, after obtaining the performance degradation information, the data analysis module 12 can determine a plurality of health monitoring data corresponding to the device to be monitored according to the device type of the device to be monitored and the above-mentioned pre-stored corresponding relationship.

[0133] It should be noted that the greater the degree of interaction between a certain operation data and another operation data, the deeper the influence between the certain operation data and the other operation data, that is, the more important the certain operation data is to the other operation data.

[0134] Therefore, the data analysis module 12 can calculate the weight values ​​of the multiple health monitoring data based on the degree of interaction between the different operation data. The greater the degree of interaction, the greater the weight value of the corresponding health monitoring data.

[0135] In this embodiment, after obtaining the performance degradation information, the data analysis module 12 may determine the target degradation trajectory corresponding to each of the plurality of health monitoring data from the plurality of data degradation trajectories included in the performance degradation information.

[0136] Afterwards, the data analysis module 12 can perform feature extraction and screening on the obtained target degradation trajectory. Specifically, the data analysis module 12 can extract key parameters in the target degradation trajectory, such as the slope, inflection point, fluctuation amplitude, etc. in the trajectory. Afterwards, the data analysis module 12 can screen out key parameters that are of great significance to the health monitoring of the equipment to be monitored based on the operating characteristics of the equipment to be monitored and the corresponding fault diagnosis requirements. For example, for the target degradation trajectory of vibration data, focus on characteristic parameters such as the growth trend of the vibration amplitude over time and the frequency and amplitude of abnormal fluctuations. These parameters can reflect whether the vibration state of the equipment to be monitored is normal and whether there is a potential risk of failure.

[0137] In this embodiment, the data analysis module 12 can perform a weighted summation of the weight values ​​of each health monitoring data and its corresponding key parameters to obtain a final health assessment value. Afterwards, the data analysis module 12 can obtain the health monitoring result of the device to be monitored based on the health assessment value and the pre-stored health grading standard.

[0138] The health classification standard may include four levels, namely, health, sub-health, failure and danger, and the four levels correspond to different evaluation value ranges. Among them, sub-health, failure and danger may be collectively referred to as abnormality.

[0139] In this embodiment, the data analysis module 12 can determine the target range of the health assessment value, and determine the level corresponding to the target range as the health monitoring result of the device to be monitored.

[0140] In this embodiment, the health monitoring results include but are not limited to normal and abnormal.

[0141] It should be noted that when the health monitoring result is abnormal, the health monitoring result may also carry the abnormal type and abnormal cause, wherein the abnormal type includes but is not limited to: sub-health, failure, and danger.

[0142] Among them, sub-health is used to describe that the equipment to be monitored has early or mid-term damage, but the operating status is stable and can continue to operate. Fault is used to describe that the equipment to be monitored has mid- or late-term damage, or the deterioration speed is very fast, and short-term monitoring operation is required. Danger is used to describe that the equipment to be monitored has serious damage, and there is a risk of continuing to operate, and it needs to be stopped for inspection and repair as soon as possible.

[0143] Based on this, when the data analysis module 12 detects that the health monitoring result of the device to be monitored is abnormal, it can output warning information including the abnormality type and the cause of the abnormality.

[0144] From the above, it can be seen that the data analysis module provided in this embodiment extracts features of the target operating data in combination with the current operating conditions and environment, and can accurately adapt to the actual operating status of the equipment; then, with the help of the constructed equipment performance degradation knowledge graph to analyze the feature information, possible situations of equipment performance degradation can be quickly located; finally, health monitoring results are generated based on the performance degradation information to make the health assessment more targeted.

[0145] See also Figure 6 , Figure 6 This is a specific implementation flow chart of the data analysis module in the abnormal warning system for equipment health monitoring provided by another embodiment of the present application. Figure 6 As shown, after obtaining the target operation data and the indicator data, the data analysis module can also perform steps S401 to S404, which are described in detail as follows: In S401, a time domain analysis is performed on the target operation data to obtain statistical features corresponding to the target operation data.

[0146] In S402, correlation analysis is performed on the target operation data and the indicator data to obtain the correlation degree between the target operation data and the indicator data.

[0147] In S403, the statistical features and the indicator data are respectively input into a health monitoring model for processing to obtain a first monitoring result corresponding to the target operation data and a second monitoring result of the indicator data.

[0148] In S404, based on the correlation between the target operating data and the indicator data, the current operating condition and the current environment, the first monitoring result and the second monitoring result are adjusted to obtain the health monitoring result.

[0149] It should be noted that the target operation data includes data of multiple different data types (such as temperature, vibration, and sound, etc.).

[0150] In this embodiment, the data analysis module 12 can perform time domain analysis on the target operation data to obtain statistical features corresponding to the target operation data, wherein the statistical features include but are not limited to: mean value and peak value.

[0151] Afterwards, the data analysis module 12 may analyze the correlation between data of different data types in the target operation data and the indicator data according to a correlation analysis method to obtain the degree of correlation between the target operation data and the indicator data.

[0152] In practical applications, correlation analysis methods include but are not limited to: Pearson correlation coefficient, Spearman rank correlation coefficient, and mutual information.

[0153] It should be noted that when the correlation analysis method is the Pearson correlation coefficient, the degree of interaction is the actual value corresponding to the Pearson correlation coefficient; when the correlation analysis method is the Spearman rank correlation coefficient, the degree of interaction is the actual value corresponding to the Spearman rank correlation coefficient; when the correlation analysis method is the mutual information, the degree of interaction is the actual value corresponding to the mutual information.

[0154] In this embodiment, the data analysis module 12 can input the statistical characteristics and the indicator data into the health monitoring model for processing, and obtain the first monitoring result corresponding to the target operation data and the second monitoring result of the indicator data.

[0155] It should be noted that the health monitoring model can be obtained by training a pre-constructed fourth deep learning model based on a preset sample set. Among them, each sample data in the preset sample set includes sample information (statistical features or indicator data) and the sample monitoring results corresponding to the sample information (the first monitoring results corresponding to the statistical features or the second monitoring results corresponding to the indicator data. When training the pre-constructed fourth deep learning model, the sample information in each sample is used as the input of the fourth deep learning model, and the sample monitoring results corresponding to the sample information in each sample are used as the output of the fourth deep learning model. Through training, the fourth deep learning model can learn the correspondence between all possible sample information and sample monitoring results, and the trained fourth deep learning model is used as the health monitoring model.

[0156] In this embodiment, the data analysis module 12 can determine the first weight corresponding to the first monitoring result according to the correlation between the target operation data and the indicator data. The higher the correlation, the greater the first weight. Afterwards, the data analysis module 12 can determine the second weight corresponding to the second monitoring result according to the first weight.

[0157] It should be noted that the first weight+the second weight=1.

[0158] In this embodiment, the data analysis module 12 can process the current working condition and the current environment input information quantization model to obtain the adjustment coefficients corresponding to the current working condition and the current environment.

[0159] The information quantization model may be obtained by training a pre-constructed fifth deep learning model based on a preset sample set. Each sample data in the preset sample set includes a sample parameter (sample working condition and sample environment) and a sample coefficient corresponding to the sample parameter. When training the pre-constructed fifth deep learning model, the sample parameters in each sample are used as the input of the fifth deep learning model, and the sample coefficient corresponding to the sample parameters in each sample is used as the output of the fifth deep learning model. Through training, the fifth deep learning model can learn the correspondence between all possible sample parameters and sample coefficients, and the trained fifth deep learning model is used as the information quantization model.

[0160] In this embodiment, the data analysis module 12 can perform weighted summation on the above-mentioned first monitoring result, first weight, second monitoring result, second weight and adjustment coefficient to determine the health score.

[0161] Afterwards, the data analysis module 12 can obtain the health monitoring result of the device to be monitored according to the health score and the pre-stored health grading standard.

[0162] The health classification standard may include four levels, namely, health, sub-health, failure and danger, and the four levels correspond to different evaluation value ranges. Among them, sub-health, failure and danger may be collectively referred to as abnormality.

[0163] In this embodiment, the data analysis module 12 can determine the target range of the health score, and determine the level corresponding to the target range as the health monitoring result of the device to be monitored.

[0164] In this embodiment, the health monitoring results include but are not limited to normal and abnormal.

[0165] It should be noted that when the health monitoring result is abnormal, the health monitoring result may also carry the abnormal type and abnormal cause, wherein the abnormal type includes but is not limited to: sub-health, failure, and danger.

[0166] Among them, sub-health is used to describe that the equipment to be monitored has early or mid-term damage, but the operating status is stable and can continue to operate. Fault is used to describe that the equipment to be monitored has mid- or late-term damage, or the deterioration speed is very fast, and short-term monitoring operation is required. Danger is used to describe that the equipment to be monitored has serious damage, and there is a risk of continuing to operate, and it needs to be stopped for inspection and repair as soon as possible.

[0167] Based on this, when the data analysis module 12 detects that the health monitoring result of the device to be monitored is abnormal, it can output warning information including the abnormality type and the cause of the abnormality.

[0168] From the above, it can be seen that the data analysis module provided in this embodiment obtains statistical characteristics by performing time domain analysis on the target operation data, and can accurately extract the basic characteristic information of the equipment operation; then, a correlation analysis is performed on the target operation data and the indicator data to clearly present the relationship between the two; finally, the first monitoring result and the second monitoring result are adjusted according to the degree of correlation between the target operation data and the indicator data, the current operating conditions and the current environment, so that the final health monitoring result is as close to the actual operation of the equipment as possible.

[0169] In one embodiment of the present application, the abnormal warning system for equipment health monitoring also supports a dual alarm system of equipment adaptive threshold + manual threshold.

[0170] Specifically, the equipment adaptive threshold alarm system specifically refers to: having adaptive threshold learning capabilities for on-site service equipment, that is, after the monitored equipment is communicatively connected with the abnormal warning system of equipment health monitoring, the abnormal warning system of equipment health monitoring can collect, learn and adaptively match the parameters of the normal operating state of the equipment within 20 to 30 days. When the working value of the equipment shows a trend of increasing, the abnormal warning system of equipment health monitoring can automatically issue an alarm.

[0171] The manual threshold alarm system opens the function of autonomously adjusting the alarm threshold at the back end when the user needs it, so as to better fit the user's on-site monitoring of the actual operation of each device, and the alarm accuracy can be effectively improved.

[0172] Through the multiple guarantees of adaptive equipment threshold learning + manual threshold setting + automatic alarm, the false alarm rate of previous fixed threshold alarms for equipment failures in on-site service has been greatly reduced, the workload of on-site equipment management personnel has been reduced, and the intelligent management and control of on-site equipment has been better assisted.

[0173] In another embodiment of the present application, combined with the dual alarm system of the above-mentioned device adaptive threshold + manual threshold, the data analysis module in the abnormal warning system of the equipment health monitoring can be in a dormant state when the equipment is working normally. When the data acquisition module detects that the value of a certain data type in the monitored equipment shows a trend of increasing, the abnormal warning system of the equipment health monitoring uses the dual alarm system of the device adaptive threshold + manual threshold. After detecting that the above-mentioned value reaches the alarm condition, the data analysis module will be awakened. The data analysis module can be combined with the data information corresponding to the above-mentioned value in the database of the abnormal warning system of the equipment health monitoring to realize automatic diagnosis of whether the monitored equipment has fault degradation. After degradation occurs, the data analysis module can automatically push diagnostic information related to the monitored equipment.

[0174] In another embodiment of the present application, the abnormal warning system for equipment health monitoring may also include an equipment three-dimensional digital twin module, which can realize the equipment three-dimensional digital twin function to display the operating status of the equipment to be monitored and various monitoring data in real time. The equipment three-dimensional digital twin module can accurately locate the faulty equipment to the part level, and send the twin model corresponding to the equipment to the display device, and the faulty parts can flash on the twin model corresponding to the equipment, so that the user can intuitively control the fault location of the faulty equipment on the display device.

[0175] In another embodiment of the present application, the abnormal warning system for equipment health monitoring may also include a data monitoring module. The data monitoring module can monitor the data transmission link to ensure the security of data transmission at each node. When the data monitoring module detects a signal interruption between the data perception module and the data acquisition module, it can accurately capture the abnormal communication node to ensure convenient troubleshooting and maintenance when the system component fails. The specific functions are as follows: Monitor the transmission signal between the data perception module and the data acquisition module, and the interruption can be located in time; Monitor the transmission signal between the data acquisition module and the data analysis module, and the interruption can be located in time; Monitor the battery power of the data sensing module and issue a low-battery reminder in time to avoid data transmission interruption.

[0176] At the same time, the data perception module, data acquisition module and data analysis module also have built-in "watchdog" circuits, which can automatically restart themselves when a signal interruption failure occurs in the data perception module, data acquisition module or data analysis module, reducing the maintenance workload of maintenance personnel and ensuring safer and more stable operation of the system.

[0177] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0178] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0179] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. An abnormal warning system for equipment health monitoring, characterized in that: include: A data acquisition module, used to obtain initial operation data of the device to be monitored, determine an acquisition method for the device to be monitored based on the initial operation data, and acquire target operation data of the device to be monitored based on the acquisition method; The data analysis module is connected to the data acquisition module and is used to analyze the target operation data based on the current working conditions and the current environment to obtain the health monitoring results of the equipment to be monitored, and output warning information when the health monitoring results are detected to be abnormal.

2. The abnormal warning system for equipment health monitoring according to claim 1, characterized in that: The data acquisition module is specifically used for: Acquiring the initial operation data, and determining abnormal points of the initial operation data; Performing anomaly detection on the abnormal point based on a trend recognition algorithm to obtain an abnormality level of the initial operation data; The trend identification algorithm refers to an algorithm used to analyze the changing trend of abnormal points with set variables; Determining the collection method based on the abnormality level; Based on the collection method, data is collected on the device to be monitored to obtain the target operation data.

3. The abnormal warning system for equipment health monitoring according to claim 2, characterized in that: The determining the collection method based on the abnormality level includes: If the abnormality level is the first level, the collection mode is determined to be a numerical collection mode; the numerical collection mode refers to collecting only the operating value of the device to be monitored; If the abnormality level is the second level, the acquisition mode is determined to be an additional waveform acquisition mode; the additional waveform acquisition means that the acquisition of waveform data is increased under the numerical acquisition mode; the abnormality level of the second level is higher than the abnormality level of the first level; If the abnormality level is the third level, it is determined that the acquisition method is a multi-point synchronous waveform acquisition method; the multi-point synchronous waveform acquisition means that under the numerical acquisition method, multiple measuring points of the monitored equipment are added to simultaneously acquire waveforms; the abnormality degree of the third level is higher than the abnormality degree of the second level.

4. The abnormal warning system for equipment health monitoring according to claim 1, characterized in that: The data acquisition module is also used for: determining a density type of the target operating data; Analyze the target operation data based on the density type to obtain indicator data corresponding to the monitoring device; The target operation data and the indicator data are sent to the data analysis module.

5. The abnormal warning system for equipment health monitoring according to claim 4, characterized in that: The analyzing the target operation data based on the density type to obtain the indicator data corresponding to the monitoring device includes: If the density type is the first density type, the traditional waveform index corresponding to the device to be monitored is calculated based on the target operation data; the first density type is used to describe that the target operation data is low-density data, and the low density is used to characterize that the target operation data contains a small amount of information per unit time, and the traditional waveform index is used to describe signal characteristics; If the density type is the second density type, the traditional waveform indicator and the set fault indicator corresponding to the monitored equipment are calculated based on the target operation data; the second density type is used to describe that the target operation data is high-density data, and the high density is used to characterize that the target operation data contains a large amount of information per unit time; the set fault indicator is different from the traditional waveform indicator.

6. The abnormal warning system for equipment health monitoring according to any one of claims 1 to 5, characterized in that: The data analysis module is specifically used for: Extracting features of the target operating data based on the current operating condition and the current environment to obtain feature information of the device to be monitored; Analyze the characteristic information based on the constructed equipment performance degradation knowledge graph to obtain performance degradation information of the equipment to be monitored; generating the health monitoring result based on the performance degradation information; If the health monitoring result is abnormal, the warning information is output.

7. The abnormal warning system for equipment health monitoring according to claim 6, characterized in that: The extracting features of the target operating data based on the current operating condition and the current environment to obtain feature information of the device to be monitored includes: Based on the current environment and the current operating condition, determining a dictionary learning algorithm corresponding to the target operating data; The target operation data is subjected to feature extraction based on the dictionary learning algorithm to obtain the feature information.

8. The abnormal warning system for equipment health monitoring according to claim 6, characterized in that: Before analyzing the characteristic information based on the constructed equipment performance degradation knowledge graph to obtain the performance degradation trend information of the equipment to be monitored, the method further includes: Acquire a historical operation data set of the device to be monitored; the historical operation data set includes historical data of different data types when the device to be monitored is continuously operated under different working conditions and different environments, and historical data of different data types when the device to be monitored fails under different working conditions and different environments; Extracting features from each historical data in the historical operation data set to obtain a plurality of historical feature parameters; Analyze the plurality of characteristic parameters to obtain a data degradation trajectory of the device to be monitored; Performing correlation analysis on each historical data of the different data types to obtain the degree of interaction between each historical data of the different data types; Based on the historical operation data set, the data degradation trajectory and the degree of interaction, the equipment performance degradation knowledge graph is constructed.

9. The abnormal warning system for equipment health monitoring according to claim 8, characterized in that: The performance degradation information includes a plurality of the data degradation trajectories and a degree of interaction between different operating data; The generating the health monitoring result based on the performance degradation information includes: Determining multiple health monitoring data based on the device type of the device to be monitored; Based on the degree of interaction between the different operating data, calculating the weight value of each of the multiple health monitoring data; Determining a target degradation trajectory corresponding to each of the plurality of health monitoring data from the plurality of data degradation trajectories; The health monitoring result is generated based on the weight values ​​of each of the plurality of health monitoring data and the target degradation trajectory.

10. The abnormal warning system for equipment health monitoring according to claim 4, characterized in that: The data analysis module is also used for: Performing time domain analysis on the target operation data to obtain statistical features corresponding to the target operation data; Performing a correlation analysis on the target operation data and the indicator data to obtain a correlation degree between the target operation data and the indicator data; Inputting the statistical features and the indicator data into a health monitoring model for processing respectively, to obtain a first monitoring result corresponding to the target operation data and a second monitoring result of the indicator data; Based on the degree of correlation between the target operating data and the indicator data, the current operating condition and the current environment, the first monitoring result and the second monitoring result are adjusted to obtain the health monitoring result.

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