Control instrument intelligent fault diagnosis method and system based on multi-source data

Abstract

The invention relates to the technical field of fault diagnosis, in particular to an intelligent fault diagnosis method and system for a control instrument based on multi-source data. Control instrument anomaly analysis is performed based on a damage evaluation result of the control instrument, a statistical complexity analysis result and accurate operation data of the instrument in the operation process, and whether troubleshooting needs to be performed on the control instrument or not is judged based on a control instrument anomaly analysis result. The performance of the control instrument is comprehensively evaluated and analyzed by comprehensively testing the control precision of the control instrument, evaluating the damage of the fluid to the control instrument and analyzing the statistical complexity, the performance analysis accuracy of the control instrument is improved, and the negative influence caused by fault misjudgment is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of fault diagnosis, and particularly to an intelligent fault diagnosis method and system for control instruments based on multi-source data. Background Art

[0002] With the rapid development of industrial automation and intelligence, as a core component of industrial systems, the operating status of control instruments directly affects the safety, stability, and efficiency of the entire production process. Traditional fault diagnosis methods mainly rely on experience and simple logical judgments, and cannot meet the requirements of modern industry for high precision, high efficiency, and real-time performance. At the same time, the complexity of industrial systems and the diversity of data are increasing continuously. Diagnostic methods based on a single data source are difficult to meet actual needs. Therefore, intelligent fault diagnosis technology based on multi-source data has gradually become a research hotspot. When diagnosing faults in control instruments, due to the accuracy of control instruments (such as flow meters) being affected by many aspects (such as damage to control instruments by fluids and the complexity of statistical data changes), the accuracy of control instruments is constantly changing, which easily leads to misjudgment of faults and thus negative impacts. The prior art cannot solve the corresponding problems; For example, in a Chinese patent with the application publication number CN116910677A, an industrial instrument fault diagnosis method and system are disclosed. The method includes: obtaining historical operation data of the industrial instrument, and performing data reconstruction on the historical operation data, and pre-constructing a fault diagnosis model according to the data reconstruction result; determining the monitoring time span, obtaining real-time operation data of the industrial instrument within the current monitoring time span, and analyzing abnormal data according to the real-time operation data; inputting the abnormal data into the pre-constructed fault diagnosis model for fault diagnosis, and outputting a fault diagnosis result. This invention reconstructs the historical operation data of industrial instruments and constructs a training set and a test set, and then trains the constructed model to obtain a fault diagnosis model, improving the model accuracy and diagnosis accuracy rate; performing clustering analysis based on the density of real-time operation data, screening out abnormal data, and then diagnosing instrument faults based on the abnormal data and the fault diagnosis model, making the diagnosis more targeted and the result more accurate. This invention's method of static modeling based on historical data, its diagnostic accuracy depends on the data consistency under steady-state working conditions. However, the accuracy attenuation of control instruments (such as flow meters) is a dynamic process, affected by time-varying factors such as fluid impact and material fatigue, and its fault characteristics will change with accuracy drift, resulting in the difficulty for the model based on a fixed training set to capture this non-linear degradation law. The historical data reconstruction method of industrial instruments assumes that the measurement error follows a fixed distribution, but the fluid damage involved in this application will cause the error distribution to be time-varying, which makes the clustering analysis of this invention misidentify normal accuracy drift as abnormal data, instead exacerbating misjudgment. Therefore, it is obvious that this invention cannot solve the technical problems proposed in this application.

[0003] To solve these problems, the present application designs an intelligent fault diagnosis method and system for control instruments based on multi-source data. Summary of the Invention

[0004] To overcome the defects and deficiencies of the prior art, the present invention provides an intelligent fault diagnosis method and system for control instruments based on multi-source data. Load analysis is performed based on the operation data of the fluid in the control area of the control instrument, and then damage assessment of the control instrument by the fluid is carried out. Statistical complexity analysis is performed based on the operation data of the fluid in the control area of the control instrument. Abnormal analysis of the control instrument is carried out based on the damage assessment result of the control instrument, the statistical complexity analysis result, and the accurate operation data during the operation of the instrument. Based on the abnormal analysis result of the control instrument, it is judged whether it is necessary to perform fault maintenance on the control instrument. The present application comprehensively tests the control accuracy of the control instrument, the damage assessment of the control instrument by the fluid, and the statistical complexity analysis to comprehensively evaluate and analyze the performance of the control instrument, improving the accuracy of performance analysis of the control instrument and avoiding the negative impact caused by misjudgment of faults.

[0005] To achieve the above object, the present invention adopts the following technical solutions: In the first aspect, the present invention provides an intelligent fault diagnosis method for control instruments based on multi-source data, including the following steps: S1. Obtain the accurate operation data of the control instrument during operation, and at the same time obtain the operation data of the fluid in the control area of the control instrument; S2. Perform load analysis based on the operation data of the fluid in the control area of the control instrument, and then perform damage assessment of the control instrument by the fluid; S3. Perform statistical complexity analysis based on the operation data of the fluid in the control area of the control instrument; S4. Perform abnormal analysis of the control instrument based on the damage assessment result of the control instrument, the statistical complexity analysis result, and the accurate operation data during the operation of the instrument; S5. Judge whether it is necessary to perform fault maintenance on the control instrument based on the abnormal analysis result of the control instrument.

[0006] In an implementation manner of the present invention, the method for obtaining the accurate operation data of the control instrument during operation is as follows: Obtain the fluid flow rate data recorded by the instrument and the standard value of the fluid flow rate data within a specified operation cycle, compare the fluid flow rate data recorded by the instrument with the standard value of the fluid flow rate data, obtain the difference value between the fluid flow rate data recorded by the instrument and the standard value of the fluid flow rate data, analyze the ratio of the difference value to the standard value of the fluid flow rate data, and obtain the accurate operation data by subtracting the ratio of the difference value to the standard value of the fluid flow rate data from the maximum value of the operation accuracy. The operation data of the fluid includes the real-time temperature, density, pressure data of the fluid, and the flow rate data to be detected. Here, all the data are obtained through the corresponding sensing modules and stored in the corresponding storage modules. For example, the real-time temperature data is obtained through the corresponding temperature sensor, and the density data is obtained through the corresponding density sensor.

[0007] In an implementation manner of the present invention, in step S2, load analysis is performed based on the operation data of the fluid in the control area of the control instrument, and then damage assessment of the fluid to the control instrument includes the following specific steps: S21. Obtain the pressure data and flow rate data of the input fluid, and at the same time obtain the safe detection flow rate range and safe detection pressure range of the control instrument; S22. Perform load anomaly analysis of the control area based on the pressure data and flow rate data of the input fluid at the corresponding moment, the safe detection flow rate range and safe detection pressure range of the control instrument; In an implementation manner of the present invention, in step S2, damage assessment of the fluid to the control instrument includes the following specific steps: S23. Obtain the real-time temperature and density data of the fluid at the corresponding moment, and at the same time obtain the load anomaly analysis result of the control area at the corresponding moment. Based on the deviation between the real-time temperature of the fluid and the safe temperature range of the control instrument and the deviation between the density data and the safe density range of the control instrument, perform weighted summation and then add it to the load anomaly analysis result of the control area at the corresponding moment to obtain the damage assessment result of the fluid to the control instrument at the corresponding moment.

[0008] In an implementation manner of the present invention, in step S3, statistical complexity analysis is performed based on the operation data of the fluid in the control area of the control instrument, including the following specific steps: S31. Specify the standard value of the fluid flow rate data within the operation cycle, and compare it with the change value of the flow rate data that the control instrument can effectively count at the corresponding moment to obtain the flow rate statistical complexity; S32. Obtain the standard value of the fluid flow rate data within the operation cycle, and perform change statistical complexity analysis based on the change degree of the standard value of the fluid flow rate data within the operation cycle; S33. Obtain the flow statistics complexity and change statistics complexity at the corresponding moment, and perform weighted summation to obtain the statistics complexity.

[0009] In an implementation manner of the present invention, in step S4, based on the damage assessment result of the control instrument, the statistics complexity analysis result, and the operation accurate data of the instrument during operation, the abnormal analysis of the control instrument is carried out, including the following specific contents: Obtain the damage assessment result of the fluid to the control instrument, the statistics complexity analysis result, and the operation accurate data of the instrument during operation at the corresponding moment obtained through analysis. Substitute the damage assessment result of the fluid to the control instrument, the statistics complexity analysis result, and the operation accurate data of the instrument during operation at the corresponding moment obtained through analysis within the test period into the control instrument abnormal analysis value calculation formula to calculate the control instrument abnormal analysis value.

[0010] The control instrument abnormal analysis value represents the error abnormality of statistical errors under the combined action of statistics complexity and damage.

[0011] In an implementation manner of the present invention, in step S5, based on the control instrument abnormal analysis result, it is judged whether it is necessary to perform fault maintenance on the control instrument, including the following specific contents: Compare the calculated control instrument abnormal analysis value with the set control instrument abnormal analysis threshold. If the control instrument abnormal analysis value is greater than or equal to the set control instrument abnormal analysis threshold, it indicates that it is necessary to perform fault maintenance on the control instrument. If the control instrument abnormal analysis value is less than the set control instrument abnormal analysis threshold, it indicates that it is not necessary to perform fault maintenance on the control instrument. It is also necessary to edit whether it is necessary to perform fault maintenance on the control instrument into a reception instruction and send it to the maintenance personnel.

[0012] In the second aspect, the present invention also provides an intelligent fault diagnosis system for a control instrument based on multi-source data, including: A data acquisition module, which is used to acquire the operation accurate data of the control instrument during operation, and at the same time acquire the operation data of the fluid in the control area of the control instrument; A damage assessment module, which performs load analysis based on the operation data of the fluid in the control area of the control instrument, and then performs damage assessment of the fluid to the control instrument; A statistics complexity analysis module, which performs statistics complexity analysis based on the operation data of the fluid in the control area of the control instrument; A control instrument abnormal analysis module, which performs abnormal analysis of the control instrument based on the damage assessment result of the control instrument, the statistics complexity analysis result, and the operation accurate data of the instrument during operation; A fault maintenance module, which judges whether it is necessary to perform fault maintenance on the control instrument based on the control instrument abnormal analysis result; A control module, configured to control the operation of a data acquisition module, a damage assessment module, a statistical complexity analysis module, a control instrument anomaly analysis module, and a fault repair module.

[0013] In a third aspect, an electronic device provided by the present invention includes: a processor and a memory. Among them, a computer program that can be called by the processor is stored in the memory, and the processor executes an intelligent fault diagnosis method for control instruments based on multi-source data by calling the computer program stored in the memory.

[0014] In a fourth aspect, a computer-readable storage medium provided by the present invention stores instructions. When the instructions run on a computer, the computer is caused to execute an intelligent fault diagnosis method for control instruments based on multi-source data.

[0015] Compared with the prior art, the present invention has the following advantages and beneficial effects: The present invention performs load analysis based on the operation data of the fluid in the control area of the control instrument, and then conducts damage assessment of the fluid on the control instrument. It performs statistical complexity analysis based on the operation data of the fluid in the control area of the control instrument, conducts control instrument anomaly analysis based on the damage assessment results of the control instrument, the statistical complexity analysis results, and the operation accurate data of the instrument during operation, and determines whether it is necessary to perform fault repair on the control instrument based on the control instrument anomaly analysis results. This application comprehensively tests the control accuracy of the control instrument, the damage assessment of the fluid on the control instrument, and the statistical complexity analysis to comprehensively evaluate and analyze the performance of the control instrument, improving the accuracy of performance analysis of the control instrument and avoiding the negative impacts caused by false fault judgments. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] By reading the detailed description of the non-restrictive embodiments with reference to the following drawings, other features, objectives, and advantages of the present invention will become more apparent: Figure 1 It is a schematic diagram of the overall process flow of the method of the present invention; Figure 2 It is a working flow diagram of S2 in the method of the present invention; Figure 3 It is a working flow diagram of S3 in the method of the present invention; Figure 4 It is a schematic diagram of the structure of the system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] The technical solutions of the present invention will be described in detail below through the drawings and specific embodiments. It should be understood that the specific features in the embodiments of the present invention are detailed descriptions of the technical solutions of the present invention, rather than limitations on the technical solutions of the present invention. Without conflict, the technical features in the embodiments of the present invention and the embodiments can be combined with each other.

[0018] Example 1

[0019] As Figures 1 to 3 shown, this embodiment provides an intelligent fault diagnosis method for control instruments based on multi-source data, which specifically includes the following steps: S1. Obtain the accurate operation data of the control instrument during operation, and at the same time obtain the operation data of the fluid in the control area of the control instrument; In the first step of this embodiment, the required data is first obtained. In this embodiment, the method for obtaining the accurate operation data of the control instrument during operation is as follows: obtain the fluid flow data recorded by the instrument within a specified operation cycle and the standard value of the fluid flow data, compare the fluid flow data recorded by the instrument with the standard value of the fluid flow data, obtain the difference value between the fluid flow data recorded by the instrument and the standard value of the fluid flow data, analyze the ratio of the difference value to the standard value of the fluid flow data, and obtain the accurate operation data by subtracting the ratio of the difference value to the standard value of the fluid flow data from the maximum operation accuracy value. The operation data of the fluid includes the real-time temperature, density, pressure data of the fluid, and the flow data to be detected. Here, the data are all obtained through the corresponding sensing modules and stored in the corresponding storage modules. For example, the real-time temperature data is obtained through the corresponding temperature sensor, and the density data is obtained through the corresponding density sensor. At the same time, the standard value can be the detection results of multiple measuring instruments or detection instruments with higher accuracy than the accuracy of this control instrument. Such instruments are usually more expensive and not suitable for large-scale equipment, but only suitable for data correction; S2. Perform load analysis based on the operation data of the fluid in the control area of the control instrument, and then perform damage assessment of the fluid on the control instrument; Secondly, in this embodiment, the load analysis based on the operation data of the fluid in the control area of the control instrument in step S2 includes the following specific steps: S21. Obtain the pressure data and flow data of the input fluid, and at the same time obtain the safe detection flow range and safe detection pressure range of the control instrument; S22. Perform control area load anomaly analysis based on the pressure data and flow data of the input fluid at the corresponding moment, the safe detection flow range and safe detection pressure range of the control instrument. Among them, the control area load anomaly analysis formula at the corresponding moment is: , where the meaning of con() is: if the number in the parentheses is greater than 0, the value is the corresponding number; if the number in the parentheses is less than or equal to 0, the value is 0. Ft is the pressure data at the corresponding moment, Fm is the maximum value of the safe detection flow range of the control instrument, Qt is the flow data at the corresponding moment, Qm is the maximum value of the safe detection pressure range of the control instrument, and Skz is the residual factor of the influence of the previous fluid load. Among them, the calculation formula of the residual factor of the influence of the previous fluid load is: , where dt is the time integral, T is the operating duration of the control instrument, ti is the ti moment of the control instrument's operation, Fti is the pressure data at the ti moment, Qti is the flow rate data at the ti moment, and Tm is the duration standard value. In this formula, not only the influence of the pressure data and flow rate data of the input fluid at the corresponding moment on the abnormal load of the control area is analyzed, but also the combined influence of the remaining influence of the previous fluid load on the abnormal load of the control area is considered, comprehensively analyzing the load influence of the input fluid on the control instrument; In step S2, an assessment of the damage of the fluid to the control instrument is carried out, including the following specific steps: S23. Obtain the real-time temperature and density data of the fluid at the corresponding moment, and at the same time obtain the analysis result of the abnormal load of the control area at the corresponding moment. Based on the deviation between the real-time temperature of the fluid and the safe temperature range of the control instrument and the deviation between the density data and the safe density range of the control instrument, a weighted sum is performed and then added to the analysis result of the abnormal load of the control area at the corresponding moment to obtain the damage assessment result of the fluid to the control instrument at the corresponding moment; S3. Conduct a statistical complexity analysis based on the operating data of the fluid in the control area of the control instrument; Then, in this embodiment, the statistical complexity analysis based on the operating data of the fluid in the control area of the control instrument in step S3 includes the following specific steps: S31. Specify the standard value of the fluid flow rate data within the operating cycle, and compare it with the change value of the flow rate data that the control instrument can effectively count at the corresponding moment to obtain the flow rate statistical complexity. Among them, the calculation formula for the flow rate statistical complexity at the corresponding moment is: , where ln() is the natural logarithm, zst is the standard value of the fluid flow rate data at the corresponding moment, and zsm is the median of the change value of the flow rate data that the control instrument can effectively count. The role of using the ln function in this formula is to make the change trend of the calculated value in the following parentheses smaller, so that it meets the change trend of the statistical complexity with the deviation between the two. For example, if the flow rate measurement range is: 2 cubic meters per hour to 100 cubic meters per hour, then measuring 105 cubic meters per hour exceeds the range, resulting in complex flow rate statistics and possible errors. The calculation result is: 0.722; S32. Obtain the standard value of the fluid flow rate data within the operating cycle, and conduct a change statistical complexity analysis based on the change degree of the standard value of the fluid flow rate data within the operating cycle. The calculation formula for the change statistical complexity at the corresponding moment is: , where zs(t - 1) is the standard value of the fluid flow rate data at the previous moment, zsmax is the maximum value of the change in the flow rate data that the control instrument can effectively count, and zsmin is the minimum value of the change in the flow rate data that the control instrument can effectively count. For example, if the flow measurement range is: 2 cubic meters per hour to 100 cubic meters per hour, then when measuring the fluid with a previous moment of 105 cubic meters per hour and a subsequent moment of 50 cubic meters per hour, due to the excessive change, it may be difficult for the measuring instrument to keep up, resulting in possible errors. The calculation result is: 0.43; S33. Obtain the flow statistical complexity and change statistical complexity at the corresponding moment, and perform weighted summation to obtain the statistical complexity; S4. Based on the damage assessment result of the control instrument, the statistical complexity analysis result, and the operation accurate data of the instrument during operation, perform abnormal analysis of the control instrument; Furthermore, in this embodiment, the abnormal analysis of the control instrument based on the damage assessment result of the control instrument, the statistical complexity analysis result, and the operation accurate data of the instrument during operation in step S4 includes the following specific contents: Obtain the damage assessment result of the fluid on the control instrument, the statistical complexity analysis result, and the operation accurate data of the instrument during operation at the corresponding moment obtained by the analysis, and substitute the damage assessment result of the fluid on the control instrument, the statistical complexity analysis result, and the operation accurate data of the instrument during operation at the corresponding moment obtained by the analysis within the test period into the control instrument abnormal analysis value calculation formula to calculate the control instrument abnormal analysis value. Among them, the control instrument abnormal analysis value calculation formula is: , where Zqt is the operation accurate data of the instrument during operation at time t, representing the accuracy of data acquisition, Skt is the damage assessment result of the fluid on the control instrument at time t, and Ndt is the statistical complexity analysis result at time t. Among them, is the damage assessment result influence factor, is the statistical complexity analysis result influence factor; S5. Based on the control instrument abnormal analysis result, determine whether it is necessary to perform fault repair on the control instrument Finally, the determination of whether it is necessary to perform fault repair on the control instrument based on the control instrument abnormal analysis result in step S5 includes the following specific contents: Compare the calculated abnormal analysis value of the control instrument with the set abnormal analysis threshold of the control instrument. If the abnormal analysis value of the control instrument is greater than or equal to the set abnormal analysis threshold of the control instrument, it indicates that the control instrument needs to be fault repaired. If the abnormal analysis value of the control instrument is less than the set abnormal analysis threshold of the control instrument, it indicates that the control instrument does not need to be fault repaired. It is also necessary to edit whether the control instrument needs to be fault repaired into a received instruction and send it to the maintenance personnel.

[0020] It should be noted that in this embodiment, the acquisition method of the set parameters (such as each weighting weight and the abnormal analysis threshold of the instrument, etc.) in this embodiment is obtained through experiments by those skilled in the art. The specific experimental method is as follows: Obtain the accurate operation data of the historical control instrument during operation, and at the same time obtain the operation data of the fluid in the control area of the historical control instrument. Substitute them into each step of this embodiment to obtain the abnormal analysis value of the control instrument. At the same time, obtain the judgment result of whether the control instrument fails. Based on the obtained result of the abnormal analysis value of the control instrument and the judgment result of whether the control instrument fails, import them into the fitting software for iterative fitting of data, and output the set parameter value that meets the maximum judgment accuracy rate.

[0021] It should be noted that in this embodiment, the following benefits are achieved. Load analysis is performed based on the operation data of the fluid in the control area of the control instrument, and then damage assessment of the fluid to the control instrument is carried out. Statistical complexity analysis is performed based on the operation data of the fluid in the control area of the control instrument. Abnormal analysis of the control instrument is performed based on the damage assessment result of the control instrument, the statistical complexity analysis result, and the accurate operation data of the instrument during operation. Whether the control instrument needs to be fault repaired is judged based on the abnormal analysis result of the control instrument. This application comprehensively tests the control accuracy of the control instrument, the damage assessment of the fluid to the control instrument, and the statistical complexity analysis to comprehensively evaluate and analyze the performance of the control instrument, improving the accuracy rate of the performance analysis of the control instrument and avoiding the negative impact caused by misjudgment of faults.

[0022] Embodiment 2

[0023] As Figure 4 shown, this embodiment provides an intelligent fault diagnosis system for control instruments based on multi-source data, including: A data acquisition module, configured to acquire the accurate operation data of the control instrument during operation, and at the same time acquire the operation data of the fluid in the control area of the control instrument; A damage assessment module, which performs load analysis based on the operation data of the fluid in the control area of the control instrument, and then performs damage assessment of the fluid to the control instrument; A statistical complexity analysis module, which performs statistical complexity analysis based on the operation data of the fluid in the control area of the control instrument; The control instrument anomaly analysis module performs control instrument anomaly analysis based on the damage assessment results of the control instrument, the statistical complexity analysis results, and the operation accurate data of the instrument during operation; The fault repair module determines whether it is necessary to perform fault repair on the control instrument based on the control instrument anomaly analysis results; The control module is used to control the operation of the data acquisition module, the damage assessment module, the statistical complexity analysis module, the control instrument anomaly analysis module, and the fault repair module.

[0024] For the above parameters and the steps for each unit module in the intelligent fault diagnosis system of the control instrument based on multi-source data of the present invention to achieve corresponding functions and the corresponding roles, reference can be made to the parameters and steps in the embodiments of the intelligent fault diagnosis method of the control instrument based on multi-source data in the foregoing text, and details will not be elaborated herein.

[0025] Embodiment 3

[0026] An electronic device according to an embodiment of the present invention includes: a processor and a memory, wherein a computer program callable by the processor is stored in the memory, and the processor executes the intelligent fault diagnosis method of the control instrument based on multi-source data by calling the computer program stored in the memory. It should be noted that: all computer programs of the intelligent fault diagnosis method of the control instrument based on multi-source data are implemented using the C language.

[0027] Embodiment 4

[0028] This embodiment provides a computer-readable storage medium, on which a rewritable computer program is stored; When the computer program runs on a computer device, the computer device is enabled to execute the above-mentioned intelligent fault diagnosis method of the control instrument based on multi-source data.

[0029] Each embodiment in the present invention is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the embodiments of the Internet of Things devices and media, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiments.

[0030] The systems and media provided by the embodiments of the present invention correspond one-to-one with the methods. Therefore, the systems and media also have beneficial technical effects similar to those of the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the systems and media will not be elaborated herein.

[0031] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code.

[0032] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0033] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0034] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0035] The memory may include non-permanent memory in the computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0036] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0037] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0038] The above are only embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A method for intelligent fault diagnosis of control instruments based on multi-source data, characterized in that, Including the following steps: Obtain the accurate operation data of the control instrument during operation, and at the same time obtain the operation data of the fluid in the control area of the control instrument; Conduct load analysis based on the operation data of the fluid in the control area of the control instrument, and then conduct damage assessment of the fluid to the control instrument; Conduct statistical complexity analysis based on the operation data of the fluid in the control area of the control instrument; Conduct abnormal analysis of the control instrument based on the damage assessment result of the control instrument, the statistical complexity analysis result, and the accurate operation data of the instrument during operation; Judge whether it is necessary to conduct fault maintenance on the control instrument based on the abnormal analysis result of the control instrument.

2. The intelligent fault diagnosis method for a control instrument based on multi-source data according to claim 1, wherein The above-mentioned conduct load analysis based on the operation data of the fluid in the control area of the control instrument, and then conduct damage assessment of the fluid to the control instrument includes the following specific steps: Obtain the pressure data and flow rate data of the input fluid, and at the same time obtain the safe detection flow rate range and safe detection pressure range of the control instrument; Based on the pressure data and flow rate data of the input fluid at the corresponding moment, the safety detection flow range and the safety detection pressure range of the control instrument, perform control area load anomaly analysis. Among them, the control area load anomaly analysis formula at the corresponding moment is: , where the meaning of con() is: if the number in the parentheses is greater than 0, the value is the corresponding number; if the number in the parentheses is less than or equal to 0, the value is 0. ft is the pressure data at the corresponding moment, Fm is the maximum value of the safety detection flow range of the control instrument, Qt is the flow rate data at the corresponding moment, Qm is the maximum value of the safety detection pressure range of the control instrument, and Skz is the residual factor of the influence of the previous fluid load; Obtain the real-time temperature and density data of the fluid at the corresponding moment, and at the same time obtain the abnormal analysis result of the control area load at the corresponding moment. Based on the deviation between the real-time temperature of the fluid and the safe temperature range of the control instrument and the deviation between the density data and the safe density range of the control instrument, after weighted summation and adding to the abnormal analysis result of the control area load at the corresponding moment, obtain the damage assessment result of the fluid to the control instrument at the corresponding moment.

3. The intelligent fault diagnosis method for a control instrument based on multi-source data according to claim 2, wherein The above-mentioned conduct statistical complexity analysis based on the operation data of the fluid in the control area of the control instrument includes the following specific steps: Specify the standard value of the fluid flow rate data within the operation cycle, and compare it with the change value of the flow rate data that the control instrument can effectively count at the corresponding moment to obtain the flow rate statistical complexity; Obtain the standard value of the fluid flow rate data within the operation cycle, and conduct change statistical complexity analysis based on the change degree of the standard value of the fluid flow rate data within the operation cycle; Obtain the flow rate statistical complexity and change statistical complexity at the corresponding moment and conduct weighted summation to obtain the statistical complexity.

4. The intelligent fault diagnosis method for a control instrument based on multi-source data according to claim 3, wherein The above-mentioned conduct abnormal analysis of the control instrument based on the damage assessment result of the control instrument, the statistical complexity analysis result, and the accurate operation data of the instrument during operation includes the following specific contents: Obtain the damage assessment result of the fluid to the control instrument at the corresponding moment obtained by analysis, the statistical complexity analysis result, and the accurate operation data of the instrument during operation. Substitute the damage assessment result of the fluid to the control instrument at the corresponding moment, the statistical complexity analysis result, and the accurate operation data of the instrument during operation obtained by analysis within the test period into the calculation formula for the abnormal analysis value of the control instrument. Among them, the calculation formula for the abnormal analysis value of the control instrument is: , where Zqt is the accurate operation data of the instrument during operation at time t, representing the accuracy of data acquisition, Sk t is the damage assessment result of the fluid to the control instrument at time t, and Ndt is the statistical complexity analysis result at time t. Among them, is the influence factor of the damage assessment result, is the influence factor of the statistical complexity analysis result.

5. The intelligent fault diagnosis method for control instruments based on multi-source data according to claim 4, characterized in that, The above-mentioned judge whether it is necessary to conduct fault maintenance on the control instrument based on the abnormal analysis result of the control instrument includes the following specific contents: Compare the calculated abnormal analysis value of the control instrument with the set abnormal analysis threshold of the control instrument. If the abnormal analysis value of the control instrument is greater than or equal to the set abnormal analysis threshold of the control instrument, it indicates that it is necessary to conduct fault maintenance on the control instrument. If the abnormal analysis value of the control instrument is less than the set abnormal analysis threshold of the control instrument, it indicates that it is not necessary to conduct fault maintenance on the control instrument. It is also necessary to edit whether it is necessary to conduct fault maintenance on the control instrument into a received instruction and send it to the maintenance personnel.

6. The intelligent fault diagnosis method for a control instrument based on multi-source data according to claim 2, wherein, The calculation formula for the influence of the previous fluid load on the residual factor is as follows: , where dt is the time integral, T is the operation duration of the control instrument, ti is the operation ti moment of the control instrument, Fti is the pressure data at the ti moment, Qti is the flow rate data at the ti moment, and Tm is the duration standard value.

7. The intelligent fault diagnosis method for a control instrument based on multi-source data according to claim 6, wherein, The method for obtaining the accurate operation data of the control instrument during operation is as follows: Obtain the fluid flow data recorded by the instrument and the standard value of the fluid flow data within a specified operation cycle, compare the fluid flow data recorded by the instrument with the standard value of the fluid flow data, obtain the difference value between the fluid flow data recorded by the instrument and the standard value of the fluid flow data, analyze the ratio of the difference value to the standard value of the fluid flow data, and obtain the accurate operation data by subtracting the ratio of the difference value to the standard value of the fluid flow data from the maximum operation accuracy value. The operation data of the fluid includes the real-time temperature, density, pressure data of the fluid, and the flow data to be detected.

8. The intelligent fault diagnosis method for a control instrument based on multi-source data according to claim 3, characterized in that The calculation formula for the complexity of the change statistics at the corresponding moment is as follows: , where zs(t - 1) is the standard value of the fluid flow rate data at the previous moment, zsmax is the maximum value of the change value of the flow rate data that the control instrument can effectively count, and zsmin is the minimum value of the change value of the flow rate data that the control instrument can effectively count.

9. An intelligent fault diagnosis system for a control instrument based on multi-source data, which is implemented based on the intelligent fault diagnosis method for a control instrument based on multi-source data described in any one of claims 1-8, and is characterized in that, The system includes: A data acquisition module, which is used to acquire the accurate operation data of the control instrument during operation, and at the same time acquire the operation data of the fluid in the control area of the control instrument; A damage assessment module, which performs load analysis based on the operation data of the fluid in the control area of the control instrument, and then conducts damage assessment of the fluid on the control instrument; A statistical complexity analysis module, which conducts statistical complexity analysis based on the operation data of the fluid in the control area of the control instrument; A control instrument anomaly analysis module, which conducts control instrument anomaly analysis based on the damage assessment result of the control instrument, the statistical complexity analysis result, and the accurate operation data of the instrument during operation; A fault maintenance module, which determines whether it is necessary to perform fault maintenance on the control instrument based on the control instrument anomaly analysis result; A control module, which is used to control the operation of the data acquisition module, the damage assessment module, the statistical complexity analysis module, the control instrument anomaly analysis module, and the fault maintenance module.

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