WO2024007567A1 - 基于fmeca的海上浮式风机可靠性分析方法及装置 - Google Patents

基于fmeca的海上浮式风机可靠性分析方法及装置 Download PDF

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WO2024007567A1
WO2024007567A1 PCT/CN2023/071061 CN2023071061W WO2024007567A1 WO 2024007567 A1 WO2024007567 A1 WO 2024007567A1 CN 2023071061 W CN2023071061 W CN 2023071061W WO 2024007567 A1 WO2024007567 A1 WO 2024007567A1
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fault
analysis
offshore floating
floating wind
reliability
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English (en)
French (fr)
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祝金涛
朱俊杰
吕亮
武青
吴昊
魏昂昂
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中国华能集团清洁能源技术研究院有限公司
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Publication of WO2024007567A1 publication Critical patent/WO2024007567A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/17—Mechanical parametric or variational design
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/20—Administration of product repair or maintenance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/08—Probabilistic or stochastic CAD
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]

Definitions

  • the present disclosure relates to the technical field of wind power generation, and specifically relates to a reliability analysis method and device for offshore floating wind turbines based on FMECA (Failure Mode, Effects and Criticality Analysis).
  • the present disclosure aims to solve one of the technical problems in the related art, at least to a certain extent.
  • the first purpose of this disclosure is to propose a reliability analysis method for offshore floating wind turbines based on FMECA.
  • This method is based on the FMECA analysis method and conducts a comprehensive analysis of offshore floating wind turbines from two aspects: qualitative analysis and quantitative analysis. , system and high-confidence reliability analysis are conducive to ensuring the system reliability of offshore floating wind turbines.
  • the second purpose of this disclosure is to propose a reliability analysis device for offshore floating wind turbines based on FMECA.
  • the third object of the present disclosure is to provide an electronic device.
  • a fourth object of the present disclosure is to provide a non-transitory computer-readable storage medium.
  • a fifth object of the present disclosure is to provide a computer program product.
  • a sixth object of the present disclosure is to provide a computer program.
  • the first embodiment of the present disclosure proposes a reliability analysis method for offshore floating wind turbines based on FMECA.
  • the method includes the following steps:
  • the equipment or the subsystem conduct a hazard analysis on the fault mode based on three indicators: severity, occurrence and detection, and evaluate the risk level of the fault unit, so as to Implement quantitative analysis of reliability;
  • the offshore floating wind turbine is decomposed into a preset number of systems, including: a wind energy receiving system, an electric energy production system, an electric energy conversion system, a support structure system and an auxiliary system.
  • the determination of each of the The equipment or subsystems included in the system to be analyzed include: decomposing the wind energy receiving system into blades and hubs; decomposing the electric energy production system into main shaft, main bearing, gearbox and generator; decomposing the electric energy conversion system Decompose it into a rectifier and a transformer; decompose the support structure system into a tower, tower and mooring subsystem; decompose the auxiliary system into a yaw subsystem, a pitch subsystem, a controller and an electronic component subsystem.
  • hazard analysis is performed on each fault mode and the risk level of the fault unit is evaluated based on three indicators: severity, occurrence and detection, including: formulating the severity, The quantification criterion of the occurrence degree and the detection degree; calculating the risk priority number RPN of the fault unit according to the quantification criterion; sorting all the risk priority numbers RPN in descending order.
  • the risk priority number RPN is calculated by the following formula:
  • S is the severity
  • O is the occurrence degree
  • D is the detection degree
  • the method after analyzing the fault cause and fault impact of the fault mode, includes: formulating fault avoidance measures based on the analysis results of the fault cause and the fault impact; After the risk priority number RPN is sorted in order from large to small, it also includes: determining the target faulty unit whose risk level is above the preset risk level threshold according to the sorting result; taking corresponding measures for the target faulty unit. Described fault avoidance measures.
  • the second embodiment of the present disclosure also proposes an offshore floating wind turbine reliability analysis device based on FMECA, including the following modules:
  • a decomposition module used to decompose the offshore floating wind turbine into a preset number of systems according to the functions performed, and determine the equipment or subsystems included in each system to be analyzed;
  • the first analysis module is used to obtain the failure mode of the offshore floating wind turbine and each of the systems, and analyze the failure causes and failure effects of the failure mode to achieve qualitative analysis of reliability;
  • the second analysis module is used to use the system, the equipment or the subsystem as a fault unit, conduct a hazard analysis on the fault mode based on three indicators of severity, occurrence and detection, and evaluate the fault mode. Risk level of failed units to enable quantitative analysis of reliability; and
  • a determination module is used to assist in determining the operation strategy and maintenance measures of the offshore floating wind turbine based on the reliability analysis results.
  • the offshore floating wind turbine is decomposed into a preset number of systems, including: wind energy receiving system, electric energy production system, electric energy conversion system, support structure system and auxiliary system.
  • the decomposition module is specifically used. In: determining that the wind energy receiving system includes blades and hubs; determining that the electrical energy production system includes a main shaft, main bearings, gearboxes and generators; determining that the electrical energy conversion system includes a rectifier and a transformer; determining that the support structure system includes a tower Tube, tower and mooring subsystem; it is determined that the auxiliary system includes yaw subsystem, pitch subsystem, controller and electronic component subsystem.
  • the second analysis module is specifically configured to: formulate quantification criteria for the severity, the occurrence degree, and the detection degree; and calculate the risk priority number of the faulty unit according to the quantification criteria. RPN; Sort all the risk priority numbers RPN in descending order.
  • the second analysis module is specifically configured to calculate the risk priority number RPN through the following formula:
  • S is the severity
  • O is the occurrence degree
  • D is the detection degree
  • the first analysis module is further configured to: formulate fault avoidance measures based on the analysis results of the fault cause and the fault impact, and the determination module is further configured to: determine the risk level according to the sorting result. Target faulty units above a preset risk level threshold; and taking corresponding fault avoidance measures for the target faulty units.
  • a third embodiment of the present disclosure provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor.
  • the computer program is When the processor is executed, the FMECA-based reliability analysis method for offshore floating wind turbines described in the embodiment of the first aspect of the present disclosure is implemented.
  • the fourth embodiment of the present disclosure also provides a non-transitory computer-readable storage medium on which a computer program is stored.
  • the embodiment of the first aspect of the present disclosure is implemented. Reliability analysis method of offshore floating wind turbines based on FMECA.
  • the fifth embodiment of the present disclosure proposes a computer program product, including a computer program, wherein when the computer program is executed by a processor, the computer program implements the FMECA-based method as described in the first embodiment of the present disclosure. Reliability analysis method of offshore floating wind turbines.
  • the sixth aspect of the present disclosure provides a computer program, wherein the computer program includes a computer program code.
  • the computer program code When the computer program code is run on a computer, the computer executes the steps of the first aspect of the present disclosure.
  • this disclosure conducts a comprehensive, systematic and high-credibility reliability analysis of offshore floating wind turbines from two aspects: qualitative analysis and quantitative analysis. analysis, improving the accuracy and reliability of offshore floating wind turbine reliability analysis. Moreover, this disclosure also predicts the occurrence of faults and avoids them by formulating fault avoidance measures. Among them, key fault units with high risk levels are selected according to the quantitative analysis results and measures are taken to avoid faults, thereby reducing the occurrence of faults in offshore floating wind turbines. On the basis of probability, inefficient fault avoidance measures can be avoided, thereby ensuring the system reliability of offshore floating wind turbines and avoiding wasting resources.
  • Figure 1 is a flow chart of a reliability analysis method for offshore floating wind turbines based on FMECA proposed by an embodiment of the present disclosure
  • Figure 2 is a flow chart of a quantitative analysis method for offshore floating wind turbine reliability proposed by an embodiment of the present disclosure
  • Figure 3 is a schematic diagram of a specific severity, occurrence and detection quantification criterion proposed by an embodiment of the present disclosure
  • Figure 4 is a flow chart of a specific FMECA-based reliability analysis method for offshore floating wind turbines proposed by the embodiment of the present disclosure
  • Figure 5 is a schematic structural diagram of an offshore floating wind turbine reliability analysis system based on FMECA proposed by an embodiment of the present disclosure.
  • Figure 1 is a flow chart of a reliability analysis method for offshore floating wind turbines based on FMECA proposed by an embodiment of the present disclosure. As shown in Figure 1, the method includes the following steps: S101-S104.
  • Step S101 Decompose the offshore floating wind turbine into a preset number of systems according to the functions performed, and determine the equipment or subsystems included in each system to be analyzed.
  • reliability analysis refers to a systematic process of collecting, characterizing, and organizing reliability information, and obtaining credible analysis results based on scientific and reasonable modeling methods. This disclosure also learns methods to avoid faults from faults during the reliability analysis process. Its purpose is to predict the occurrence of faults by learning the essential characteristics of faults and formulate fault avoidance measures. Moreover, this disclosure conducts reliability analysis based on Failure Mode, Effects and Criticality Analysis (FMECA).
  • FMECA Failure Mode, Effects and Criticality Analysis
  • FMECA includes Failure Mode and Effects Analysis (FMEA) and Criticality Analysis (CA).
  • FMECA assigns failure risk evaluation indicators to the basic failure units of the analyzed products or systems, including subjective indicators such as severity S, occurrence degree O, and detection degree D, as well as objective indicators such as failure probability and failure cost, and then establishes the risk priority number (Risk Priority Number (RPN for short) to comprehensively evaluate the risk level of the basic fault unit. Therefore, this disclosure first decomposes the offshore floating wind turbine into multiple systems, and determines the equipment or subsystems included in each system to be analyzed, so as to subsequently determine the fault unit for analysis.
  • RPN Risk Priority Number
  • the product or system is first decomposed.
  • the product is an offshore floating wind turbine.
  • the offshore floating wind turbine is decomposed into multiple systems according to the functions of the system, and then each system is decomposed to determine the number of components in each system. Included equipment or subsystems to be analyzed, among which only important equipment or subsystems in the system that require reliability analysis are determined.
  • the preset number of decomposed systems can be determined based on various factors such as characteristics of current offshore floating wind turbines and accuracy requirements of reliability analysis.
  • offshore floating wind turbines can be decomposed into five major systems, namely: wind energy receiving system, electric energy production system, electric energy conversion system, support structure system and auxiliary system, and then determine the components to be analyzed included in each system. device or subsystem.
  • the wind energy receiving system includes blades and a hub, and its function is to convert wind energy into mechanical energy.
  • Electric energy production system including main shaft, main bearing, gearbox and generator. Its function is to convert mechanical energy into unstable electrical energy based on the principle of electromagnetic induction.
  • Power conversion system including rectifiers and transformers, its function is to convert unstable power generated by the generator into stable power that meets the requirements of the booster station.
  • Support structure system including tower, tower and mooring system, its function is to stably support the main functional components of offshore floating wind turbines.
  • Auxiliary system including yaw system, pitch system, controller and electronic component subsystem, its function is to ensure the production efficiency of electric energy.
  • Step S102 Obtain the fault modes of the offshore floating wind turbine and each system, and analyze the fault causes and fault effects of the fault modes to achieve qualitative analysis of reliability.
  • the failure mode refers to the manifestation of faults that occur in offshore floating wind turbines or each system, such as insufficient power supply in the electric energy production system, corrosion, cracks or deformation of the tower and tower, short circuit in the equipment, etc.
  • the impact of fault refers to the impact of this fault mode on the safety and functions of offshore floating wind turbines.
  • this disclosure first conducts a qualitative analysis of the fault, that is, conducts a failure mode and impact analysis, including first collecting the fault modes of the product or decomposed system, that is, obtaining all possible fault modes of offshore floating wind turbines, and based on the fault modes The analysis determines the causes of each failure mode and the impact on the operation of offshore floating wind turbines.
  • failure modes of products or systems can be collected in various ways. For example, it is possible to obtain the potential future failure modes determined based on the design data during the design process of the offshore floating wind turbine when designing each component unit of the offshore floating wind turbine. Alternatively, the operating status of the offshore floating wind turbine can also be detected, the operating data of the offshore floating wind turbine can be stored, and the historical fault data of the offshore floating wind turbine can be recorded, and then the historical data can be retrieved to collect the failure modes.
  • relevant data on various aspects of the offshore floating wind turbine can be collected in advance.
  • the collected data includes the structure and function of the offshore floating wind turbine. Data, operation and maintenance data, historical operation data and environmental data of the wind turbine, etc., and then combined with the collected data to conduct a mechanism analysis of the equipment and systems of the offshore floating wind turbine, and sort out various faults of the offshore floating wind turbine based on the mechanism analysis results.
  • the causes, effects and maintenance measures corresponding to the modes are then constructed to build a fault diagnosis knowledge base for offshore floating wind turbines. During actual analysis, the collected failure modes are entered into the fault knowledge base to analyze their causes and effects.
  • the impact of the fault when analyzing the impact of a fault, the impact of the fault can be analyzed from three perspectives: the equipment itself, the system, and the offshore floating wind turbine, thereby improving the comprehensiveness of the analysis of the impact of the fault.
  • the present disclosure analyzes that the impact of this failure mode on the electronic component subsystem may be to reduce operating efficiency, which may affect the performance of the electronic component subsystem.
  • the impact of the auxiliary system in which this subsystem is located is a reduction in the production efficiency of auxiliary power, which may ultimately have no impact on offshore floating wind turbines. Therefore, this disclosure conducts failure impact analysis from three levels: local equipment, system and final product impact, further improving the comprehensiveness and systematicness of failure impact analysis, and helping to fully ensure the reliability of offshore floating wind turbines.
  • Step S103 Using the system, equipment or subsystem as the fault unit, conduct a hazard analysis of the fault mode based on three indicators: severity, occurrence and detection, and evaluate the risk level of the fault unit to achieve quantitative reliability analysis.
  • the present disclosure performs fault hazard analysis (CA), that is, each fault mode obtained in step S102 is analyzed according to the comprehensive impact of its severity, occurrence probability and detectability, and a risk priority number RPN is established.
  • CA fault hazard analysis
  • RPN risk priority number
  • Figure 2 is a flow chart of a quantitative analysis method for offshore floating wind turbine reliability proposed by an embodiment of the present disclosure. As shown in Figure 2, the method includes the following steps: S201-S203.
  • Step S201 Develop quantification criteria for severity, occurrence and detection.
  • severity is the consequence of the failure mode
  • occurrence is the possibility of the failure mode
  • Detectability is the difficulty with which a fault mode is detected. This disclosure first formulates quantification criteria for severity, occurrence, and detection, so as to facilitate subsequent determination of the quantified index values of each failure mode and then perform quantitative calculations.
  • This disclosure proposes an FMECA quantification criterion for severity, occurrence, and detection as shown in Figure 3.
  • the criterion is divided into 10 levels of quantified values, and the corresponding quantified values for each quantified value are clearly defined. Judgment conditions for severity, occurrence and detection.
  • Step S202 Calculate the risk priority number RPN of the faulty unit according to the quantification criterion.
  • the risk priority number RPN is a numerical representation of the risk level of the fault unit.
  • this disclosure collects the failure modes of each equipment and each system in offshore floating wind turbines, this disclosure can use the system and each equipment or subsystem in the system as a fault unit to target two-level faults.
  • the units separately calculate the risk priority number RPN of the possible faults of each faulty unit, thereby quantifying and evaluating the risk level of faulty units at each level, further improving the systematicness and comprehensiveness of reliability analysis.
  • the severity, occurrence and detection quantification values of each failure mode can be determined first based on quantitative criteria.
  • the severity, occurrence and detection quantification values of each failure mode can be determined based on expert knowledge and historical experience in this field.
  • the detection degree corresponds to which quantization value in the quantization criterion of Figure 3 respectively.
  • the risk priority number RPN is calculated through the following formula:
  • S is the severity
  • O is the occurrence degree
  • D is the detection degree. That is, after determining the severity, occurrence and detection quantification values of the failure mode, multiply the three indicators to obtain the corresponding value of the risk level of the fault unit.
  • Step S203 Sort all risk priority numbers RPN in descending order.
  • all risk priority numbers RPN are sorted from large to small to facilitate the intuitive display of the risk level of each fault unit and subsequent determination of the target fault unit.
  • this disclosure also conducts quantitative analysis after qualitative analysis based on FMECA, in which severity (S), occurrence (O), detection (D), RPN and RPN ranking are the quantitative indicators of FMECA.
  • S severity
  • O occurrence
  • D detection
  • RPN RPN ranking
  • Step S104 Assist in determining the operation strategy and maintenance measures of the offshore floating wind turbine based on the reliability analysis results.
  • fault avoidance measures can also be formulated based on the analysis results of the fault cause and fault impact.
  • the fault avoidance measures are used to eliminate faults in advance. reasons to avoid accidents.
  • all feasible solutions to solve the fault can be read from the fault diagnosis knowledge base in the above embodiment, and then the prevention solutions are selected to formulate fault avoidance measures corresponding to the fault mode.
  • the risk priority number RPN after sorting all the risk priority numbers RPN in descending order, it also includes: determining the target faulty unit whose risk level is above the preset risk level threshold according to the sorting result; taking measures to target the target faulty unit.
  • Corresponding fault avoidance measures That is, after sorting the risk priority numbers RPN from large to small, key fault units with high risk levels can be selected based on the sorting results.
  • the risk level threshold can be a predetermined RPN value, which is used to distinguish High and low risk levels. When the calculated RPN value of the faulty unit is greater than the RPN value, it means that the risk level of the faulty unit is higher. Then, for the selected key fault units, fault avoidance measures corresponding to each key fault unit are taken to avoid the occurrence of faults.
  • the FMECA-based reliability analysis method of offshore floating wind turbines in the embodiment of the present disclosure is based on the FMECA analysis method and conducts a comprehensive, systematic and highly reliable analysis of offshore floating wind turbines from two aspects: qualitative analysis and quantitative analysis.
  • Degree of reliability analysis improves the accuracy and reliability of reliability analysis of offshore floating wind turbines.
  • this disclosure also predicts the occurrence of faults and avoids them by formulating fault avoidance measures. Among them, key fault units with high risk levels are selected according to the quantitative analysis results and measures are taken to avoid faults, thereby reducing the occurrence of faults in offshore floating wind turbines. On the basis of probability, inefficient fault avoidance measures can be avoided, thereby ensuring the system reliability of offshore floating wind turbines and avoiding wasting resources.
  • FIG 4 is a flow chart of a specific FMECA-based reliability analysis method for offshore floating wind turbines proposed by an embodiment of the present disclosure. As shown in Figure 4, the reliability analysis method of this embodiment includes the following steps: S401-S408.
  • Step S401 Decompose the product or system. Specifically, offshore floating wind turbines are broken down into five major systems.
  • Step S402 Collect product or system failure modes.
  • Step S403 Analyze the cause and impact of the fault.
  • Step S404 Develop fault avoidance measures.
  • Step S405 Perform hazard analysis and decompose it into three indicators: severity, occurrence and detection.
  • Step S406 Develop severity, occurrence and detection quantification criteria.
  • Step S407 Calculate RPN values and sort. Specifically, the risk priority numbers RPN of components and systems are calculated and sorted from large to small.
  • Step S408 Propose operation and maintenance suggestions and measures.
  • FIG. 5 is a schematic structural diagram of a reliability analysis device for offshore floating wind turbines based on FMECA proposed in an embodiment of the disclosure.
  • the device includes a decomposition module 100, a first analysis module 200, a second analysis module 300 and a determination module 400.
  • the decomposition module 100 is used to decompose the offshore floating wind turbine into a preset number of systems according to the functions performed, and determine the equipment or subsystems included in each system to be analyzed.
  • the first analysis module 200 is used to obtain the fault mode of the offshore floating wind turbine and each system, and analyze the fault causes and fault effects of the fault mode to achieve qualitative reliability analysis.
  • the second analysis module 300 is used to use the system, equipment or subsystems as fault units, perform hazard analysis on the fault mode based on three indicators of severity, occurrence and detection, and evaluate the risk level of the fault unit, so as to Implement quantitative analysis of reliability.
  • the determination module 400 is used to assist in determining the operation strategy and maintenance measures of the offshore floating wind turbine based on the reliability analysis results.
  • the offshore floating wind turbine is decomposed into a preset number of systems, including: wind energy receiving system, electric energy production system, electric energy conversion system, support structure system and auxiliary system.
  • the decomposition module 100 is specifically used for : Determine the wind energy receiving system including blades and hubs; Determine the electric energy production system including main shaft, main bearings, gearboxes and generators; Determine the electric energy conversion system including rectifiers and transformers; Determine the support structure system including towers, towers and mooring subsystems. ; Determine the auxiliary system including yaw subsystem, pitch subsystem, controller and electronic component subsystem.
  • the second analysis module 300 is specifically configured to: formulate quantification criteria for severity, occurrence and detection; calculate the risk priority number RPN of the faulty unit according to the quantification criteria; combine all Risk priority numbers RPN are sorted from large to small.
  • the second analysis module 300 is specifically configured to calculate the risk priority number RPN through the following formula:
  • S is the severity
  • O is the occurrence degree
  • D is the detection degree
  • the first analysis module 200 is also used to formulate fault avoidance measures based on the analysis results of fault causes and fault effects
  • the determination module 400 is also used to determine the risk level at a preset risk level threshold based on the sorting results. Based on the target faulty unit above, take corresponding fault avoidance measures for the target faulty unit.
  • the FMECA-based reliability analysis device for offshore floating wind turbines in the embodiment of the present disclosure conducts a comprehensive, systematic and highly reliable analysis of offshore floating wind turbines from two aspects: qualitative analysis and quantitative analysis based on the FMECA analysis method.
  • Degree of reliability analysis improves the accuracy and reliability of reliability analysis of offshore floating wind turbines.
  • this disclosure also predicts the occurrence of faults and avoids them by formulating fault avoidance measures. Among them, key fault units with high risk levels are selected according to the quantitative analysis results and measures are taken to avoid faults, thereby reducing the occurrence of faults in offshore floating wind turbines. On the basis of probability, inefficient fault avoidance measures can be avoided, thereby ensuring the system reliability of offshore floating wind turbines and avoiding wasting resources.
  • the present disclosure also proposes an electronic device with a memory, a processor and a computer program stored on the memory and executable on the processor, and the computer program is executed by the processor
  • the FMECA-based reliability analysis method of offshore floating wind turbines as described in any of the above embodiments.
  • embodiments of the present disclosure also propose a non-transitory computer-readable storage medium on which a computer program is stored.
  • the computer program is executed by a processor, the computer program based on any of the above embodiments is implemented.
  • FMECA reliability analysis method for offshore floating wind turbines.
  • embodiments of the present disclosure also propose a computer program product, including a computer program, wherein when the computer program is executed by a processor, the computer program implements the FMECA-based offshore floating system as described in any of the above embodiments.
  • Type fan reliability analysis method
  • embodiments of the present disclosure also provide a computer program, wherein the computer program includes computer program code.
  • the computer program code When the computer program code is run on a computer, it causes the computer to execute any one of the above embodiments.
  • references to the terms “one embodiment,” “some embodiments,” “an example,” “specific examples,” or “some examples” or the like means that specific features are described in connection with the embodiment or example. , structures, materials, or features are included in at least one embodiment or example of the present disclosure. In this specification, the schematic expressions of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the specific features, structures, materials or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, those skilled in the art may combine and combine different embodiments or examples and features of different embodiments or examples described in this specification unless they are inconsistent with each other.
  • first and second are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of indicated technical features. Therefore, features defined as “first” and “second” may explicitly or implicitly include at least one of these features.
  • “plurality” means at least two, such as two, three, etc., unless otherwise expressly and specifically limited.
  • a "computer-readable medium” may be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
  • Non-exhaustive list of computer readable media include the following: electrical connections with one or more wires (electronic device), portable computer disk cartridges (magnetic device), random access memory (RAM), Read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and portable compact disc read-only memory (CDROM).
  • the computer-readable medium may even be paper or other suitable medium on which the program may be printed, as the paper or other medium may be optically scanned, for example, and subsequently edited, interpreted, or otherwise suitable as necessary. process to obtain the program electronically and then store it in computer memory.
  • various parts of the present disclosure may be implemented in hardware, software, firmware, or combinations thereof.
  • various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system.
  • a suitable instruction execution system For example, if it is implemented in hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: discrete logic gate circuits with logic functions for implementing data signals; Logic circuits, application specific integrated circuits with suitable combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), etc.
  • the program can be stored in a computer-readable storage medium.
  • the program can be stored in a computer-readable storage medium.
  • each functional unit in various embodiments of the present disclosure may be integrated into one processing module, each unit may exist physically alone, or two or more units may be integrated into one module.
  • the above integrated modules can be implemented in the form of hardware or software function modules. If the integrated module is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.
  • the storage media mentioned above can be read-only memory, magnetic disks or optical disks, etc.

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Abstract

提出了一种基于FMECA的海上浮式风机可靠性分析方法及装置，该方法包括：根据执行的功能将海上浮式风机分解为预设数量个系统，并确定每个系统中待分析的设备或子系统；获取海上浮式风机和每个系统的故障模式，并分析故障模式的故障原因和故障影响；将系统，以及设备或子系统作为故障单元，基于严重度、发生度和探测度三个指标对故障模式进行危害度分析并评判故障单元的风险水平；和根据可靠性分析结果辅助确定海上浮式风机的运行策略和维护措施。

Description

基于FMECA的海上浮式风机可靠性分析方法及装置
相关申请的交叉引用
本申请基于申请号为2022107896414、申请日为2022年7月6日的中国专利申请提出，并要求该中国专利申请的优先权，该中国专利申请的全部内容在此引入本申请作为参考。
技术领域
本公开涉风力发电技术领域，具体涉及一种基于FMECA(Failure Mode,Effects and Criticality Analysis，影响及危害性分析)的海上浮式风机可靠性分析方法及装置。
背景技术
随着风力发电技术的发展，为了充分利用丰富的海上风力能源，现代风能产业正逐渐从陆上风电向海上风电聚集，且海上风机也正由近海固定式向远海浮式挺进，海上浮式风机的普及率正逐渐提高。
然而，风机大型化、海洋环境的极端化及设计与运维经验的匮乏等因素，给海上浮式风机的开发与建设带来了诸多难题，主要包括以下几点：一是海上风电成本高，经济优势并未完全显现；二是海上浮式风机的故障率远高于陆上风机；三是运维成本高，成熟的陆上风机和海上固定基础风机的运维成本占其总体经济收益的20％-30％，而海上浮式风机有望超过35％。因此，海上浮式风机的发展前景和经济优势需要更高的系统可靠性，而海上浮式风机可靠性分析是其可靠性、安全性、可用性、维护性和经济性研究的基础，从而必须对海上浮式风机进行可靠性分析。但是，相关技术中的海上浮式风机可靠性分析方案，分析结果的可信度较低且分析过程较为片面。
发明内容
本公开旨在至少在一定程度上解决相关技术中的技术问题之一。
为此，本公开的第一个目的在于提出一种基于FMECA的海上浮式风机可靠性分析方法，该方法基于FMECA分析方式，从定性分析和定量分析两个方面对海上浮式风机进行了全面、系统和高可信度的可靠性分析，有利于保证海上浮式风机的系统可靠性。
本公开的第二个目的在于提出一种基于FMECA的海上浮式风机可靠性分析装置。
本公开的第三个目的在于提出一种电子设备。
本公开的第四个目的在于提出一种非临时性计算机可读存储介质。
本公开的第五个目的在于提出一种计算机程序产品。
本公开的第六个目的在于提出一种计算机程序。
为达上述目的，本公开的第一方面实施例提出了一种基于FMECA的海上浮式风机可靠性分析方法，该方法包括以下步骤：
根据执行的功能将所述海上浮式风机分解为预设数量个系统，并确定每个所述系统包括的待分析的设备或子系统；
获取所述海上浮式风机和每个所述系统的故障模式，并分析所述故障模式的故障原因和故障影响，以实现可靠性定性分析；
将所述系统，以及所述设备或所述子系统作为故障单元，基于严重度、发生度和探测度三个指标对所述故障模式进行危害度分析并评判所述故障单元的风险水平，以实现可靠性定量分析；和
根据可靠性分析结果辅助确定所述海上浮式风机的运行策略和维护措施。
在本公开的一个实施例中，海上浮式风机分解成的预设数量个系统，包括：风能接收系统、电能生产系统、电能转换系统、支撑结构系统和辅助系统，所述确定每个所述系统包括的待分析的设备或子系统，包括：将所述风能接收系统分解为叶片和轮毂；将所述电能生产系统分解为主轴、主轴承、齿轮箱和发电机；将所述电能转换系统分解为整流器和变压器；将所述支撑结构系统分解为塔筒、塔架和系泊子系统；将所述辅助系统分解为偏航子系统、变桨子系统、控制器和电子部件子系统。
在本公开的一个实施例中，基于严重度、发生度和探测度三个指标对每个所述故障模式进行危害度分析并评判所述故障单元的风险水平，包括：制定所述严重度、所述发生度和所述探测度的量化准则；根据所述量化准则计算所述故障单元的风险优先数RPN；将全部的所述风险优先数RPN按由大至小的顺序进行排序。
在本公开的一个实施例中，通过以下公式计算所述风险优先数RPN：
RPN＝S×O×D
其中，S是严重度，O是发生度，D是探测度。
在本公开的一个实施例中，在所述分析所述故障模式的故障原因和故障影响之后，包括：根据所述故障原因和所述故障影响的分析结果制定故障规避措施；在所述将全部的所述风险优先数RPN按由大至小的顺序进行排序之后，还包括：根据排序结果确定风险水平在预设的风险水平阈值之上的目标故障单元；针对所述目标故障单元采取对应的所述故障规避措施。
为达上述目的，本公开的第二方面实施例还提出了一种基于FMECA的海上浮式风机可靠性分析装置，包括以下模块:
分解模块，用于根据执行的功能将所述海上浮式风机分解为预设数量个系统，并确定 每个所述系统包括的待分析的设备或子系统；
第一分析模块，用于获取所述海上浮式风机和每个所述系统的故障模式，并分析所述故障模式的故障原因和故障影响，以实现可靠性定性分析；
第二分析模块，用于将所述系统，以及所述设备或所述子系统作为故障单元，基于严重度、发生度和探测度三个指标对所述故障模式进行危害度分析并评判所述故障单元的风险水平，以实现可靠性定量分析；和
确定模块，用于根据可靠性分析结果辅助确定所述海上浮式风机的运行策略和维护措施。
在本公开的一个实施例中，海上浮式风机分解成的预设数量个系统，包括：风能接收系统、电能生产系统、电能转换系统、支撑结构系统和辅助系统，所述分解模块，具体用于：确定所述风能接收系统包括叶片和轮毂；确定所述电能生产系统包括主轴、主轴承、齿轮箱和发电机；确定所述电能转换系统包括整流器和变压器；确定所述支撑结构系统包括塔筒、塔架和系泊子系统；确定所述辅助系统包括偏航子系统、变桨子系统、控制器和电子部件子系统。
在本公开的一个实施例中，第二分析模块具体用于：制定所述严重度、所述发生度和所述探测度的量化准则；根据所述量化准则计算所述故障单元的风险优先数RPN；将全部的所述风险优先数RPN按由大至小的顺序进行排序。
在本公开的一个实施例中，第二分析模块具体用于通过以下公式计算所述风险优先数RPN：
RPN＝S×O×D
其中，S是严重度，O是发生度，D是探测度。
在本公开的一个实施例中，第一分析模块还用于：根据所述故障原因和所述故障影响的分析结果制定故障规避措施，所述确定模块还用于：根据排序结果确定风险水平在预设的风险水平阈值之上的目标故障单元；和针对所述目标故障单元采取对应的所述故障规避措施。
为了实现上述实施例，本公开第三方面实施例提出了一种电子设备，包括存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机程序，所述计算机程序被处理器执行时实现本公开第一方面实施例所述的基于FMECA的海上浮式风机可靠性分析方法。
为了实现上述实施例，本公开第四方面实施例还提出了一种非临时性计算机可读存储介质，其上存储有计算机程序，计算机程序被处理器执行时实现本公开第一方面实施例中的基于FMECA的海上浮式风机可靠性分析方法。
为了实现上述实施例，本公开第五方面实施例提出了一种计算机程序产品，包括计算机程序，其中所述计算机程序在被处理器执行时实现如本公开第一方面实施例所述的基于FMECA的海上浮式风机可靠性分析方法。
为了实现上述实施例，本公开第六方面实施例提出了一种计算机程序，其中所述计算机程序包括计算机程序代码，当所述计算机程序代码在计算机上运行时，使得计算机执行如本公开第一方面实施例所述的基于FMECA的海上浮式风机可靠性分析方法。
本公开的实施例提供的技术方案至少带来以下有益效果：本公开基于FMECA分析方式，从定性分析和定量分析两个方面对海上浮式风机进行了全面、系统和高可信度的可靠性分析，提高了海上浮式风机可靠性分析的准确性和可靠性。并且，本公开还预测故障的发生，通过制定故障的规避措施规避故障发生，其中，根据定量分析结果选取具有高风险等级的关键故障单元采取措施进行故障规避，在减小海上浮式风机发生故障的概率的基础上，避免采取低效的故障规避措施，从而可以保障海上浮式风机的系统可靠性，并避免了浪费资源。
本公开附加的方面和优点将在下面的描述中部分给出，部分将从下面的描述中变得明显，或通过本公开的实践了解到。
附图说明
本公开上述的和/或附加的方面和优点从下面结合附图对实施例的描述中将变得明显和容易理解，其中：
图1为本公开实施例提出的一种基于FMECA的海上浮式风机可靠性分析方法的流程图；
图2为本公开实施例提出的一种海上浮式风机可靠性定量分析方法的流程图；
图3为本公开实施例提出的一种具体的严重度、发生度和探测度量化准则示意图；
图4为本公开实施例提出的一种具体的基于FMECA的海上浮式风机可靠性分析方法的流程图；
图5为本公开实施例提出的一种基于FMECA的海上浮式风机可靠性分析系统的结构示意图。
具体实施方式
下面详细描述本公开的实施例，所述实施例的示例在附图中示出，其中自始至终相同或类似的标号表示相同或类似的元件或具有相同或类似功能的元件。下面通过参考附图描述的实施例是示例性的，旨在用于解释本公开，而不能理解为对本公开的限制。
下面参考附图详细描述本公开实施例所提出的一种基于FMECA的海上浮式风机可靠性分析方法和系统。
图1为本公开实施例提出的一种基于FMECA的海上浮式风机可靠性分析方法的流程图，如图1所示，该方法包括以下步骤：S101-S104。
步骤S101，根据执行的功能将海上浮式风机分解为预设数量个系统，并确定每个系统包括的待分析的设备或子系统。
需要说明的是，在本公开中，可靠性分析是指收集、表征、组织可靠性信息，并依据科学、合理的建模方法，求取可信分析结果的系统化流程。本公开在可靠性分析过程中还从故障中学习规避故障的方法，其目的是通过学习故障的本质特征来预测故障的发生，并制定故障的规避措施。并且，本公开是基于故障模式、影响及危害度分析(Failure Mode,Effects and Criticality Analysis，简称FMECA)进行可靠性分析。
其中，FMECA包括故障模式及影响分析(Failure Mode and Effects Analysis，简称FMEA)和危害度分析(Criticality Analysis，简称CA)。FMECA为所分析的产品或系统的基本故障单元分配故障风险评价指标，包括严酷度S、发生度O、探测度D等主观指标和故障概率、故障成本等客观指标，进而建立风险优先数(Risk Priority Number，简称RPN)来综合评判基本故障单元的风险水平。因此，本公开先将海上浮式风机分解为多个系统，并确定每个系统包括的待分析的设备或子系统，以便后续确定故障单元进行分析。
具体的，先对产品或系统进行分解，在本公开中产品即海上浮式风机，根据系统的功能将海上浮式风机分解为多个系统，再对每个系统进行分解，确定每个系统中包括的待分析的设备或子系统，其中，仅确定系统中需要进行可靠性分析的重要设备或子系统。
在本公开一个实施例中，分解后的系统的预设数量可以根据当前海上浮式风机的特性和可靠性分析的精度需求等各种因素确定。作为其中一种可能的实现方式，可以将海上浮式风机分解成五大系统，分别为：风能接收系统、电能生产系统、电能转换系统、支撑结构系统和辅助系统，再确定各系统包括的待分析的设备或子系统。
在本实施例中，风能接收系统：包括叶片和轮毂，功能是将风能转化为机械能。电能生产系统：包括主轴、主轴承、齿轮箱和发电机，功能是根据电磁感应原理将机械能转化为不稳定的电能。电能转换系统：包括整流器和变压器，功能是将发电机产生的不稳定的电能转换为稳定的且符合升压站要求的电能。支撑结构系统：包括塔筒、塔架和系泊系统，功能是稳定支撑海上浮式风机的主要功能部件。辅助系统：包括偏航系统、变桨系统、控制器和电子部件子系统，功能是保证电能的生产效率。
步骤S102，获取海上浮式风机和每个系统的故障模式，并分析故障模式的故障原因和故障影响，以实现可靠性定性分析。
其中，故障模式是指海上浮式风机或每个系统发生的故障的表现形式，比如，电能生产系统供电量不足，塔筒、塔架出现腐蚀、裂纹或变形，设备发生短路等。故障影响是指该故障模式对海上浮式风机的安全性和实现的功能等产生的影响。
具体的，本公开先对故障进行定性分析，即进行故障模式及影响分析，包括先收集产品或分解后的系统的故障模式，即获取海上浮式风机所有可能发生的故障模式，根据对故障模式的分析确定每种故障模式发生的原因和对海上浮式风机工作的影响。
在本公开一个实施例中，可以通过多种方式收集产品或系统的故障模式。举例而言，可以获取在海上浮式风机在设计过程中，设计海上浮式风机各组成单元时基于设计数据确定的未来潜在的故障模式。或者，还可以对海上浮式风机的运行状态进行检测，存储海上浮式风机的运行数据，并记录海上浮式风机发生的历史故障数据，进而调取历史数据收集其中的故障模式。
进一步的，在本实施例中分析故障原因和故障影响时，作为一种可能的实现方式，可以预先收集海上浮式风机各个方面的相关资料，收集的资料包括海上浮式风机的结构和功能相关资料、运维资料、历史的运行操作资料和风机所处环境资料等，再结合收集的资料对海上浮式风机的设备、系统等进行机理分析，根据机理分析结果整理出海上浮式风机的各个故障模式对应的原因、影响和维护措施，进而构建针对海上浮式风机的故障诊断知识库。在实际进行分析时，将收集的故障模式输入故障知识库分析其原因及影响。
在本实施例中，分析故障影响时，可以从设备本身、系统和海上浮式风机三个角度分析故障影响，从而提高故障影响分析的全面性。举例而言，若上述实施例中的电子部件子系统中的某一部件发生了轻微的接触不良故障，则本公开分析出该故障模式对电子部件子系统产生的影响可能是降低运行效率，对该子系统所处的辅助系统的影响是辅助电能的生产效率的效果有所降低，而最终对海上浮式风机可能没有影响。由此，本公开从局部设备、系统和产品最终影响三个等级进行了故障影响分析，进一步提高故障影响分析的全面性和系统性，有利于充分保障海上浮式风机的可靠性。
步骤S103，将系统，以及设备或子系统作为故障单元，基于严重度、发生度和探测度三个指标对故障模式进行危害度分析并评判故障单元的风险水平，以实现可靠性定量分析。
具体的，本公开进行故障危害性分析(CA)，即把步骤S102中获取的每个故障模式按其影响的严重程度、发生概率和可探测性的综合影响进行分析，并建立风险优先数RPN来综合评判基本故障单元的风险水平，从而更加全面的评价各种故障模式的影响，实现对故障进行定量分析。
为了更加清楚的说明本公开进行危害性分析的具体实现过程，下面在本公开一个实施例中提出的一种分析方法进行示例性说明。图2为本公开实施例提出的一种海上浮式风机 可靠性定量分析方法的流程图，如图2所示，该方法包括以下步骤：S201-S203。
步骤S201，制定严重度、发生度和探测度的量化准则。
具体的，进行危害度分析时，分解为严重度、发生度和探测度三个指标进行分析，其中，严重度(S)是故障模式发生的后果，发生度(O)是故障模式发生的可能性，探测度(D)是故障模式被探测的难度。本公开先制定严重度、发生度和探测度的量化准则，便于后续确定各故障模式的指标量化值再进行量化计算。
本公开提出了如图3所示的FMECA严重度、发生度和探测度量化准则，由图3可知，在该准则中分为10个等级的量化值，并明确规定了每个量化值对应的严重度、发生度和探测度的判断条件。
步骤S202，根据量化准则计算故障单元的风险优先数RPN。
其中，风险优先数RPN是故障单元风险水平的数值化表示。
需要说明的是，由于本公开收集了海上浮式风机中各设备和每个系统的故障模式，因此，本公开可以将系统，以及系统中的各设备或子系统作为故障单元，针对两级故障单元分别计算各故障单元可能发生的故障的风险优先数RPN，从而对各层级故障单元的风险水平进行量化和评判，进一步提高了可靠性分析的系统性和全面性。
具体实施时，可以先根据量化准则确定每个故障模式的严重度、发生度和探测度量化值，比如，可以结合本领域的专家知识和历史经验确定每个故障模式的严重度、发生度和探测度分别对应于图3的量化准则中的哪个量化值。在确定严重度、发生度和探测度的量化值后，再通过以下公式计算风险优先数RPN：
RPN＝S×O×D
其中，S是严重度，O是发生度，D是探测度。即，确定故障模式的的严重度、发生度和探测度量化值后，将三个指标相乘得到对应的故障单元风险水平的数值。
步骤S203，将全部的风险优先数RPN按由大至小的顺序进行排序。
具体的，将全部的风险优先数RPN按由大至小的顺序进行排序，便于直观的显示各个故障单元的风险水平和后续确定目标故障单元。
由此，本公开基于FMECA在进行定性分析后还进行了定量分析，其中，严重度(S)、发生度(O)、探测度(D)、RPN和RPN排序为FMECA的定量指标。本公开在进行FMECA分析时，可以按照以下表1所示的内容进行数据的收集和计算：
表1
Figure PCTCN2023071061-appb-000001
Figure PCTCN2023071061-appb-000002
步骤S104，根据可靠性分析结果辅助确定海上浮式风机的运行策略和维护措施。
具体的，根据上述可靠性分析结果，提出海上浮式风机在运行与维护过程中的建议和措施，结合这些建议和措施，以及海上浮式风机实际的工作要求和所处工况，制定海上浮式风机的运行策略和维护措施，以保证海上浮式风机的系统可靠性。举例而言，调整资源分配，针对风险水平更高的故障单元执行更多的维护措施。
为了进一步保障海上浮式风机的系统可靠性，在本公开一个实施中，还可以选取具有高风险等级的关键故障单元采取措施进行故障规避，避免故障发生。具体实施时，作为一种可能的实现方式，在步骤S102分析故障模式的故障原因和故障影响之后，还可以根据故障原因和故障影响的分析结果制定故障规避措施，故障规避措施用于提前排除故障原因，避免发生事故。具体实施时，可以从上述实施例中的故障诊断知识库中读取解决该故障所有可行的解决方案，进而选取其中的预防方案制定故障模式对应的故障规避措施。
进一步的，在将全部的风险优先数RPN按由大至小的顺序进行排序之后，还包括：根据排序结果确定风险水平在预设的风险水平阈值之上的目标故障单元；针对目标故障单元采取对应的故障规避措施。即，将风险优先数RPN按由大至小的顺序进行排序后，可以根据排序结果选取具有高风险等级的关键故障单元，其中，风险水平阈值可以是预先确定的一个RPN值，它用于区分高低风险水平，当计算出的故障单元的RPN值大于该RPN值时，表示该故障单元的风险等级较高。进而，再针对筛选出的关键故障单元，采取各关键故障单元对应的故障规避措施规避故障发生。
综上所述，本公开实施例的基于FMECA的海上浮式风机可靠性分析方法，基于FMECA分析方式，从定性分析和定量分析两个方面对海上浮式风机进行了全面、系统和高可信度的可靠性分析，提高了海上浮式风机可靠性分析的准确性和可靠性。并且，本公开还预测故障的发生，通过制定故障的规避措施规避故障发生，其中，根据定量分析结果选取具有高风险等级的关键故障单元采取措施进行故障规避，在减小海上浮式风机发生故障的概率的基础上，避免采取低效的故障规避措施，从而可以保障海上浮式风机的系统可靠性，并避免了浪费资源。
为了更加清楚地说明本公开实施例的基于FMECA的海上浮式风机可靠性分析方法，下面以一个具体对海上浮式风机可靠性进行FMECA分析的方法实施例进行详细说明。图4为本公开实施例提出的一种具体的基于FMECA的海上浮式风机可靠性分析方法的流程图，如图4所示，该实施例的可靠性分析方法包括以下步骤：S401-S408。
步骤S401：将产品或系统分解。具体的，将海上浮式风机分解成五大系统。
步骤S402：收集产品或系统故障模式。
步骤S403：分析故障原因和故障影响。
步骤S404：制定故障规避措施。
上述步骤S401至步骤S404为定性分析。
步骤S405：进行危害度分析，分解为严重度、发生度和探测度三个指标。
步骤S406：制定严重度、发生度和探测度量化准则。
步骤S407：计算RPN值并进行排序。具体的，计算零部件和系统的风险优先数RPN并由大到小排序。
步骤S408：提出运行与维护建议和措施。
上述步骤S405至步骤S408为定量分析。
需要说明的是，本实施例中各步骤的具体实现方式可参照上述实施例中的相关描述，此处不再赘述。
为了实现上述实施例，本公开还提出了一种基于FMECA的海上浮式风机可靠性分析装置，图5为本公开实施例提出的一种基于FMECA的海上浮式风机可靠性分析装置的结构示意图，如图5所示，该装置包括分解模块100、第一分析模块200、第二分析模块300和确定模块400。
其中，分解模块100，用于根据执行的功能将海上浮式风机分解为预设数量个系统，并确定每个系统包括的待分析的设备或子系统。
第一分析模块200，用于获取海上浮式风机和每个系统的故障模式，并分析故障模式的故障原因和故障影响，以实现可靠性定性分析。
第二分析模块300，用于将系统，以及设备或子系统作为故障单元，基于严重度、发生度和探测度三个指标对故障模式进行危害度分析并评判所述故障单元的风险水平，以实现可靠性定量分析。
确定模块400，用于根据可靠性分析结果辅助确定海上浮式风机的运行策略和维护措施。
在本公开的一个实施例中，海上浮式风机分解成的预设数量个系统，包括：风能接收系统、电能生产系统、电能转换系统、支撑结构系统和辅助系统，分解模块100，具体用于：确定风能接收系统包括叶片和轮毂；确定电能生产系统包括主轴、主轴承、齿轮箱和发电机；确定电能转换系统包括整流器和变压器；确定支撑结构系统包括塔筒、塔架和系泊子系统；确定辅助系统包括偏航子系统、变桨子系统、控制器和电子部件子系统。
在本公开的一个实施例中，第二分析模块300第二分析模块具体用于：制定严重度、发生度和探测度的量化准则；根据量化准则计算故障单元的风险优先数RPN；将全部的风险优先数RPN按由大至小的顺序进行排序。
在本公开的一个实施例中，第二分析模块300具体用于通过以下公式计算所述风险优先数RPN：
RPN＝S×O×D
其中，S是严重度，O是发生度，D是探测度。
在本公开的一个实施例中，第一分析模块200还用于根据故障原因和故障影响的分析结果制定故障规避措施，确定模块400还用于根据排序结果确定风险水平在预设的风险水平阈值之上的目标故障单元，针对目标故障单元采取对应的故障规避措施。
需要说明的是，前述对基于FMECA的海上浮式风机可靠性分析方法的实施例的解释说明也适用于该实施例的装置，此处不再赘述。
综上所述，本公开实施例的基于FMECA的海上浮式风机可靠性分析装置，基于FMECA分析方式，从定性分析和定量分析两个方面对海上浮式风机进行了全面、系统和高可信度的可靠性分析，提高了海上浮式风机可靠性分析的准确性和可靠性。并且，本公开还预测故障的发生，通过制定故障的规避措施规避故障发生，其中，根据定量分析结果选取具有高风险等级的关键故障单元采取措施进行故障规避，在减小海上浮式风机发生故障的概率的基础上，避免采取低效的故障规避措施，从而可以保障海上浮式风机的系统可靠性，并避免了浪费资源。
为了实现上述实施例，本公开还提出了一种电子设备，其上存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机程序，所述计算机程序被处理器执行时实现如上述实施例中任一所述的基于FMECA的海上浮式风机可靠性分析方法。
为了实现上述实施例，本公开实施例还提出了一种非临时性计算机可读存储介质，其上存储有计算机程序，计算机程序被处理器执行时实现如上述实施例中任一所述的基于FMECA的海上浮式风机可靠性分析方法。
为了实现上述实施例，本公开实施例还提出了一种计算机程序产品，包括计算机程序，其中所述计算机程序在被处理器执行时实现如上述实施例中任一所述的基于FMECA的海上浮式风机可靠性分析方法。
为了实现上述实施例，本公开实施例还提出了一种计算机程序，其中所述计算机程序包括计算机程序代码，当所述计算机程序代码在计算机上运行时，使得计算机执行如上述实施例中任一所述的基于FMECA的海上浮式风机可靠性分析方法。
需要注意的是，前述对基于FMECA的海上浮式风机可靠性分析方法和装置的实施例的解释说明也适用于本公开实施例的电子设备、非瞬时性计算机可读存储介质、计算机程序产品和计算机程序，此处不再赘述。
在本说明书的描述中，参考术语“一个实施例”、“一些实施例”、“示例”、“具体示例”、 或“一些示例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或者特点包含于本公开的至少一个实施例或示例中。在本说明书中，对上述术语的示意性表述不必须针对的是相同的实施例或示例。而且，描述的具体特征、结构、材料或者特点可以在任一个或多个实施例或示例中以合适的方式结合。此外，在不相互矛盾的情况下，本领域的技术人员可以将本说明书中描述的不同实施例或示例以及不同实施例或示例的特征进行结合和组合。
此外，术语“第一”、“第二”仅用于描述目的，而不能理解为指示或暗示相对重要性或者隐含指明所指示的技术特征的数量。由此，限定有“第一”、“第二”的特征可以明示或者隐含地包括至少一个该特征。在本公开的描述中，“多个”的含义是至少两个，例如两个，三个等，除非另有明确具体的限定。
流程图中或在此以其他方式描述的任何过程或方法描述可以被理解为，表示包括一个或更多个用于实现定制逻辑功能或过程的步骤的可执行指令的代码的模块、片段或部分，并且本公开的优选实施方式的范围包括另外的实现，其中可以不按所示出或讨论的顺序，包括根据所涉及的功能按基本同时的方式或按相反的顺序，来执行功能，这应被本公开的实施例所属技术领域的技术人员所理解。
在流程图中表示或在此以其他方式描述的逻辑和/或步骤，例如，可以被认为是用于实现逻辑功能的可执行指令的定序列表，可以具体实现在任何计算机可读介质中，以供指令执行系统、装置或设备(如基于计算机的系统、包括处理器的系统或其他可以从指令执行系统、装置或设备取指令并执行指令的系统)使用，或结合这些指令执行系统、装置或设备而使用。就本说明书而言，"计算机可读介质"可以是任何可以包含、存储、通信、传播或传输程序以供指令执行系统、装置或设备或结合这些指令执行系统、装置或设备而使用的装置。计算机可读介质的更具体的示例(非穷尽性列表)包括以下：具有一个或多个布线的电连接部(电子装置)，便携式计算机盘盒(磁装置)，随机存取存储器(RAM)，只读存储器(ROM)，可擦除可编辑只读存储器(EPROM或闪速存储器)，光纤装置，以及便携式光盘只读存储器(CDROM)。另外，计算机可读介质甚至可以是可在其上打印所述程序的纸或其他合适的介质，因为可以例如通过对纸或其他介质进行光学扫描，接着进行编辑、解译或必要时以其他合适方式进行处理来以电子方式获得所述程序，然后将其存储在计算机存储器中。
应当理解，本公开的各部分可以用硬件、软件、固件或它们的组合来实现。在上述实施方式中，多个步骤或方法可以用存储在存储器中且由合适的指令执行系统执行的软件或固件来实现。如，如果用硬件来实现和在另一实施方式中一样，可用本领域公知的下列技术中的任一项或他们的组合来实现：具有用于对数据信号实现逻辑功能的逻辑门电路的离 散逻辑电路，具有合适的组合逻辑门电路的专用集成电路，可编程门阵列(PGA)，现场可编程门阵列(FPGA)等。
本技术领域的普通技术人员可以理解实现上述实施例方法携带的全部或部分步骤是可以通过程序来指令相关的硬件完成，所述的程序可以存储于一种计算机可读存储介质中，该程序在执行时，包括方法实施例的步骤之一或其组合。
此外，在本公开各个实施例中的各功能单元可以集成在一个处理模块中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个模块中。上述集成的模块既可以采用硬件的形式实现，也可以采用软件功能模块的形式实现。所述集成的模块如果以软件功能模块的形式实现并作为独立的产品销售或使用时，也可以存储在一个计算机可读取存储介质中。
上述提到的存储介质可以是只读存储器，磁盘或光盘等。尽管上面已经示出和描述了本公开的实施例，可以理解的是，上述实施例是示例性的，不能理解为对本公开的限制，本领域的普通技术人员在本公开的范围内可以对上述实施例进行变化、修改、替换和变型。

Claims (14)

  1. 一种基于FMECA的海上浮式风机可靠性分析方法，其特征在于，包括以下步骤：
    根据执行的功能将所述海上浮式风机分解为预设数量个系统，并确定每个所述系统包括的待分析的设备或子系统；
    获取所述海上浮式风机和每个所述系统的故障模式，并分析所述故障模式的故障原因和故障影响，以实现可靠性定性分析；
    将所述系统，以及所述设备或所述子系统作为故障单元，基于严重度、发生度和探测度三个指标对所述故障模式进行危害度分析并评判所述故障单元的风险水平，以实现可靠性定量分析；和
    根据可靠性分析结果辅助确定所述海上浮式风机的运行策略和维护措施。
  2. 根据权利要求1所述的分析方法，其特征在于，所述海上浮式风机分解成的预设数量个系统，包括：风能接收系统、电能生产系统、电能转换系统、支撑结构系统和辅助系统，所述确定每个所述系统包括的待分析的设备或子系统，包括：
    确定所述风能接收系统包括叶片和轮毂；
    确定所述电能生产系统包括主轴、主轴承、齿轮箱和发电机；
    确定所述电能转换系统包括整流器和变压器；
    确定所述支撑结构系统包括塔筒、塔架和系泊子系统；和
    确定所述辅助系统包括偏航子系统、变桨子系统、控制器和电子部件子系统。
  3. 根据权利要求1或2所述的分析方法，其特征在于，所述基于严重度、发生度和探测度三个指标对每个所述故障模式进行危害度分析并评判所述故障单元的风险水平，包括：
    制定所述严重度、所述发生度和所述探测度的量化准则；
    根据所述量化准则计算所述故障单元的风险优先数RPN；和
    将全部的所述风险优先数RPN按由大至小的顺序进行排序。
  4. 根据权利要求3所述的分析方法，其特征在于，通过以下公式计算所述风险优先数RPN：
    RPN＝S×O×D
    其中，S是严重度，O是发生度，D是探测度。
  5. 根据权利要求3或4所述的分析方法，其特征在于，在所述分析所述故障模式的故障原因和故障影响之后，包括：
    根据所述故障原因和所述故障影响的分析结果制定故障规避措施，
    在所述将全部的所述风险优先数RPN按由大至小的顺序进行排序之后，还包括：
    根据排序结果确定风险水平在预设的风险水平阈值之上的目标故障单元；和
    针对所述目标故障单元采取对应的所述故障规避措施。
  6. 一种基于FMECA的海上浮式风机可靠性分析装置，其特征在于，包括以下模块：
    分解模块，用于根据执行的功能将所述海上浮式风机分解为预设数量个系统，并确定每个所述系统包括的待分析的设备或子系统；
    第一分析模块，用于获取所述海上浮式风机和每个所述系统的故障模式，并分析所述故障模式的故障原因和故障影响，以实现可靠性定性分析；
    第二分析模块，用于将所述系统，以及所述设备或所述子系统作为故障单元，基于严重度、发生度和探测度三个指标对所述故障模式进行危害度分析并评判所述故障单元的风险水平，以实现可靠性定量分析；和
    确定模块，用于根据可靠性分析结果辅助确定所述海上浮式风机的运行策略和维护措施。
  7. 根据权利要求6所述的分析装置，其特征在于，所述海上浮式风机分解成的预设数量个系统，包括：风能接收系统、电能生产系统、电能转换系统、支撑结构系统和辅助系统，所述分解模块，具体用于：
    确定所述风能接收系统包括叶片和轮毂；
    确定所述电能生产系统包括主轴、主轴承、齿轮箱和发电机；
    确定所述电能转换系统包括整流器和变压器；
    确定所述支撑结构系统包括塔筒、塔架和系泊子系统；和
    确定所述辅助系统包括偏航子系统、变桨子系统、控制器和电子部件子系统。
  8. 根据权利要求6或7所述的分析装置，其特征在于，所述第二分析模块具体用于：
    制定所述严重度、所述发生度和所述探测度的量化准则；
    根据所述量化准则计算所述故障单元的风险优先数RPN；和
    将全部的所述风险优先数RPN按由大至小的顺序进行排序。
  9. 根据权利要求8所述的分析装置，其特征在于，所述第二分析模块具体用于通过以下公式计算所述风险优先数RPN：
    RPN＝S×O×D
    其中，S是严重度，O是发生度，D是探测度。
  10. 根据权利要求8或9所述的分析装置，其特征在于，所述第一分析模块还用于：
    根据所述故障原因和所述故障影响的分析结果制定故障规避措施，
    所述确定模块还用于：
    根据排序结果确定风险水平在预设的风险水平阈值之上的目标故障单元；和
    针对所述目标故障单元采取对应的所述故障规避措施。
  11. 一种电子设备，其特征在于，包括存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机程序，所述处理器执行所述计算机程序时，实现如权利要求1至5中任一项所述的基于FMECA的海上浮式风机可靠性分析方法。
  12. 一种非临时性计算机可读存储介质，其上存储有计算机程序，其特征在于，所述计算机程序被处理器执行时实现如权利要求1至5中任一项所述的基于FMECA的海上浮式风机可靠性分析方法。
  13. 一种计算机程序产品，包括计算机程序，其中所述计算机程序在被处理器执行时实现如权利要求1至5中任一项所述的基于FMECA的海上浮式风机可靠性分析方法。
  14. 一种计算机程序，其特征在于，所述计算机程序包括计算机程序代码，当所述计算机程序代码在计算机上运行时，使得计算机执行如权利要求1至5中任一项所述的基于FMECA的海上浮式风机可靠性分析方法。
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