A method and apparatus for reliability assessment of equipment condition monitoring system

Abstract

This invention discloses a method and apparatus for reliability assessment of an equipment condition monitoring system, comprising: configuring weight coefficients for each system function of the equipment condition monitoring system to be assessed according to the current monitoring target; selecting a first data type for the equipment condition monitoring system to be assessed according to the current monitoring target and collecting first data corresponding to the first data type; calculating the stability of the data trend prediction result based on the first data; selecting a second data type for the equipment condition monitoring system to be assessed according to the current monitoring target and collecting second data corresponding to the second data type; calculating the mean time between failures (MTBF) assessment result based on the second data; verifying the data trend prediction result and the MTBF assessment result, and determining the reliability assessment result of the equipment condition monitoring system to be assessed based on the verification result and the weight coefficients. This solution can perform in-depth assessment and judgment of the reliability of the equipment condition monitoring system.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology applications, and in particular to a method and apparatus for reliability assessment of an equipment condition monitoring system, a storage medium, and an electronic device. Background Technology

[0002] During the fault diagnosis of turbine equipment, the equipment condition monitoring system (EDS), serving as a detection and analysis system for critical power equipment such as steam turbines, records the equipment's status and performs early warning correlations based on system alarm values. The EDS can promptly alarm for common faults such as mass imbalance, initial bending, thermal bending, blade detachment, misalignment, oil film oscillation, steam flow excitation, friction, bearing loosening, and resonance. However, during on-site analysis, if contradictory data emerges, the accuracy and reliability of the system's data cannot be effectively assessed. Therefore, a reliability assessment of the EDS is fundamental to ensuring its long-term reliable operation. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention aims to provide a method and apparatus for reliability assessment of an equipment condition monitoring system, as well as a storage medium and electronic device, to at least resolve the issue that existing technologies cannot provide in-depth assessments of the reliability of equipment condition monitoring systems, thereby ensuring the long-term stable operation of the system.

[0004] The technical solution of this invention is implemented as follows:

[0005] This invention provides a reliability assessment method for an equipment condition monitoring system, comprising:

[0006] Obtain the system functions of the equipment condition monitoring system to be evaluated, and configure weight coefficients for each system function of the equipment condition monitoring system to be evaluated according to the current monitoring objectives;

[0007] Based on the current monitoring target, the system for monitoring the status of the equipment to be evaluated selects a first data type and collects the first data corresponding to the first data type; the stability of the data trend prediction result is calculated based on the first data.

[0008] Based on the current monitoring target, the system for monitoring the condition of the equipment to be evaluated selects a second data type and collects the second data corresponding to the second data type; the mean time between failures (MTBF) is calculated based on the second data to evaluate the results.

[0009] The data trend prediction results and mean time between failures (MTBF) assessment results are verified, and the reliability assessment result of the condition monitoring system of the equipment to be evaluated is determined based on the verification results and the weighting coefficients.

[0010] Furthermore, embodiments of the present invention provide a reliability assessment device for an equipment condition monitoring system, comprising:

[0011] The system function weight allocation module is used to obtain the system functions of the equipment status monitoring system to be evaluated, and to configure weight coefficients for each system function of the equipment status monitoring system to be evaluated according to the current monitoring objectives.

[0012] The first data processing module is used to filter a first data type according to the current monitoring target of the equipment status monitoring system to be evaluated, and collect the first data corresponding to the first data type; and calculate the stability of the data trend prediction result based on the first data.

[0013] The second data processing module is used to filter the second data type of the equipment status monitoring system to be evaluated according to the current monitoring target, and collect the second data corresponding to the second data type; and calculate the average fault-free operating time evaluation result based on the second data.

[0014] The evaluation result output module is used to verify the data trend prediction results and the mean time between failures (MTBF) evaluation results, and to determine the reliability evaluation result of the condition monitoring system of the equipment to be evaluated based on the verification results and the weighting coefficients.

[0015] Furthermore, embodiments of the present invention provide a storage medium storing a computer program thereon, wherein the program, when executed by a processor, implements the equipment status monitoring system reliability assessment method as described in the above embodiments.

[0016] Furthermore, embodiments of the present invention provide a storage medium, an electronic device, including: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the device condition monitoring system reliability assessment method as described in the above embodiments by executing the executable instructions.

[0017] This invention provides a method for reliability assessment of an equipment condition monitoring system. By configuring weights for each function of the equipment condition monitoring system based on current visual inspection, and by calculating the accuracy of data trends using detection signals of a first data type and calculating the mean time between failures (MTBF) using detection data of a second data type, the method combines the data trend prediction results, MTBF assessment results, and weight coefficients to calculate the reliability assessment results of each function of the equipment condition monitoring system in the current application scenario, thereby achieving an accurate judgment on the reliability of the equipment condition monitoring system. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0019] Figure 1 This is a schematic diagram of a reliability assessment method for an equipment condition monitoring system provided in an embodiment of the present invention;

[0020] Figure 2 A schematic diagram of a reliability assessment device for an equipment condition monitoring system provided in an embodiment of the present invention;

[0021] Figure 3 A schematic diagram of a storage medium provided in an embodiment of the present invention;

[0022] Figure 4 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0023] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0024] This invention provides a method for reliability assessment of an equipment condition monitoring system. Figure 1 This is a schematic diagram of a reliability assessment method for an equipment condition monitoring system provided in an embodiment of the present invention; as shown below. Figure 1 As shown, the reliability assessment method for the equipment condition monitoring system provided in this application includes:

[0025] Step S11: Obtain the system functions of the equipment condition monitoring system to be evaluated, and configure weight coefficients for each system function of the equipment condition monitoring system to be evaluated according to the current monitoring objectives.

[0026] Step S12: Based on the current monitoring target, the system filters the first data type of the equipment status monitoring system to be evaluated, and collects the first data corresponding to the first data type; calculates the stability of the data trend prediction result based on the first data.

[0027] Step S13: Based on the current monitoring target, the system for monitoring the status of the equipment to be evaluated selects a second data type and collects the second data corresponding to the second data type; the mean time between failures (MTBF) is calculated based on the second data to evaluate the results.

[0028] Step S14: Verify the data trend prediction results and the mean time between failures (MTBF) assessment results, and determine the reliability assessment result of the condition monitoring system of the equipment to be evaluated based on the verification results and the weighting coefficients.

[0029] In this embodiment of the application, the rotating machinery diagnostic monitoring and management system, as a data management system, can be used to conduct in-depth analysis of data during the operation of the unit and can acquire fault characteristic data such as speed, vibration waveform, spectrum, amplitude and phase of harmonics.

[0030] In step S11 above, the basic functions of an equipment condition monitoring system generally include: remote database, data transmission testing, data synchronization testing, etc. For the current equipment condition monitoring system to be evaluated, the current on-site monitoring focus can be taken as the current monitoring target. Based on the actual needs of the monitoring data of the current monitoring target, the functional proportions of each system function of the equipment condition monitoring system are divided, and the corresponding weight coefficients for each function are configured. In different actual application environments, different monitoring objects may have different actual needs for different functions; for example, in some scenarios, the need for the remote database function is low, so a low weight coefficient can be configured for this function, such as 0.2 or 0.15, etc.; in other scenarios, the need for the data synchronization testing function is high, so a high weight coefficient can be configured for this function, such as 0.6 or 0.75, etc. The corresponding weight coefficients are configured based on the level of business needs of the current monitoring target for each function; the specific values ​​can be configured according to the actual scenario, and this invention does not impose any special limitations on this.

[0031] Optionally, in this embodiment of the application, the first data type includes: trend data; that is, continuous data.

[0032] Optionally, in this embodiment of the application, the trend data includes any one or a combination of any number of trend charts, axis position charts, Bode plots, and cascade plots.

[0033] In this embodiment of the application, in step S12 above, the step of calculating the stability of the data trend prediction result based on the first data includes: processing the first data using the Goel Okumoto model to obtain the data trend prediction result.

[0034] Specifically, data of the first data type within a certain time period can be collected and statistically analyzed to obtain first data, such as spectrogram, trend graph, shaft center position graph, Bode plot, cascade graph; the collected first data is predicted using the Goel Okumoto model, and data trend prediction results corresponding to various types of first data are output, and the sensitivity of each parameter to causing unit shutdown is calculated. Generally speaking, the assumption conditions of the Goel Okumoto model can include: (1) The number of faults experienced at time t follows a Poisson distribution with the mean function μ(t); the boundary conditions of this mean method are μ(0) = 0 and Limt→∞μ(t) = N < ∞; (2) The number of software faults occurring in (t, t + Δt) with Δt → 0 is proportional to the expected number of undetected errors N - μ(t), and the proportionality constant is (3) For any finite set of times t1 < t2 < ··· < tn, the number of faults occurring in each non-overlapping interval (0, t1), (t1, t2) … (tn - 1, tn) is independent; (4) Whenever a fault occurs, the fault causing the fault is immediately eliminated without introducing any new faults into the software. Since each fault is well repaired after causing the fault, the number of faults inherent in the software at the start of the test is equal to the number of faults that will occur after an infinite number of tests. According to assumption 1, M(∞) follows a Poisson distribution with the expected value N. The specific operation process of the Goel Okumoto model can be implemented using existing technologies, and this invention will not elaborate on it. Alternatively, in some exemplary embodiments, the minimum correlation error method can also be used to calculate each trend-type data respectively to obtain the corresponding preset data trend results.

[0035] Optionally, in the embodiments of the present application, the second data type includes: numerical data, that is, discrete data.

[0036] Optionally, in the embodiments of the present application, the second data includes any one or any combination of the following: spectrogram data graph, vibration shock, vibration acceleration.

[0037] Optionally, in the embodiments of the present application, in the above step S13, calculating the mean time between failures evaluation result according to the second data includes: using the Jelinsk-Moranda model to process the second data to obtain the mean time between failures evaluation result.

[0038] Specifically, secondary data of various types can be collected and statistically analyzed over a period of time to obtain secondary data. The Jelinski-Moranda model is then used to process this secondary data to obtain at least one of the following model outputs: the number of errors, failure time, and failure interval. The mean time between failures (MTBF) is then calculated based on the model's output. Specifically, the Jelinski-Moranda model is a failure interval time model. It assumes that there are N failures at the start of testing, failures occur completely randomly, and all defects have the same impact on failures during testing. Simultaneously, the repair time for failures is negligible, and the repair of each failure is perfect (completely fixed without causing other failures). Based on these assumptions, the failure rate of the software product improves by the same amount after each repair. Therefore, the probability function of danger at time ti (the instantaneous failure rate function, or the time between the (i-1)th failure and the ith failure) can be obtained as: λ(ti) = φ[N-(i-1)], i = 1, 2…N; N: the number of software defects at the start of the test; Φ: a proportionality constant indicating the failure rate provided by each failure; ti = the time between the (i-1)th failure and the (i)th failure. The specific calculation process of the model can be implemented using existing technology, and will not be elaborated upon in this invention.

[0039] In this embodiment of the application, in step S14 above, the verification of the data trend prediction result and the mean time between failures (MTBF) assessment result includes: collecting on-site monitoring data; and verifying the data trend prediction result and the mean time between failures (MTBF) assessment result based on the on-site monitoring data to obtain the corresponding verification results.

[0040] Specifically, after using different models to predict trend-type and numerical data and obtaining the corresponding prediction results, on-site monitoring data can be collected. The U-chart method can then be used to compare and verify the collected on-site monitoring data with the data trend prediction results and the mean time between failures (MTBF) assessment results, thereby enabling the judgment of the data prediction results.

[0041] In this embodiment of the application, in step S14 above, determining the reliability assessment result of the condition monitoring system of the equipment to be evaluated based on the test results and the weighting coefficients includes: processing the test results of the data trend prediction results, the mean time between failures (MTBF) assessment results, and the weighting coefficients using the U-chart method to obtain the reliability assessment result of the condition monitoring system of the equipment to be evaluated.

[0042] Specifically, for each type of data, the correlation between the monitoring functions of each system and each type of data can be obtained in advance. Based on this correlation, corresponding weight coefficients can be configured for different types of data according to the weight coefficients already configured for the system functions, thereby being used for calculating the system reliability assessment. By applying specific algorithms to different types of data, reliability modeling is performed, and the data is predicted based on Markov models to establish a system fault prediction model. The U-graph method is used to judge the prediction results, thereby completing the reliability assessment of the equipment condition monitoring system. The reliability of the monitoring system is assessed, and the assessment results are displayed to facilitate timely adjustments.

[0043] Furthermore, in the embodiments of this application, the above method may further include: step S15, performing statistical analysis on the error data in the storage database according to a preset statistical period, and calculating the storage reliability of the device status monitoring system to be evaluated based on the statistical results of the error data.

[0044] For example, a storage database can be evaluated over a year, divided into twelve storage nodes based on months, and a (6, 12) threshold scheme can be used to set the data. Based on the data error statistics as N (N < 1), the system's storage reliability P is:

[0045] P = N12 + C1 × N11 × (1 - N) 11 +C2 2 ×N10×(1-N) 2 +……C11 11 ×N1×(1-N)+C12 12 ×(1-N)

[0046] Furthermore, embodiments of the present invention provide a reliability assessment device for an equipment condition monitoring system. Figure 2 This is a schematic diagram of a reliability assessment device for an equipment condition monitoring system provided in an embodiment of the present invention; as shown. Figure 2 As shown, the equipment condition monitoring system reliability assessment device 20 provided in this application embodiment includes:

[0047] The system function weight allocation module 201 is used to obtain the system functions of the equipment status monitoring system to be evaluated, and to configure weight coefficients for each system function of the equipment status monitoring system to be evaluated according to the current monitoring target.

[0048] The first data statistics module 202 is used to filter the first data type of the equipment status monitoring system to be evaluated according to the current monitoring target, and collect the first data corresponding to the first data type; and calculate the stability of the data trend prediction result based on the first data.

[0049] The second data statistics module 203 is used to filter the second data type of the equipment status monitoring system to be evaluated according to the current monitoring target, and collect the second data corresponding to the second data type; and calculate the average fault-free working time evaluation result based on the second data.

[0050] The evaluation result output module 204 is used to verify the data trend prediction results and the mean time between failures (MTBF) evaluation results, and to determine the reliability evaluation result of the condition monitoring system of the equipment to be evaluated based on the verification results and the weighting coefficients.

[0051] Optionally, the device further includes: a storage reliability assessment module, used to perform statistical analysis on the error data in the storage database according to a preset statistical period, and to calculate the storage reliability of the device status monitoring system to be evaluated based on the statistical results of the error data.

[0052] Since the functional modules of the equipment condition monitoring system reliability assessment device in the embodiments of the present invention are the same as those in the embodiments of the above-described equipment condition monitoring system reliability assessment method, they will not be described again here.

[0053] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0054] Furthermore, embodiments of the present invention provide a computer-readable storage medium. Figure 3 This is a schematic diagram of a computer-readable storage medium provided for an embodiment of the present invention. Specifically, it stores a program product capable of implementing the methods described above in this specification. In some possible embodiments, various aspects of the present invention can also be implemented as a program product comprising program code, which, when the program product is run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of the present invention described in the "Exemplary Methods" section of this specification.

[0055] According to embodiments of the present invention, a program product for implementing the above-described method may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, a readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0056] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0057] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0058] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0059] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0060] Furthermore, embodiments of the present invention provide an electronic device. Figure 4 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. This electronic device can be used to implement the reliability assessment of the aforementioned equipment condition monitoring system. Figure 4As shown, the electronic device 800 is presented in the form of a general-purpose computing device. The components of the electronic device 800 may include, but are not limited to: at least one processing unit 810, at least one storage unit 820, a bus 830 connecting different system components (including storage unit 820 and processing unit 810), and a display unit 840.

[0061] The storage unit stores program code that can be executed by the processing unit 810, causing the processing unit 810 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 810 can perform actions such as... Figure 1 The steps are shown in the figure.

[0062] Storage unit 820 may include volatile storage units, such as random access memory (RAM) 8201 and / or cache memory 8202, and may further include read-only memory (ROM) 8203.

[0063] The storage unit 820 may also include a program / utility 8204 having a set (at least one) of program modules 8205, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0064] Bus 830 may include a data bus, an address bus, and a control bus.

[0065] Electronic device 800 can also communicate with one or more external devices 900 (e.g., keyboard, pointing device, Bluetooth device, etc.) via input / output (I / O) interface 850. Electronic device 800 also includes a display unit 840 connected to input / output (I / O) interface 850 for display purposes. Furthermore, electronic device 800 can communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 860. As shown, network adapter 860 communicates with other modules of electronic device 800 via bus 830. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 800, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0066] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention.

Claims

1. A reliability assessment method for an equipment condition monitoring system, characterized in that, include: Obtain the system functions of the equipment condition monitoring system to be evaluated, and configure weight coefficients for each system function of the equipment condition monitoring system to be evaluated according to the current monitoring objectives; Based on the current monitoring target, the system filters the first data type of the equipment status monitoring system to be evaluated, and collects the first data corresponding to the first data type; The stability of the data trend prediction results is calculated based on the first data. Based on the current monitoring target, the system filters the second data type of the equipment status monitoring system to be evaluated, and collects the second data corresponding to the second data type; The mean time between failures (MTBF) assessment results were calculated based on the second set of data. The data trend prediction results and mean time between failures (MTBF) assessment results are verified, and the reliability assessment result of the condition monitoring system of the equipment to be evaluated is determined based on the verification results and the weighting coefficients. The first data type includes: trend data; the first data includes: vibration velocity, vibration displacement trend map, axis position map, Bode plot, cascade plot, any one or any combination of multiple items; the step of calculating the data trend prediction result based on the first data includes: processing the first data using the Goel Okumoto model to obtain the data trend prediction result; The second data type includes: numerical data; the second data includes: any one or any combination of spectrum data, vibration shock, and vibration acceleration; the calculation of the mean time between failures (MTBF) assessment result based on the second data includes: processing the second data using the Jelinsk-Moranda model to obtain the MTBF assessment result.

2. The reliability assessment method for an equipment condition monitoring system according to claim 1, characterized in that, The method further includes: The system performs statistical analysis on the error data in the storage database according to a preset statistical period, and calculates the storage reliability of the device status monitoring system to be evaluated based on the statistical results of the error data.

3. The reliability assessment method for an equipment condition monitoring system according to claim 1, characterized in that, The verification of the data trend prediction results and the mean time between failures (MTBF) assessment results includes: Collect on-site monitoring data; The data trend prediction results and mean time between failures (MTBF) assessment results are verified based on the on-site monitoring data to obtain the corresponding verification results.

4. The reliability assessment method for an equipment condition monitoring system according to claim 1, characterized in that, The process of determining the reliability assessment result of the condition monitoring system of the equipment to be evaluated based on the test results and the weighting coefficients includes: The U-chart method is used to process the data trend prediction results, the mean time between failures (MTBF) assessment results, and the weighting coefficients to obtain the reliability assessment results of the condition monitoring system of the equipment to be evaluated.

5. A reliability assessment device for an equipment condition monitoring system, characterized in that, include: The system function weight allocation module is used to obtain the system functions of the equipment status monitoring system to be evaluated, and to configure weight coefficients for each system function of the equipment status monitoring system to be evaluated according to the current monitoring objectives. The first data processing module is used to filter the first data type of the equipment status monitoring system to be evaluated according to the current monitoring target, and to collect the first data corresponding to the first data type. The stability of the data trend prediction results is calculated based on the first data. The second data processing module is used to filter the second data type of the equipment status monitoring system to be evaluated according to the current monitoring target, and collect the second data corresponding to the second data type; and calculate the average fault-free operating time evaluation result based on the second data. The evaluation result output module is used to verify the data trend prediction results and the mean time between failures (MTBF) evaluation results, and to determine the reliability evaluation result of the condition monitoring system of the equipment to be evaluated based on the verification results and the weighting coefficients. The first data type includes: trend data; the first data includes: vibration velocity, vibration displacement trend map, axis position map, Bode plot, cascade plot, any one or any combination of multiple items; the step of calculating the data trend prediction result based on the first data includes: processing the first data using the Goel Okumoto model to obtain the data trend prediction result; The second data type includes: numerical data; the second data includes: any one or any combination of spectrum data, vibration shock, and vibration acceleration; the calculation of the mean time between failures (MTBF) assessment result based on the second data includes: processing the second data using the Jelinsk-Moranda model to obtain the MTBF assessment result.

6. A storage medium, characterized in that, It stores a computer program, which, when executed by a processor, implements the reliability assessment method for the equipment condition monitoring system according to any one of claims 1 to 4.

7. An electronic device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to execute the device condition monitoring system reliability assessment method according to any one of claims 1 to 4 by executing the executable instructions.

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