Fault tree generation device and fault tree generation method

Abstract

To create a fault tree in which the causes of failure events are appropriately developed.SOLUTION: A fault tree generation device according to the present invention includes a formula database that stores formulas related to physical phenomena, a formula base causal model generation unit that generates a causal relationship of failure events based on the formulas, a formula base fault tree generation unit that generates a formula base fault tree that is a combination of the causal relationships of the failure events, and an output unit that outputs the formula base fault tree.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a fault tree generation device and a fault tree generation method. [Background technology]

[0002] A method called Fault Tree Analysis (FTA) is used to investigate the causes of product defects and to identify the causes of failures that may occur during the design stage. FTA is an analytical technique that systematically explores the causes of product defects by taking a product defect, sequentially identifying the causes of the failure, and developing them hierarchically.

[0003] The result of this analysis is a tree structure with the product failure event at the top and its causes at the lower levels. This tree structure is called a fault tree. The product failure event at the top of the fault tree is called the top event (the starting point on the upper side). In a fault tree, the causes at the lower levels than the top event are called intermediate events.

[0004] The knowledge information conversion device of Patent Document 1 converts knowledge information in a rule format into knowledge information in a directed graph format. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Publication No. 7-262019 Summary of the Invention [Problem to be solved by the invention]

[0006] When creating a fault tree, it is desirable to properly identify the cause of a malfunction. If a cause is missed, the malfunction cannot be identified, which prolongs the investigation into the cause of the malfunction and leads to oversights in the design stage. Also, if duplication occurs, the same cause will be repeatedly investigated, resulting in wasted work.

[0007] Specifically, the knowledge information conversion device of Patent Document 1 can generate a causal relationship that the cause of "ignition flag failure" is "high exhaust gas cylinder temperature" from knowledge information in the form of a rule, "IF ignition flag failure - THEN exhaust gas cylinder temperature high." However, this technology does not know whether the cause of "ignition flag failure" is only "high exhaust gas cylinder temperature," in other words, whether the causal relationship has been properly developed.

[0008] Therefore, an object of the present invention is to create a fault tree in which the causes of malfunction events are properly expanded. [Means for solving the problem]

[0009] The fault tree generation device of the present invention includes a mathematical expression database storing mathematical expressions related to physical phenomena, a mathematical expression-based causal model generation unit that generates causal relationships of malfunction events based on the mathematical expressions, a mathematical expression-based fault tree generation unit that generates a mathematical expression-based fault tree combining the causal relationships of the malfunction events, and an output unit that outputs the mathematical expression-based fault tree. the mathematical-formula-based fault tree expresses both a resulting event and a causing event in terms of magnitude of a physical quantity, and the mathematical formula is a linear mathematical formula expressing the relationship between the physical quantity of the resulting event and the physical quantity of the causing event; It is characterized by: Other means will be described in the detailed description of the invention. [Effects of the Invention]

[0010] According to the present invention, it is possible to create a fault tree in which the causes of malfunction events are properly expanded. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 1 is a diagram illustrating a configuration of a fault tree generation device. [Figure 2] FIG. 10 is a diagram illustrating an example of a mathematical formula database. [Figure 3] FIG. 10 is a diagram illustrating an example of a causal model. [Figure 4A] FIG. 10 is a diagram illustrating a mathematical expression-based causal model generation process. [Figure 4B]FIG. 10 is a diagram illustrating a mathematical expression-based causal model generation process. [Figure 5] FIG. 10 is a diagram illustrating a mathematical expression-based causal model generation process. [Figure 6] This is an example of a formula-based causal model. [Figure 7] This is an example of a formula-based causal model. [Figure 8] This is an example of a formula-based causal model. [Figure 9] 1 is an example of a formula-based fault tree. [Figure 10] 1 is an example of a defect information-based causal model. [Figure 11] 1 is an example of a fault tree. [Figure 12] FIG. 1 is a diagram illustrating a configuration of a fault tree generation device. DETAILED DESCRIPTION OF THE INVENTION

[0012] Example 1 (Fault tree generator) Fig. 1 is a diagram illustrating the configuration of a fault tree generation device 101. The device includes a fault tree generation device 101 and an information terminal 102. These devices can communicate with each other via a network 103. The fault tree generation device 101 is a general computer, and includes a central control device 104, an input device 105, an output device 106, a main memory device 107, and an auxiliary memory device 108. These devices are interconnected by a bus.

[0013] The top event input unit 109, the mathematical expression-based causal model generation unit 110, the mathematical expression-based fault tree generation unit 111, the malfunction information-based causal model generation unit 112, the malfunction information-based causal model combination unit 113, and the output unit 119 in the main memory device 107 are programs. Hereinafter, when an entity is described as "XX unit," it means that the central control unit 104 reads each program from the auxiliary memory device 108, loads it into the main memory device 107, and then realizes the function of each program (described in detail later).

[0014] The top event input unit 109 accepts the top event input by the user. The mathematical formula database 114 stores mathematical formulas. The mathematical-expression-based causal model generation unit 110 generates a causal relationship from the relationship between the variables on the left side and the variables on the right side of the mathematical expression stored in the mathematical expression database 114. The causal relationship generated by the mathematical-expression-based causal model generation unit 110 is called a mathematical-expression-based causal model. The mathematical-expression-based causal model generation unit 110 stores the mathematical-expression-based causal model in a mathematical-expression-based causal model database 115. Details of the processing by the mathematical-expression-based causal model generation unit 110 and the form of the mathematical-expression-based causal model will be described later.

[0015] The mathematical-formula-based fault tree generation unit 111 searches the mathematical-formula-based causal model database 115 for a mathematical-formula-based causal model related to the top event received by the top event input unit 109, and generates a mathematical-formula-based fault tree by combining the causal relationships resulting from the search. The defect information database 116 stores defect information. The defect-information-based causal model generation unit 112 uses natural language processing to extract causal relationships of defect events described in the defect information stored in the defect information database 116, and generates a defect-information-based causal model. The defect information may be, for example, information describing a defect event in sentences. Furthermore, the defect information-based causal model generation unit 112 stores the defect information-based causal model in the defect information-based causal model database 117.

[0016] The malfunction information-based causal model combination unit 113 combines the mathematical expression-based fault tree generated by the mathematical expression-based fault tree generation unit 111 with the malfunction information-based causal models stored in the malfunction information-based causal model database 117, and outputs the combined result. The output unit 119 outputs various information including the formula-based fault tree to the output device 106 .

[0017] The information terminal 102 is also a general computer, and has a central control unit, an input device, an output device, a main memory device, and an auxiliary memory device (not shown) like the fault tree generation device 101. When the user cannot directly operate the fault tree generation device 101, the user uses the information terminal 102 to remotely operate the fault tree generation device 101 via the network 108.

[0018] (Formula Database) 2 is a diagram showing an example of the mathematical formula database 114. In the mathematical formula database 114, products and parts are stored in a product / parts column 202, left-hand side variables are stored in a left-hand side variable column 203, and right-hand side variables are stored in a right-hand side variable column 204, all in association with IDs stored in an ID (Identifier) ​​column 201. The ID in the ID column 201 is an identifier that uniquely identifies a formula. Here, the IDs "1", "2", "3", . . . identify the formulas (1), (2), (3), . . . The product or part in the product / part column 202 indicates the product or part to which the formula is applied. The left-hand side variable in the left-hand side variable column 203 is the variable on the left-hand side of the formula (objective variable). The right-hand variables in the right-hand variable column 204 are the variables (explanatory variables) on the right-hand side of the formula.

[0019] For example, in the case of a part in which a shaft is press-fitted into a cylinder, the friction force "F" generated on the shaft can be calculated using formula (1).

[0020]

number

[0021] Here, "μ" is the coefficient of friction between the cylinder and the shaft. "P" is the internal pressure between the cylinder and the shaft. "A" is the area of ​​the joint. "d1" is the inner diameter of the cylinder.

[0022] As information relating to formula (1), in the first line of the formula database 114, “fastening part between shaft and cylinder” 205 is stored in the product / parts column 202, “shaft: friction force” 206 is stored in the variable column 203 on the left side, and “between cylinder and shaft: friction coefficient, between cylinder and shaft: internal pressure, joint: area, cylinder: inner diameter” 207 is stored in the variable column 204 on the right side.

[0023] The variables on the left and right sides are stored as pairs of the target part and variable in the format "part:variable". For example, the right-hand variable "friction coefficient between cylinder and shaft" is stored in the format "between cylinder and shaft: friction coefficient". If there are multiple right-hand variables, each right-hand variable is stored separated by a comma.

[0024] In addition, the internal pressure "P" between the cylinder and the axis can be calculated using equation (2).

[0025]

number

[0026] Here, "d1" is the inner diameter of the cylinder. "d2" is the outer diameter of the cylinder. "E" is the elastic modulus of the shaft. "Δ" is the interference of the shaft.

[0027] As information relating to formula (2), in the second line of the formula database 114, “fastening part between shaft and cylinder” 208 is stored in the product / parts column 202, “internal pressure between cylinder and shaft” 209 is stored in the variable column 203 on the left side, and “cylinder: inner diameter, cylinder: outer diameter, shaft: elastic coefficient, shaft: interference” 210 is stored in the variable column 204 on the right side.

[0028] Furthermore, the area "A" of the joint is calculated by equation (3).

[0029]

number

[0030] Here, "d1" is the inner diameter of the cylinder, and "L" is the insertion length of the shaft.

[0031] As information regarding formula (3), in the third line of the formula database 114 (FIG. 2), “Fastening part between shaft and cylinder” 211 is stored in the product / parts column 202, “Joint: area” 212 is stored in the variable column 203 on the left side, and “Cylinder: inner diameter, shaft: insertion length” 213 is stored in the variable column 204 on the right side.

[0032] (Formulas and variables) The mathematical formula in Example 1 is a mathematical formula related to a physical phenomenon. This mathematical formula can be said to be a law of nature that is guaranteed and proven to always hold true. Generally, products are designed and operated using such mathematical formulas. The mathematical formula here may be an equation or an inequality. Furthermore, the mathematical formula contains multiple variables. Generally, a mathematical formula can be transformed without changing its content. Depending on the transformation method, the variables and their number on the left side change, and the variables and their number on the right side also change.

[0033] For ease of understanding, the mathematical expressions in Example 1 include one variable on the left side and one or more other variables on the right side, as in mathematical expressions (1) to (3). The variables included on the left side (variables on the left side) are also called "objective variables," and the variables included on the right side (variables on the right side) are also called "explanatory variables." The explanatory variables correspond to the causes of a causal relationship, and the objective variables correspond to the results of the causal relationship. The objective variables and explanatory variables are any physical quantities indicated by a product.

[0034] (Preventing omissions and duplication of defect events) A person viewing Formulas (1) to (3) will notice the existence of all objective variables and all explanatory variables. For example, it is not normal for a person to notice the existence of “μ” and “P” in Formula (1) while failing to notice the existence of “A” and “d1.” Furthermore, “P,” one of the explanatory variables in Formula (1), is the objective variable of Formula (2). Similarly, “A,” another explanatory variable in Formula (1), is the objective variable of Formula (3). In other words, “P” and “A” are included in the hierarchy immediately below “F.” These prevent omissions and duplications of defect events at the same hierarchy. Therefore, the output unit 119 may output Formulas (1) to (3) on the output device 106 or the information terminal 102 to prompt the user to confirm that there are no omissions or duplications of defect events.

[0035] (causal model) The mathematical-formula-based causal model generation unit 110 generates a mathematical-formula-based causal model from the relationship between the objective variables and explanatory variables of the mathematical formulas stored in the mathematical formula database 114. The defect-information-based causal model generation unit 112 extracts, using natural language processing, the causal relationships of defect events described in defect information stored in the defect information database 116, and generates a defect-information-based causal model. Here, a causal model is a model of the causal relationships.

[0036] FIG. 3 is a diagram showing an example of a causal model. Each element (e.g., reference numeral 301) constituting the causal model is an event composed of a part (e.g., reference numeral 302) and a phenomenon (e.g., reference numeral 303). Since "events" occurring in the real world are represented by "elements" of the causal model, Example 1 uses both "event" and "element," but the essential meaning of both is the same. These elements are expressed in a chain in the order of causal relationships. In the example of FIG. 3, the right element (cause) causes the left element (result). The elements are connected by an AND condition 304 and an OR condition 305. The AND condition 304 indicates that the left element is caused when all of the connected elements occur. The OR condition 305 indicates that the left element is caused when at least one of the connected elements occurs.

[0037] 3, when phenomenon D309 occurs in part D308 or phenomenon E311 occurs in part E310, phenomenon B307 occurs in part B306. Furthermore, when phenomenon B307 occurs in part B306 and phenomenon C313 occurs in part C312, phenomenon A303 occurs in part A302.

[0038] A causal model generated from a mathematical formula is called a mathematical formula-based causal model, and a causal model generated from the causal relationships of malfunction events described in malfunction information is called a malfunction information-based causal model.

[0039] (Formula-based causal model generation processing) The mathematical-expression-based causal model generation unit 110 generates a mathematical-expression-based causal model from the relationship between the objective variables and explanatory variables of the mathematical expressions stored in the mathematical expression database 114. This process consists of the following three steps. During these steps, reference is made to Figures 4A, 4B, and 5.

[0040] (Step 1) The mathematical-formula-based causal model generation unit 110 regards the inadequacy of the objective variable as a malfunction event, and further generates an element with a large objective variable and an element with a small objective variable as causes of the malfunction. Specifically, for example, in the mathematical formula (4) described below, the mathematical formula-based causal model generation unit 110 adds the character string "inadequacy" to the objective variable "part A: variable A" as shown in FIG. 4A to make it "part A: variable A inadequacy" 401. Each variable in the mathematical formula (4) is expressed in the format of "part: variable". For example, the left side of the mathematical formula (1) is expressed in the format of "shaft: frictional force".

[0041] Each variable (objective variable and explanatory variable) has a “normal range” within which the product operates normally. The normal range is the range between an arbitrary upper reference value and an arbitrary lower reference value within the range of physical quantities that the product can exhibit. If the value of a variable falls outside the normal range, a malfunction occurs in the product. The value of a variable falls outside the normal range when the value of the variable is greater than the upper reference value of the normal range or when the value of the variable is smaller than the lower reference value of the normal range. For this reason, the mathematical-formula-based causal model generation unit 110 generates the elements “Part A: Variable A Large” 402 and “Part A: Variable A Small” 403 by adding the strings “Large” and “Small” to “Part A: Variable A” as the cause of “Part A: Variable A Unsuitable” 401. The mathematical-formula-based causal model generation unit 110 may accept the upper reference value and the lower reference value as input by the user, or may automatically set them based on past experience without accepting them.

[0042]

number

[0043] The "f" on the right side of the formula (4) represents a function. The formula (4) has one objective variable and N explanatory variables.

[0044] (Step 2) The mathematical-expression-based causal model generation unit 110 considers the cause of the large response variable and small response variable generated in step 1 to be when the explanatory variables become inappropriate, and generates the mathematical-expression-based causal model with the explanatory variable inappropriateness as an element. For example, in the case of mathematical expression (4), as shown in FIG. 4B , the mathematical-expression-based causal model generation unit 110 adds "Component B1: Variable B1 Inappropriate" 404, "Component B2: Variable B2 Inappropriate" 405, ..., "Component BN: Variable BN Inappropriate" 406 to "Component A: Variable A Small" 403 generated in step 1. In FIG. 4B , the descriptions of "Component B1: Variable B1 Inappropriate" 404, "Component B2: Variable B2 Inappropriate" 405, ..., "Component BN: Variable BN Inappropriate" 406 added to "Component A: Variable A Small" 403 are omitted.

[0045] (Step 3) The mathematical-formula-based causal model generation unit 110 generates elements of large explanatory variables and small explanatory variables as causes of the inadequacies of the explanatory variables generated in step 2. This is based on the idea that, similar to step 1, variables have normal ranges, and when the value of a variable falls outside that range, a defect occurs in the product. The cases where the value of a variable falls outside the normal range are when the value of the variable is greater than the upper reference value of the normal range, and when the value of the variable is smaller than the lower reference value of the normal range.

[0046] For example, in the case of Equation (4), as shown in Fig. 5, the mathematical-formula-based causal model generation unit 110 adds "Component B1: Variable B1 large" 501 and "Component B1: Variable B1 small" 502, etc. to "Component B1: Variable B1 unsuitable" 404, "Component B2: Variable B2 unsuitable" 405, ..., "Component BN: Variable BN unsuitable" 406 generated in step 2. Although omitted in Fig. 5, the same cause as "Component A: Variable A large" is also added to "Component A: Variable A small."

[0047] In steps 1 to 3, if there are a target variable and an explanatory variable, the mathematical-expression-based causal model generation unit 110 can generate a mathematical-expression-based causal model. In the first embodiment, the mathematical-expression-based causal model generation unit 110 acquires variables from the left-hand side variable column 203 and the right-hand side variable column 204 of the mathematical expression database 114 shown in Fig. 2, and generates a mathematical-expression-based causal model.

[0048] For example, when generating a causal model based on formula (1), the mathematical-formula-based causal model generation unit 110 obtains "axis: friction force" 206 from the variable column 203 on the left side and "between cylinder and axis: friction coefficient, between cylinder and axis: internal pressure, joint: area, cylinder: inner diameter" 207 from the variable column 204 on the right side to generate the mathematical-formula-based causal model.

[0049] 6, 7, and 8 are examples of mathematical-expression-based causal models generated based on mathematical expressions (1), (2), and (3), respectively. The mathematical-expression-based causal model generation unit 110 stores the generated mathematical-expression-based causal models in the mathematical-expression-based causal model database 115.

[0050] (Formula-based fault tree generation process) The mathematical-expression-based fault tree generation unit 111 searches the mathematical-expression-based causal model database 115 for a mathematical-expression-based causal model related to the top event received by the top event input unit 109. The mathematical-expression-based fault tree generation unit 111 generates a mathematical-expression-based fault tree by combining multiple mathematical-expression-based causal models found as a result of the search.

[0051] For example, when the top event input unit 109 receives "axis: inadequate friction force" as a top event, the mathematical-expression-based fault tree generation unit 111 searches for a mathematical-expression-based causal model including "axis: inadequate friction force" from the mathematical-expression-based causal model database 115. As a result, the mathematical-expression-based causal model (FIG. 6) generated based on the mathematical expression (1) is found to be the relevant model.

[0052] The mathematical-formula-based fault tree generator 111 then searches the mathematical-formula-based causal model database 115 for mathematical-formula-based causal models that include individual elements of the matched mathematical-formula-based causal model. As a result of a search using "Between cylinder and shaft: large internal pressure", which is one of the elements of the mathematical-formula-based causal model generated based on formula (1), the mathematical-formula-based causal model (FIG. 7) generated based on formula (2) is matched. As a result of a search using "Joint: large area", which is one of the elements of the mathematical-formula-based causal model generated based on formula (1), the mathematical-formula-based causal model (FIG. 8) generated based on formula (3) is matched.

[0053] The mathematical-expression-based fault tree generation unit 111 combines these corresponding mathematical-expression-based causal models (FIGS. 7 and 8) with the mathematical-expression-based causal model (FIG. 6) generated based on the mathematical expression (1). The mathematical-expression-based fault tree generation unit 111 further searches the individual elements of the combined mathematical-expression-based causal models, and if any corresponding mathematical-expression-based causal models are found, combines them. The result of repeating this process is called a mathematical-expression-based fault tree.

[0054] Figure 9 is an example of a mathematical expression-based fault tree. The mathematical expression-based fault tree in Figure 9 is the result of combining the mathematical expression-based causal model in Figure 6 with the mathematical expression-based causal model in Figure 7 and the mathematical expression-based causal model in Figure 8, and expresses resulting events and causing events in terms of the magnitude of physical quantities. By performing the above process, a fault tree can be generated for all top events without omissions or duplications.

[0055] (Fault information-based causal model generation processing) The defect-information-based causal model generation unit 112 uses natural language processing to extract causal relationships between defect events described in defect information stored in the defect information database 116, and generates a defect-information-based causal model. The defect information is a sentence describing an actual defect event and its cause. For example, a sentence such as "The coefficient of friction between the cylinder and the shaft became small due to an inappropriate shaft material" or "The cylinder's inner diameter became large due to a design error in the cylinder" is described.

[0056] 10 shows an example of a defect information-based causal model. The defect information-based causal model generation unit 112 generates defect information-based causal models 1001 and 1002 (FIG. 10) from defect information by natural language processing. Specifically, the defect information-based causal model generation unit 112 extracts components and phenomena from natural language text, sets highly correlated components and phenomena from the extracted results as events (elements), and analyzes the causal relationships between the events to arrange the events in order of causal relationships.

[0057] If the defect information is "the coefficient of friction between the cylinder and the shaft has become small due to an inappropriate material of the shaft," the defect-information-based causal model generation unit 112 extracts "shaft" and "between the cylinder and the shaft" as parts from the wording of this defect information, and extracts "inappropriate material" and "small coefficient of friction" as phenomena. The defect-information-based causal model generation unit 112 may extract parts and phenomena by comparing the wording of the defect information with a dictionary of parts and phenomena prepared in advance.

[0058] The defect information based causal model generation unit 112 then calculates the degree of association based on the number of words between the extracted parts and phenomena, and pairs parts and phenomena with high degrees of association. For example, the defect information based causal model generation unit 112 pairs "shaft" and "unsuitable material" to create an element "shaft: unsuitable material", and pairs "between cylinder and shaft" and "small coefficient of friction" to create an element "between cylinder and shaft: small coefficient of friction".

[0059] The defect information-based causal model generation unit 112 then recognizes that there is a causal relationship between a phrase such as "due to" included in the defect information and the phrase before and after it, and generates a defect information-based causal model (e.g., reference numeral 1001 in FIG. 10 ). The defect information-based causal model generation unit 112 may recognize the causal relationship by comparing the phrase in the defect information with a dictionary of causal relationship expressions prepared in advance. The defect information-based causal model generation unit 112 stores the generated defect information-based causal model in the defect information-based causal model database 117.

[0060] (Fault information-based causal model combination processing) The malfunction information-based causal model combination unit 113 combines the mathematical expression-based fault tree generated by the mathematical expression-based fault tree generation unit 111 with the malfunction information-based causal models stored in the malfunction information-based causal model database 117, and outputs the combined result.

[0061] For example, when the mathematical expression-based fault tree is as shown in Fig. 9, the malfunction information-based causal model combination unit 113 searches the malfunction information-based causal model database 117 for the elements "between cylinder and shaft: small friction coefficient" and "cylinder: large inner diameter" contained therein. As a result, malfunction information-based causal models 1001 and 1002 in Fig. 10 are found to be matched. The malfunction information-based causal model combination unit 113 combines these with the mathematical expression-based fault tree in Fig. 9 to generate a fault tree (Fig. 11).

[0062] FIG. 11 is an example of the fault tree described above.

[0063] The output device 106 displays the mathematical expression-based fault tree (FIG. 9) and the fault tree (FIG. 11) on the screen. By combining the mathematical expression-based fault tree (FIG. 9) with the malfunction information-based causal model (FIG. 10), it is possible to generate a fault tree that includes detailed malfunction events that cannot be expressed by mathematical expressions alone. By the above processing, it is possible to generate a fault tree in which the causes of malfunction events are expanded without omission or duplication.

[0064] Example 2 (Fault tree generator) FIG. 12 is also a diagram for explaining the configuration of the fault tree generating device 101. The configuration of the fault tree generation device 101 of the second embodiment is the same as that of the first embodiment, except that a score calculation unit 118 (FIG. 1) is added in the second embodiment. In the second embodiment, the score calculation unit 118 calculates and displays a score for each element of the generated fault tree. The score calculation unit 118 may display causes having a high or low score with respect to an arbitrary criterion in a specific manner. Displaying in a specific manner includes hiding causes having a low score (correlation) with respect to the arbitrary criterion, and highlighting causes having a high score with respect to the arbitrary criterion. There are two methods for calculating the score:

[0065] The first method is a method that uses a mathematical formula. For example, the result of generating a mathematical formula-based causal model based on mathematical formula (1) is shown in Figure 6. The score calculation unit 118 ultimately generates two elements, "Between cylinder and shaft: large friction coefficient" and "Between cylinder and shaft: small friction coefficient," as the cause of "shaft: large friction force."

[0066] In formula (1), when the explanatory variable "between cylinder and shaft: coefficient of friction" increases, the objective variable "shaft: friction force" increases. Conversely, when the explanatory variable "between cylinder and shaft: coefficient of friction" decreases, the objective variable "shaft: friction force" decreases. In other words, "between cylinder and shaft: large coefficient of friction" is more likely to be the cause of "shaft: large friction force". In this case, the score for "between cylinder and shaft: large coefficient of friction" is larger than the score for "between cylinder and shaft: small coefficient of friction". In this way, the score calculation unit 118 calculates the score from the effect on the objective variable when the value of the explanatory variable is changed.

[0067] The second method is to use past performance, i.e., past defect information. Specifically, the score calculation unit 118 calculates the score based on the number of defect information items in which a defect event and a candidate event for its cause are simultaneously described in the fault tree. The larger the number of items, the higher the score.

[0068] For example, the score calculation unit 118 searches the defect information database 116 to find the number of defect information items that include both "shaft: large frictional force" and "between cylinder and shaft: large friction coefficient," as well as the number of defect information items that include both "shaft: large frictional force" and "between cylinder and shaft: small friction coefficient." If there are many defect information items that include both "shaft: large frictional force" and "between cylinder and shaft: large friction coefficient," it can be said that "between cylinder and shaft: large friction coefficient" is more appropriate or likely to be the cause of "shaft: large frictional force," and the score for this is also high.

[0069] As is clear from the above, the score is the correlation between the resulting events and the causal events of the fault tree. By calculating the score for each element in this way, it is possible to, for example, delete or disable elements with low scores. This prevents the fault tree from diverging instead of converging, and allows the generation of a fault tree consisting only of elements that are truly meaningful to the user.

[0070] The fault tree generation devices of the first and second embodiments have the following advantages. (1) The fault tree generator can generate a complete fault tree from mathematical expressions that include variables that users tend to overlook. (2) The fault tree generator can utilize mathematical expressions that include the physical quantities of the effect and the physical quantities of the cause as variables. (3) The fault tree generator can combine the user's empirical knowledge with the theoretical fault tree generated from mathematical expressions. (4) The fault tree generator can define the occurrence of a malfunction as a case where the value of a variable becomes inappropriate. (5) The fault tree generator can define the inadequacy of a variable value as whether the variable value is large or small relative to a predetermined criterion. (6) The fault tree generator can calculate the utility value of causal relationships by calculating the correlation between effects and causes.

[0071] The present invention is not limited to the above-described embodiments, but includes various modifications. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to those including all of the described configurations. Furthermore, it is possible to replace part of the configuration of one embodiment with the configuration of another embodiment, or to add the configuration of another embodiment to the configuration of one embodiment. Furthermore, it is possible to add, delete, or replace part of the configuration of each embodiment with other configurations. [Explanation of symbols]

[0072] 101 Fault Tree Generator 102 Information terminal 103 Network 104 Central Control Unit 105 Input Device 106 Output Device 107 Main storage 108 Auxiliary storage 109 Top Event Input Unit 110 Formula-based causal model generation unit 111 Formula-based fault tree generator 112 Defect information-based causal model generation unit 113 Defect Information-Based Causal Model Combination Unit 114 Formula Database 115 Formula-based causal model database 116 Defect Information Database 117 Defect Information-Based Causal Model Database 118 Score Calculation Section 119 Output section

Claims

1. a mathematical formula database storing mathematical formulas relating to physical phenomena; a mathematical formula-based causal model generation unit that generates a causal relationship of a malfunction event based on the mathematical formula; a mathematical expression-based fault tree generator that generates a mathematical expression-based fault tree that combines the causal relationships of the malfunction events; an output unit for outputting the formula-based fault tree; Equipped with The formula-based fault tree Both the resulting event and the cause event are expressed in terms of the magnitude of physical quantities, The formula is: It is a linear mathematical formula, expressing the relationship between the physical quantities of the resulting event and the physical quantities of the causal event; A fault tree generation device comprising:

2. a fault-information-based causal model combination unit that generates a fault tree by combining causal relationships extracted from fault information describing an actually occurring fault event and the cause of the fault event with the mathematical-formula-based fault tree; 2. The fault tree generator according to claim 1, wherein:

3. The malfunction event is: The value of the objective variable of the formula is inappropriate, The cause of the above malfunction is: the value of the explanatory variable in the formula becomes inappropriate; 2. The fault tree generating device according to claim 1,

4. The cases where the values ​​of the objective variable and the explanatory variable of the formula become inappropriate are as follows: The value of the objective variable and the value of the explanatory variable of the formula are large or small relative to an arbitrary criterion, 4. The fault tree generator according to claim 3, wherein:

5. Calculating correlations between the resulting events and the causal events of the fault tree from the defect information or from the relationship between the objective variable and the explanatory variable of the mathematical formula; a score calculation unit that displays in an arbitrary manner the cause of whether the correlation is large or small relative to an arbitrary standard; 3. The fault tree generator according to claim 2, wherein:

6. The formula database is It stores mathematical formulas related to physical phenomena, The mathematical formula-based causal model generation unit Generate a causal relationship of the malfunction event based on the formula; The formula-based fault tree generator generating a formula-based fault tree that combines the causal relationships of the failure events; The output device is outputting the formula-based fault tree; The formula-based fault tree Both the resulting event and the cause event are expressed in terms of the magnitude of physical quantities, The formula is: It is a linear mathematical formula, expressing the relationship between the physical quantities of the resulting event and the physical quantities of the causal event; A fault tree generation method characterized by:

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