Failure mode risk assessment method for photovoltaic air conditioning system

Abstract

The invention provides a failure mode risk assessment method for a photovoltaic air conditioning system. The method comprises the steps of obtaining system operation data; secondly, constructing a risk factor evaluation model of the failure mode through the main evaluation dimension and a plurality of auxiliary evaluation dimensions; then, determining a value range, a deviation trend and a confidence coefficient of each risk factor; constructing a comprehensive assessment model of the risk factors by using a generalized trapezoidal fuzzy number GTrFN; calculating a risk assessment set of each failure mode; and finally, sorting the failure modes according to the evaluation values, and determining the risk levels of the failure modes. Through the application of the GTrFN model, the change trend and confidence of the risk factor can be reflected more flexibly and accurately. In addition, fuzzy factors are adopted to adjust model parameters, so that the reliability of an evaluation result is ensured. According to the method, a scientific basis is provided for fault prediction and maintenance of the photovoltaic air conditioning system, and the stability and safety of the system can be improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of failure mode analysis, and in particular relates to a failure mode risk assessment method for a photovoltaic air conditioning system. Background Art

[0002] As an effective reliability analysis technology, FMEA has been widely used in various industries to evaluate the risks of potential failure modes in the design and manufacturing process, and to make improvement decisions based on the risk assessment results. However, the multiple uncertainties in the failure mode risk assessment process will affect the accuracy of the final assessment results. Therefore, this study proposes a multiple uncertainty control method for the failure mode risk assessment process, which is not only necessary for the use of FMEA technology to carry out risk assessment, but also helps to more systematically control the uncertainty influencing factors in the entire assessment process, thereby obtaining more accurate failure mode risk assessment results.

[0003] At present, when enterprises use FMEA tools to conduct risk assessment of failure modes, most of them adopt the risk priority number (RPN) method, that is, the RPN value is determined by the product of the three risk factors of severity S, frequency O and detection D under a specific integer evaluation standard. The integer FMEA method is simple to calculate, easy to understand and convenient for industrial application, but the evaluation standard is designed to give a score level in a qualitative language description. With the ambiguity of the meaning and boundary of the scoring level, this will make the value criteria of the risk factor uncertain, which is also the source factor leading to the uncertainty of the failure mode risk assessment results. In addition, the uncertainty of the risk factor evaluation model and the uncertainty of the risk factor comprehensive model will affect the accuracy of the final failure mode assessment results. Therefore, in order to obtain relatively accurate failure mode risk assessment results, it is necessary to study and propose a deterministic failure mode risk assessment method. Summary of the invention

[0004] The purpose of the present invention is to provide a failure mode risk assessment method for a photovoltaic air conditioning system in order to overcome the defects of the above-mentioned prior art.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] The present invention provides a failure mode risk assessment method for a photovoltaic air conditioning system, comprising the following steps:

[0007] Step S1: Acquire photovoltaic air conditioning system data;

[0008] Step S2: constructing a risk assessment model for each risk factor of each failure mode according to the photovoltaic air conditioning system data through an evaluation method of one main evaluation dimension and multiple auxiliary evaluation dimensions;

[0009] Step S3: Determine the risk value range, value deviation trend, and confidence level of each risk factor of each failure mode through the risk assessment model of each risk factor of each failure mode;

[0010] Step S4: constructing a risk factor comprehensive model based on generalized trapezoidal fuzzy numbers for each risk factor of each failure mode according to the risk value range, value deviation trend, and confidence level of each risk factor of each failure mode;

[0011] Step S5: Calculate the risk assessment set of each failure mode according to the risk factor comprehensive model based on generalized trapezoidal fuzzy numbers of each risk factor of each failure mode;

[0012] Step S6: Calculate the risk assessment value of each failure mode according to the risk assessment set of each failure mode, sort the risk assessment values ​​of each failure mode from large to small, and obtain the ranking result of each failure mode risk from high to low.

[0013] Furthermore, the risk factors include severity S, occurrence frequency O and detection degree D.

[0014] Furthermore, the risk value range, value deviation trend, and confidence level of each risk factor of each failure mode are as follows:

[0015] {[a Ti ,b Ti ],y Ti ,z Ti}(T=S,O,D)

[0016] Among them, [a Ti ,b Ti ] is the risk value range of risk factor T of failure mode i, y Ti is the value deviation trend of the risk factor T of failure mode i, z Ti is the confidence level of the risk factor T of failure mode i, and its value range is set to (0.5,1], y Ti The value set of is:

[0017] {Extreme right (er), right (r), left (l), extreme left (el), center (m), 0}

[0018] The value 0 means that the values ​​of T are uniformly distributed in [a Ti ,b Ti ].

[0019] Furthermore, the comprehensive risk factor model based on generalized trapezoidal fuzzy numbers for each risk factor of each failure mode is: Si 、E Oi 、E Di},in,

[0020] E Ti =[e 1Ti ,e 2Ti ,e 3Ti ,e 4Ti ,w eTi ](T=S,O,D)

[0021] Among them, E Ti is the GTrFN evaluation model of risk factor T of failure mode i, e 1Ti ,e 2Ti ,e 3Ti ,e 4Ti ,w eTi are the parameters of the GTrFN evaluation model for risk factor T of failure mode i, respectively.

[0022] Furthermore, the parameter e of the GTrFN evaluation model of the risk factor T of the failure mode i is 1Ti ,e 2Ti ,e 3Ti ,e 4Ti The calculation formula is:

[0023]

[0024] Among them, π αTi is the right fuzzy factor of risk factor T of failure mode i, π βTi is the left fuzzy factor of risk factor T of failure mode i.

[0025] Furthermore, the right fuzzy factor π of the risk factor T of the failure mode i is αTi and the left fuzzy factor π βTi According to the value deviation trend y Ti By y Ti The conversion rule table between the fuzzy factors is determined.

[0026] Furthermore, the y Ti The conversion rule table between and fuzzy factors is as follows:

[0027]

[0028] Among them, y Ti is the value deviation trend of the risk factor T of failure mode i, π αTi is the right fuzzy factor of risk factor T of failure mode i, π βTi is the left fuzzy factor of risk factor T of failure mode i.

[0029] Furthermore, the parameter w of the GTrFN evaluation model of the risk factor T of the failure mode i is eTi The calculation formula is:

[0030]

[0031] Among them, u Ti (x) is the risk factor function of the risk factor T of failure mode i based on generalized trapezoidal fuzzy numbers, w is the correction coefficient, z Ti is the confidence level of risk factor T of failure mode i.

[0032] Furthermore, step S5 includes the following steps:

[0033] Calculate the product of each risk factor of failure mode i, the calculation formula is:

[0034]

[0035] Among them, E rpNi is the RPN value of failure mode i, and E RPNi With E Si Are they the same? If not, the risk assessment set of failure mode i is:

[0036]

[0037] If they are the same, the risk assessment set for failure mode i is:

[0038]

[0039] Among them, E RPNi =[e 1i ,e 2i ,e 3i ,e 4i ,w ei ].

[0040] Furthermore, the risk assessment value of each failure mode is calculated according to the risk assessment set of each failure mode, and the calculation formula is:

[0041]

[0042] Among them, d i is the risk assessment value of failure mode i, L(Θ) and L(U) are the fuzzy confidence intervals [a Ti ,b Ti ] and the length of T’s range [1, 10].

[0043] Compared with the prior art, the present invention has the following advantages:

[0044] (1) The present invention systematically examines the entire process of risk assessment of failure modes, analyzes the uncertainty factors and their interactions in the assessment process, starts from controlling the source factor of uncertainty, namely the uncertainty of the risk factor value criterion, formulates the T value criterion in the form of FCIN, thereby controlling the uncertainty of the value criterion, and then constructs a T evaluation model combining primary and secondary dimensions to control the uncertainty of the risk factor evaluation model, and then constructs a risk factor comprehensive model based on GTrFN to achieve the purpose of controlling the uncertainty of the risk factor comprehensive model, and finally outputs an objective and accurate risk assessment result.

[0045] (2) The present invention proposes a multi-deterministic assessment method for the failure mode risk assessment process to systematically examine the implementation process of the failure mode risk assessment. It can help to more comprehensively identify the existing uncertainty influencing factors and their interactions, thereby scientifically and comprehensively improving the risk assessment results and the risk identification of failure modes.

[0046] (3) The present invention combines the data of the photovoltaic air-conditioning system and adopts the evaluation method of the main evaluation dimension and multiple auxiliary evaluation dimensions to construct a risk factor assessment model for each failure mode, and comprehensively evaluates the risk through the generalized trapezoidal fuzzy number (GTrFN) method, thereby realizing a comprehensive risk assessment of the failure mode of the photovoltaic air-conditioning system.

[0047] (4) The present invention systematically evaluates various failure modes in the photovoltaic air-conditioning system by constructing a comprehensive evaluation model based on risk factors such as severity, occurrence frequency and detection, which can effectively identify potential risks and ensure the safety and reliability of the system.

[0048] (5) The present invention uses generalized trapezoidal fuzzy numbers (GTrFN) to evaluate risk factors, which can provide more accurate risk assessment results when there is a large amount of uncertainty. Fuzzy mathematical methods can be used to process fuzzy and uncertain information and provide risk analysis that is more in line with actual conditions.

[0049] (6) The present invention can dynamically identify and prioritize high-risk failure modes by calculating risk assessment sets and sorting risk assessment values, thereby providing important data support for the operation, maintenance and optimization of photovoltaic air-conditioning systems and reducing the probability of system failures.

[0050] (7) The present invention can provide targeted prevention and maintenance measures for the photovoltaic air-conditioning system through early identification and accurate evaluation of failure modes, thereby reducing the occurrence of system failures, improving the operating efficiency of the system, and extending the service life of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 is a flow chart of the method of the present invention;

[0052] Figure 2 It is a control framework diagram of multiple uncertainties of the present invention;

[0053] Figure 3 A T evaluation model diagram consisting of a main evaluation dimension and multiple auxiliary evaluation dimensions of the present invention;

[0054] Figure 4 It is an explanatory diagram of the parameters related to the GTrFN model of the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0056] Embodiment 1:

[0057] This embodiment provides a failure mode risk assessment method for a photovoltaic air conditioning system. Figure 1 As shown, the following steps are included:

[0058] Step S1: Acquire photovoltaic air-conditioning system data, where the photovoltaic air-conditioning system data includes photovoltaic air-conditioning system design, manufacturing data and usage data.

[0059] Step S2: constructing a risk assessment model for each risk factor of each failure mode according to the photovoltaic air conditioning system data through an evaluation method of one main evaluation dimension and multiple auxiliary evaluation dimensions;

[0060] Step S3: Determine the risk value range, value deviation trend, and confidence level of each risk factor of each failure mode through the risk assessment model of each risk factor of each failure mode;

[0061] Step S4: constructing a risk factor comprehensive model based on generalized trapezoidal fuzzy numbers for each risk factor of each failure mode according to the risk value range, value deviation trend, and confidence level of each risk factor of each failure mode;

[0062] Step S5: Calculate the risk assessment set of each failure mode according to the risk factor comprehensive model based on generalized trapezoidal fuzzy numbers of each risk factor of each failure mode;

[0063] Step S6: Calculate the risk assessment value of each failure mode according to the risk assessment set of each failure mode, sort the risk assessment values ​​of each failure mode from large to small, and obtain the ranking result of each failure mode risk from high to low.

[0064] Among them, risk factors include severity S, occurrence frequency O and detection degree D.

[0065] Among them, the risk value range, value deviation trend, and confidence level of each risk factor of each failure mode are:

[0066] {[a Ti ,b Ti ],y Ti ,z Ti}(T=S,O,D)

[0067] Among them, [a Ti ,b Ti ] is the risk value range of risk factor T of failure mode i, y Ti is the value deviation trend of the risk factor T of failure mode i, z Ti is the confidence level of the risk factor T of failure mode i, and its value range is set to (0.5,1], y Ti The value set of is:

[0068] {Extreme right (er), right (r), left (l), extreme left (el), center (m), 0}

[0069] The value 0 means that the values ​​of T are uniformly distributed in [a Ti ,b Ti ].

[0070] Among them, the comprehensive risk factor model based on generalized trapezoidal fuzzy numbers for each risk factor of each failure mode is: {E Si 、E Oi 、E Di},in,

[0071] E Ti =[e 1Ti ,e 2Ti ,e 3Ti ,e 4Ti ,w eTi ](T=S,O,D)

[0072] Among them, E Ti is the GTrFN evaluation model of risk factor T of failure mode i, e 1Ti ,e 2Ti ,e 3Ti ,e 4Ti ,w eTi are the parameters of the GTrFN evaluation model for risk factor T of failure mode i, respectively.

[0073] Among them, the parameter e of the GTrFN evaluation model of risk factor T of failure mode i is 1Ti ,e2Ti ,e 3Ti ,e 4Ti The calculation formula is:

[0074]

[0075] Among them, π αTi is the right fuzzy factor of risk factor T of failure mode i, π βTi is the left fuzzy factor of risk factor T of failure mode i.

[0076] Among them, the right fuzzy factor π of the risk factor T of failure mode i αTi and the left fuzzy factor π βTi According to the value deviation trend y Ti By y Ti The conversion rule table between the fuzzy factors is determined.

[0077] Among them, y Ti The conversion rule table between and fuzzy factors is as follows:

[0078]

[0079] Among them, y Ti is the value deviation trend of the risk factor T of failure mode i, π αTi is the right fuzzy factor of risk factor T of failure mode i, π βTi is the left fuzzy factor of risk factor T of failure mode i.

[0080] Among them, the parameter w of the GTrFN evaluation model of risk factor T of failure mode i is eTi The calculation formula is:

[0081]

[0082] Among them, u Ti (x) is the risk factor function of the risk factor T of failure mode i based on generalized trapezoidal fuzzy numbers, w is the correction coefficient, z Ti is the confidence level of risk factor T of failure mode i.

[0083] Wherein, step S5 comprises the following steps:

[0084] Calculate the product of each risk factor of failure mode i, the calculation formula is:

[0085]

[0086] Among them, E RPNi is the RPN value of failure mode i, and E RPNi With E SiAre they the same? If not, the risk assessment set of failure mode i is:

[0087]

[0088] If they are the same, the risk assessment set for failure mode i is:

[0089]

[0090] Among them, E RPNi =[e 1i ,e 2i ,e 3i ,e 4i ,w ei ].

[0091] Among them, the risk assessment value of each failure mode is calculated according to the risk assessment set of each failure mode, and the calculation formula is:

[0092]

[0093] Among them, d i is the risk assessment value of failure mode i, L(Θ) and L(U) are the fuzzy confidence intervals [a Ti ,b Ti ] and the length of T’s range [1, 10].

[0094] Embodiment 2:

[0095] The parts not mentioned in this embodiment are the same as those in Embodiment 1.

[0096] This embodiment provides a failure mode risk assessment method for a photovoltaic air conditioning system, comprising the following steps:

[0097] First, this example analyzes the uncertainties in the risk assessment process and their interactions:

[0098] The risk assessment process is a process in which experts analyze risk factors one by one according to the risk factor evaluation model through the risk factor value criteria specified in advance, obtain the risk factor evaluation results, and then sort the risk values ​​calculated by the risk factor comprehensive model from large to small to determine the final risk size. The risk factor is the three dimensions of measuring the failure mode risk, severity S, occurrence frequency O and detection D. The true risk value of the failure mode is R t After a series of risk assessment processes, the failure mode risk assessment value obtained is R e The difference between the two is the result of the combined influence of many uncertain factors in the risk assessment process, such as Figure 2As shown, the failure mode is the manifestation of the product or manufacturing process failing to achieve its specified function. The integer value criteria of the risk factor given in qualitative language are not only broad, but also the ambiguity of the impact level and the corresponding connotation expression defined by the language description makes the value criteria have inherent uncertainty. According to the input condition of the pre-set risk factor value criteria, the evaluation result of the risk factor is given based on the risk factor evaluation model. The uncertainty of the value criteria and the uncertainty of the risk factor evaluation model will lead to uncertainty in the evaluation opinion of the risk factor. The risk factor value criteria are S, O and D according to the integer value of 1 to 10 points or other forms of value. The risk factor evaluation model is to analyze according to a certain dimension to obtain the specific value of the risk factor. The evaluation opinion of the risk factor is the evaluation result of the risk factor finally determined according to the value criteria and the risk factor evaluation model. The evaluation opinion of the risk factor is calculated by the risk factor comprehensive model to obtain the risk assessment result of the failure mode. The uncertainty of the comprehensive model will eventually cause the uncertainty of the failure mode risk assessment result. The risk factor comprehensive model is a combination of three risk factors, usually an integer product model of risk factors.

[0099] Therefore, this embodiment proposes a method for controlling uncertainty of risk factor value criteria, including the following steps:

[0100] The risk factors S, O and D are uniformly represented by T, which controls the ambiguity of the quantitative evaluation of T by the evaluator and realizes the control of the uncertainty of the T value criterion;

[0101] The value criterion of T is formulated in the form of fuzzy confidence interval number (FCIN) to control the uncertainty of the value criterion. The formula is as follows:

[0102] {[a Ti ,b Ti ],y Ti ,z Ti}(T=S,O,D)

[0103] Among them, [a Ti ,b Ti ] is the risk value range of risk factor T of failure mode i, y Ti is the value deviation trend of the risk factor T of failure mode i, z Ti is the confidence level of the risk factor T of failure mode i, and its value range is set to (0.5,1], y Ti The value set of is:

[0104] {Extreme right (er), right (r), left (l), extreme left (el), center (m), 0}

[0105] The value 0 means that the values ​​of T are uniformly distributed in [a Ti ,b Ti ].

[0106] For the value of FCIN of the risk factor, this embodiment provides the following value-taking method, including:

[0107] Based on the value criteria of FCIN of T, a T evaluation model consisting of a main evaluation dimension and multiple auxiliary evaluation dimensions is designed to standardize the evaluator's analysis process and realize the uncertainty control of the evaluation model, such as Figure 3 As shown, the data of the photovoltaic air conditioning system is obtained, and the estimated value of the true value of T is given according to the data of the photovoltaic air conditioning system according to the main evaluation dimension, and the value interval of the true value estimate is obtained by correcting it through the auxiliary dimension. Ti ,b Ti ], according to the interval of the estimated value of the true value of T [a Ti ,b Ti ] Determine y one by one Ti 、z Ti The value of is obtained to obtain the FCIN evaluation opinion of T.

[0108] After determining the FCIN assessment opinion, the risk of failure mode of the photovoltaic air conditioning system is assessed through the following steps:

[0109] Constructing a risk factor comprehensive model based on generalized trapezoidal fuzzy number (GTrFN) to control the uncertainty of the risk factor comprehensive model;

[0110] The FCIN evaluation opinion about T is converted into the GTrFN evaluation result, and its membership function is given as follows: {E Si 、E Oi 、E Di},in,

[0111] E Ti =[e 1Ti ,e 2Ti ,e 3Ti ,e 4Ti ,w eTi ](T=S,O,D)

[0112] Among them, E Ti is the GTrFN evaluation model of risk factor T of failure mode i, e 1Ti ,e 2Ti ,e 3Ti ,e 4Ti ,w eTi are the parameters of the GTrFN evaluation model for risk factor T of failure mode i, respectively.

[0113] Parameter e of the GTrFN assessment model for risk factor T of failure mode i 1Ti ,e 2Ti ,e 3Ti ,e 4Ti The calculation formula is:

[0114]

[0115] Among them, π αTi is the right fuzzy factor of risk factor T of failure mode i, π βTi is the left fuzzy factor of risk factor T of failure mode i.

[0116] The right fuzzy factor π of the risk factor T of failure mode i αTi and the left fuzzy factor π βTi According to the value deviation trend y Ti By y Ti The conversion rule table between the fuzzy factors is determined.

[0117] y Ti The conversion rule table between and fuzzy factors is as follows:

[0118] Table 1y Ti Conversion rule table between fuzzy factors

[0119]

[0120] Among them, y Ti is the value deviation trend of the risk factor T of failure mode i, π αTi is the right fuzzy factor of risk factor T of failure mode i, π βTi is the left fuzzy factor of risk factor T of failure mode i.

[0121] Confidence z Ti Defined as the interval [a Ti ,b Ti ] belongs to the fuzzy subset consisting of the possible values ​​of T [1, 10], w eTi The calculation formula is:

[0122]

[0123] Among them, u Ti (x) is the risk factor function of the risk factor T of failure mode i based on generalized trapezoidal fuzzy numbers, and w is the correction coefficient, whose value is Figure 4 The rectangle shown The ratio of the area of ​​the figure enclosed by the rectangle He1e4I, z Ti is the confidence level of risk factor T of failure mode i.

[0124] Considering that the industrial standard generally predetermines the improvement threshold of the risk factor S of the failure mode, and when the RPN value (the product of S, O and D) and the S value are the same, it is necessary to further compare the S×O value, so the risk assessment results of the failure mode are calculated separately here, and the formula is as follows:

[0125]

[0126] Among them, E RPNi is the RPN value of failure mode i, and E RPNi With E Si Are they the same? If not, the risk assessment set of failure mode i is:

[0127]

[0128] If they are the same, the risk assessment set for failure mode i is:

[0129]

[0130] Among them, E RPNi =[e 1i ,e 2i ,e 3i ,e 4i ,w ei ].

[0131] The distance from the GTrFN centroid to the coordinate origin after comprehensive correction of non-membership and hesitation, namely the fuzzy characteristic distance d, is used as the risk assessment result of the failure mode. The formula is as follows:

[0132]

[0133]

[0134] Among them, d i is the risk assessment value of failure mode i, L(Θ) and L(U) are the fuzzy confidence intervals [a Ti ,b Ti ] and the length of the value range of T [1, 10]. Sort the d values ​​of each failure mode from large to small to obtain the ranking results of the failure mode risks from high to low.

[0135] Verify the effectiveness of the method:

[0136] Based on the actual application scenario of the photovoltaic air-conditioning system, the above method is applied to obtain its final failure mode risk assessment result.

[0137] The differentiation rate P of the on-site failure mode risk assessment results is calculated. The calculation formula of the differentiation rate is as follows:

[0138]

[0139] Where: N represents the total number of field failure modes that need to be distinguished, and n represents the number of field failure modes that are completely distinguished by the risk assessment results based on the evaluation index. The effectiveness of the present invention is verified by comparing the difference in field failure mode distinction rate between the present invention and the integer RPN method.

[0140] Embodiment 3:

[0141] The parts not mentioned in this embodiment are the same as those in Embodiment 1.

[0142] A failure mode risk assessment method for a photovoltaic air conditioning system, the specific implementation steps of which are as follows:

[0143] Step 1: Uncertain factors in the risk assessment process and their interactions:

[0144] First, the evaluation results of risk factors are the result of the decision-making of the evaluator team. The uncertainty is affected by the composition of team members (knowledge structure, professional skills and experience accumulation), analysis methods and many uncertain factors in their application process. However, the impact of such uncertain factors can be effectively controlled through scientific personnel allocation, systematic knowledge training and strict method implementation procedures.

[0145] Secondly, the uncertainty of the risk factor value criteria is the source factor that leads to the uncertainty of the final failure mode risk assessment results. Risk factors S, O and D play an extremely important role in the assessment process. Their evaluation opinions are given based on the set risk factor assessment model and the value criteria. The impact of the uncertainty of the value criteria and the uncertainty of the risk factor assessment model is comprehensively reflected in the evaluation opinions, and this uncertainty is further combined with the uncertainty of the risk factor comprehensive model, reflecting the uncertainty of the final failure mode risk assessment results. Therefore, it is necessary to comprehensively consider the relationship between the three in order to effectively control the uncertainty of the entire assessment process, such as Figure 2 shown.

[0146] Step 2: Control of uncertainty in the risk factor value criteria:

[0147] The photovoltaic air conditioning system consists of two subsystems: photovoltaic power generation and air conditioning. Its actual application scenarios are the indoor and outdoor environments of specific buildings. The indoor environment involves the users or customers of the system and their property, while the outdoor environment involves public property and public environment. Based on its actual application scenarios, Tables 2, 3, 4 and 5 respectively formulate the value criteria for the risk factors of its component field failure modes.

[0148] Table 2 Criteria for selecting the evaluation parameters of risk factor S

[0149]

[0150]

[0151]

[0152] Table 3 Criteria for selecting the evaluation parameters of risk factor O

[0153]

[0154]

[0155] Table 4 Criteria for selecting the evaluation parameters of risk factor D

[0156]

[0157]

[0158] Note: ● represents μ S The estimated value of the true value; ☆☆☆ and ⊕⊕⊕ represent a S The value accuracy requirements of b are the highest and lowest, and b s The value accuracy requirements are the lowest and highest; ⊙⊙ represents a S and b S have the same medium precision requirement; ** and * indicate that y must be given and should be given, respectively S The estimated value of .

[0159] Table 5 Evaluation parameters Z T The value criteria of (T=S,O,D)

[0160]

[0161] Note: When the interval length is 0, the interval estimation degenerates into a point estimation; ⊙⊙⊙ indicates that the value accuracy has the highest requirement.

[0162] Step 3: Control the uncertainty of the risk factor evaluation model:

[0163] Based on the FCIN evaluation model, the evaluator determines the a in the model one by one according to the established value criteria table for each evaluation parameter. T , b T ,y T and z T Table 6 gives the evaluation results of the risk factors of various field failure modes. The risk factor evaluation results of the risk assessment method of the integer value method (IVM) given by the industrial FMEA standard are also given in this table.

[0164] Table 6 Evaluation results of risk factors of field failure modes

[0165]

[0166] Step 4: Control the uncertainty of the risk factor comprehensive model:

[0167] According to the evaluation opinions of the risk factors of each field failure mode given in Table 6, the risk assessment results of each field failure mode can be obtained by using the comprehensive method proposed in this paper and the IVM risk priority number RPN method, as shown in Table 7:

[0168] Table 7 Risk assessment results of field failure modes

[0169]

[0170] Step 5: Verify the effectiveness of the method:

[0171] According to the principle of sorting the risk assessment results from large to small, the field failure mode risk ranking results based on different evaluation indicators can be obtained. Table 8 gives the failure mode risk ranking results and the corresponding differentiation rate.

[0172] Table 8 Ranking of field failure mode risk assessment results based on each evaluation index

[0173]

[0174]

[0175] It can be seen from the table that the evaluation results d calculated based on the present invention are RPN* The differentiation rate of on-site failure modes reached 90.91%, which is much higher than the differentiation rate of 36.36% of RPN based on the existing industrial FMEA standard IVM evaluation result. The difference in the differentiation rate of on-site failure modes by this method shows that the system of the present invention comprehensively controls the multiple uncertainties in the risk assessment process and can provide more objective and accurate evaluation results.

[0176] The multiple uncertainty control method for failure mode risk assessment can help to more comprehensively identify the existing uncertainty influencing factors and their interactions, thereby obtaining more accurate and objective failure mode risk assessment results.

[0177] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0178] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A failure mode risk assessment method for a photovoltaic air conditioning system, characterized in that: The following steps are involved: Step S1: Acquire photovoltaic air conditioning system data; Step S2: constructing a risk assessment model for each risk factor of each failure mode according to the photovoltaic air conditioning system data through an evaluation method of one main evaluation dimension and multiple auxiliary evaluation dimensions; Step S3: Determine the risk value range, value deviation trend, and confidence level of each risk factor of each failure mode through the risk assessment model of each risk factor of each failure mode; Step S4: constructing a risk factor comprehensive model based on generalized trapezoidal fuzzy numbers for each risk factor of each failure mode according to the risk value range, value deviation trend, and confidence level of each risk factor of each failure mode; Step S5: Calculate the risk assessment set of each failure mode according to the risk factor comprehensive model based on generalized trapezoidal fuzzy numbers of each risk factor of each failure mode; Step S6: Calculate the risk assessment value of each failure mode according to the risk assessment set of each failure mode, sort the risk assessment values ​​of each failure mode from large to small, and obtain the ranking result of each failure mode risk from high to low.

2. A failure mode risk assessment method for a photovoltaic air conditioning system according to claim 1, characterized in that: The risk factors include severity S, occurrence frequency O and detection D.

3. A failure mode risk assessment method for a photovoltaic air conditioning system according to claim 1 or 2, characterized in that: The risk value range, value deviation trend and confidence level of each risk factor of each failure mode are as follows: {[a Ti ,b Ti ],y Ti ,z Ti }(T=S,O,D) Among them, [a Ti ,b Ti ] is the risk value range of risk factor T of failure mode i, y Ti is the value deviation trend of the risk factor T of failure mode i, z Ti is the confidence level of the risk factor T of failure mode i, and its value range is set to (0.5,1], y Ti The value set of is: {Extreme right (er), right (r), left (l), extreme left (ek), center (m), 0} The value 0 means that the values ​​of T are uniformly distributed in [a Ti ,b Ti ].

4. A failure mode risk assessment method for a photovoltaic air conditioning system according to claim 3, characterized in that: The comprehensive risk factor model based on generalized trapezoidal fuzzy numbers for each risk factor of each failure mode is: Si 、E Oi 、E Di },in, E Ti =[e 1Ti ,e 2Ti ,e 3Ti ,e 4Ti ,w eTi ](T=S,O,D) Among them, E Ti is the GTrFN evaluation model of risk factor T of failure mode i, e 1Ti , e 2Ti , e 3Ti , e 4Ti , w eTi are the parameters of the GTrFN evaluation model for risk factor T of failure mode i, respectively.

5. A failure mode risk assessment method for a photovoltaic air conditioning system according to claim 4, characterized in that: The parameter e of the GTrFN evaluation model for the risk factor T of the failure mode i is 1Ti , e 2Ti , e 3Ti , e 4Ti The calculation formula is: Among them, π αTi is the right fuzzy factor of risk factor T of failure mode i, π βTi is the left fuzzy factor of risk factor T of failure mode i.

6. A failure mode risk assessment method for a photovoltaic air conditioning system according to claim 5, characterized in that: The right fuzzy factor π of the risk factor T of the failure mode i αTi and the left fuzzy factor π βTi According to the value deviation trend y Ti By y Ti The conversion rule table between the fuzzy factors is determined.

7. A failure mode risk assessment method for a photovoltaic air conditioning system according to claim 6, characterized in that: The y Ti The conversion rule table between and fuzzy factors is as follows: Among them, y Ti is the value deviation trend of the risk factor T of failure mode i, π αTi is the right fuzzy factor of risk factor T of failure mode i, π βTi is the left fuzzy factor of risk factor T of failure mode i.

8. A failure mode risk assessment method for a photovoltaic air conditioning system according to claim 4, characterized in that: The parameter w of the GTrFN evaluation model for the risk factor T of the failure mode i is eTi The calculation formula is: Among them, u Ti (x) is the risk factor function of the risk factor T of failure mode i based on generalized trapezoidal fuzzy numbers, w is the correction coefficient, z Ti is the confidence level of risk factor T of failure mode i.

9. A failure mode risk assessment method for a photovoltaic air conditioning system according to claim 1, characterized in that: The step S5 comprises the following steps: Calculate the product of each risk factor of failure mode i, the calculation formula is: Among them, E RPNi is the RPN value of failure mode i, and E RPNi With E Si Are they the same? If not, the risk assessment set of failure mode i is: If they are the same, the risk assessment set for failure mode i is: Among them, E RPNi =[e 1i , e 2i , e 3i , e 4i , w ei ].

10. A failure mode risk assessment method for a photovoltaic air conditioning system according to claim 1 or 9, characterized in that: The risk assessment value of each failure mode is calculated according to the risk assessment set of each failure mode, and the calculation formula is: Among them, d i is the risk assessment value of failure mode i, L(Θ) and L(U) are the fuzzy confidence intervals [a Ti ,b Ti ] and the length of T’s range [1, 10].

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