JP7357494B2 - diagnostic system - Google Patents

Description

TECHNICAL FIELD The present invention relates to a diagnostic system.

Conventionally, a diagnostic system for detecting equipment failure has been proposed (see, for example, Patent Document 1). The diagnostic system of Patent Document 1 includes a plurality of gear motors and the like included in a device as diagnosis target elements, and includes a plurality of sensors and a plurality of processing units respectively corresponding to the plurality of diagnosis target elements. Then, a sensor is attached to each element to be diagnosed, and a processing unit repeatedly executes a diagnostic process for each element to be diagnosed based on the output of the sensor.

JP 2018-173333 Publication

In a device including a plurality of components and in which the plurality of components operate in cooperation, a change in the state of one component may affect other components.

An object of the present invention is to provide a diagnostic system that can perform diagnosis taking into account the influence between multiple components.

One diagnostic system according to the present invention is
a plurality of sensors that are provided corresponding to the plurality of components of the device to be diagnosed and output state detection information of each corresponding component;
an abnormality determination unit that determines whether or not there is an abnormality in each component based on the state detection information of each component;
an abnormality prediction unit that predicts the next component to be determined to have an abnormality when one component is determined to have an abnormality by the abnormality determination unit;
Equipped with

Another diagnostic system according to the present invention is
a plurality of sensors that are provided corresponding to the plurality of components of the device to be diagnosed and output state detection information of each corresponding component;
an influence calculation unit that calculates the influence between one specified component among the plurality of components and each other component;
Equipped with

According to the present invention, it is possible to provide a diagnostic system that can perform diagnosis in consideration of the influence between multiple components.

FIG. 1 is a block diagram showing a diagnostic system and a diagnostic target device according to an embodiment. It is a flowchart of the diagnostic processing performed by the abnormality determination part and the abnormality prediction part. FIG. 3 is a diagram illustrating a process of calculating the degree of risk of a certain component by an abnormality prediction unit. FIG. 3 is a diagram illustrating the degree of risk of each component calculated by the abnormality prediction unit. FIG. 7 is an image diagram showing an example of the output of a component predicted to cause an abnormality next. 3 is a flowchart of setting input processing executed by the input processing section. FIG. 3 is an image diagram showing a setting input screen for an arithmetic expression. FIG. 7 is an image diagram showing a setting input screen for a coefficient matrix. It is a flowchart of the influence degree output process performed by the influence degree calculation part. FIG. 3 is a diagram illustrating a process of calculating the degree of influence by the degree of influence calculating section. FIG. 3 is an image diagram showing an example of an influence level output screen.

Embodiments of the present invention will be described in detail below with reference to the drawings.

FIG. 1 is a block diagram showing a diagnostic system and a diagnostic target device according to an embodiment.

The diagnostic system 1 of this embodiment is installed alongside a diagnostic target device 100 having a plurality of components 111, and detects an abnormality in each component 111, notifies a component 111 with a high risk of failure next, and detects an abnormality in each component 111. This system calculates and displays the degree of influence between In this embodiment, the device 100 to be diagnosed is a belt conveyor having a plurality of gear motors. Diagnosis target device 100 is not limited to this, and may be any device as long as it includes a plurality of components 111 that operate in cooperation with each other mechanically or electrically. Hereinafter, a plurality of gear motors that drive the belt will be described as a plurality of components 111.

The diagnostic system 1 includes a plurality of sensors 11 provided corresponding to the plurality of components 111, a computer 20 that receives outputs (state detection information) of the plurality of sensors 11 and performs diagnostic processing on the diagnostic target device 100, and information , and an input device 40 such as a pointing device and a keyboard that can input data into the computer 20 from outside the system.

The sensor 11 attached to each component 111 may be one type of sensor such as a vibration sensor, or may include multiple types of sensors such as a temperature sensor, a motor current sensor, and a sensor for iron powder concentration in lubricating oil. Good too. Furthermore, the same type of sensor may be provided at multiple locations on one component. The type of sensor is not limited to the above specific example.

The computer 20 includes an abnormality determination unit 21 that determines whether an abnormality has occurred in any of the components 111 based on the outputs of the plurality of sensors 11, and a system that determines the degree of risk of the next abnormality occurring when one component 111 is determined to be abnormal. An anomaly prediction unit 22 that predicts a high component 111, and an influence calculation unit 23 that calculates the degree of influence between any one component 111 and each other component 111 based on a request from an operator and outputs it from the display device 30. , an input processing unit 24 that can change settings related to calculating the degree of risk or influence, a storage device 27 that stores control data, and an interface 28 that takes in signals from the plurality of sensors 11. The control data stored in the storage device 27 includes a relationship data table T1 used by the abnormality prediction section 22 and the influence degree calculation section 23, a state degree calculation control data DB1, a coefficient matrix M3, and a reason database DB2. included. The computer 20 includes a CPU (Central Processing Unit), a storage device 27 storing control data and programs, and a RAM (Random Access Memory), and includes the above-mentioned abnormality determination section 21, abnormality prediction section 22, and influence calculation section 23. The input processing unit 24 is a software function module realized by the CPU executing a program.

<Diagnostic processing>
Next, detailed operations of each part will be explained with reference to flowcharts. FIG. 2 is a flowchart of diagnostic processing executed by the abnormality determination section and the abnormality prediction section.

While the diagnostic target device 100 is in operation, the abnormality determining unit 21 receives the outputs of the plurality of sensors 11 (S1), and performs a process of determining whether an abnormality has occurred in each component 111 (Step S2). If there is no component 111 determined to be abnormal, the abnormality determination unit 21 repeats the processing of steps S1 and S2.

The determination of an abnormality in step S2 is a determination of a failure that makes the component 111 inoperable, a determination of an abnormality that is close to a failure, a determination of an abnormality that indicates the need for maintenance, or a determination of an abnormality that does not reach the level of requiring maintenance. Good too. In other words, the abnormality determination unit 21 only needs to determine whether or not a predetermined abnormality condition is met, and the abnormality condition is not particularly limited.

In step S2, the abnormality determination unit 21 compares, for example, the output value of each sensor 11 with a preset threshold indicating an abnormality, and when the output value of a certain sensor 11 exceeds the threshold, the abnormality determination unit 21 11 is determined to be abnormal. Alternatively, the abnormality determination unit 21 compares the trend of the output value of the sensor 11 with a pattern indicating an abnormality in the trend, and if the two match, determines that the component 111 to which the sensor 11 is attached is abnormal. It's okay. Alternatively, when a plurality of sensors 11 of different types or installation positions are attached to each component 111, the abnormality determination unit 21 combines the outputs of the plurality of sensors 11, their trends, or both, It may be calculated using a predetermined determination formula or subjected to a predetermined determination process, and the occurrence of an abnormality may be determined based on these results.

When the abnormality determining unit 21 determines that any one component 111 is abnormal, the abnormality determining unit 21 notifies the abnormality (step S3). For example, the abnormality determination unit 21 displays and outputs the occurrence of the abnormality and identification information of the component in which the abnormality has occurred from the display device 30 as notification of the abnormality.

When the abnormality determination unit 21 determines an abnormality and reports the abnormality, the abnormality prediction unit 22 next detects one component 111 in which an abnormality has occurred (hereinafter referred to as “abnormal component 111”) and each other. Taking into account the degree of relationship with the component 111, the degree of risk of each other component 111 where an abnormality will occur is calculated next (step S4). Next, the calculation process of the degree of risk will be explained in detail.

<Risk calculation process>
FIG. 3 is a diagram illustrating a process of calculating the degree of risk of a certain component by the abnormality prediction unit. Hereinafter, in order to identify each of the plurality of components 111, the plurality of components 111 will also be expressed as components A to E. FIG. 3 shows an example of calculating the degree of risk that an abnormality will occur in component B next when an abnormality occurs in component A.

<Relationship>
The abnormality prediction unit 22 uses the relationship data table T1 (FIG. 1) in which each parameter value of the relationship between the plurality of components 111 is registered in order to calculate the degree of risk in step S4.

In the relationship data table T1, values are registered that represent the relationship between each pair of components 111 divided into a plurality of items. The relationship list L1 in FIG. 3 is a list of relationship values extracted between component A and component B from the relationship data table T1. The items of the relationship data table include a distance coefficient, a correlation coefficient, a transmission rate, etc., as shown in the relationship list L1 of FIG. The relationship data table T1 includes all combinations of components A to E ({A, B}, {A, C}, {A, D}, {A, E}, {B, C}, {B, D}, {B, E}, {C, D}, {C, E}, {D, E}), the values of the same items as in the relationship list L1 in FIG. 3 are registered. Each value of the relationship data table T1 is obtained by investigating the diagnosis target device 100 and is registered in advance. The relationship list L1 corresponds to an example of relationship information according to the present invention.

The distance coefficient is, for example, a normalized value representing a spatial distance. Not limited to this, the relationship data table T1 includes various types of distances, such as distances along power transmission paths, distances along belts, distances between electric wires connecting each other, and values related to insulation creepage distance between control boards. Coefficient items may also be included. Further, as the distance coefficient, a value related to a distance in a certain distance space constructed according to an arbitrary theory may be adopted. For example, when the causal relationships among various physical phenomena of all components 111 are expressed as a network, the minimum number of nodes in the network of causal relationships connecting a predetermined physical phenomenon of one component to a predetermined physical phenomenon of another component 111, etc. may be used as one distance coefficient.

The correlation coefficient is a coefficient indicating a cross-correlation between a predetermined physical quantity (for example, the magnitude of vibration) of one component 111 and a predetermined physical quantity (for example, the magnitude of vibration) of the other component 111. The relationship data table T1 may also include cross-correlation items regarding various physical quantities such as motor rotation speed, total number of motor rotations, and temperature. The cross-correlation may be a cross-correlation between different physical quantities, such as the temperature of one component 111 and the magnitude of vibration of another component.

The transmission rate is, for example, a torque transmission rate. Not limited to this, the relationship data table T1 includes various other transmission rate items such as energy transmission rate, power supply transmission rate, or signal transmission rate when signals are serially transmitted. You may be

<State degree>
In order to calculate the degree of risk in step S4, the abnormality prediction unit 22 further calculates the current state degree list L2 (FIG. 3) of the component B to be calculated. The state level list L2 has a plurality of items, and the value of each item for component B is determined from the sensor output of one or more sensors 11 attached to component B. In order for the abnormality prediction unit 22 to obtain each state degree, the state degree calculation control data DB1 stores data of one or more statistical processing programs and arithmetic expressions. The state level list L2 corresponds to an example of state level information according to the present invention.

Items in the state level list L2 include abnormality level, load level, failure level based on expert knowledge, and the like.

The degree of abnormality is a degree indicating how close the output of a predetermined sensor 11 is to a value indicating an abnormality. The state degree list L2 may include a plurality of items of abnormality degrees corresponding to a plurality of sensors 11 of different types or installation positions. Further, the degree of abnormality may include a degree of abnormality over time obtained by statistically processing the output values of the sensor 11 from the past to the present. The abnormality degree obtained by statistical processing corresponds to an example of the statistical state degree according to the present invention. The degree of abnormality may be expressed as a percentage, or may be expressed in stages such as high, medium, or low, to show how close the parameter for determining whether or not something is abnormal is to a threshold value indicating abnormality. Good too.

The load degree is a degree indicating the mechanical or electrical load on the component B at the present time, which is calculated from the output of the sensor 11. The state level list L2 may include a plurality of load level items for different locations and different objects in one component B.

The degree of failure based on expert knowledge indicates a degree (deterioration degree) that is close to a failure formulated theoretically or based on know-how. It is calculated by applying (substituting) the output value of No. 11. The items in the state degree list L2 may include various kinds of state degree items that are state quantities formulated theoretically or based on know-how and that can be calculated by substituting the output value of the sensor 11 into the calculation formula. good. The failure degree based on expert knowledge or the state degree obtained by an arithmetic expression corresponds to an example of the logical state degree according to the present invention.

<Relationship coefficient>
In order to calculate the degree of risk in step S4, the abnormality prediction unit 22 further uses the coefficient matrix M3. The coefficient matrix M3 includes (i×j) relationship coefficients α _fg . Here, f=1 to i, g=1 to j, i is the number of items in the state degree list L2, and j is the number of items in the relationship degree list L1. The relationship coefficient α _fg is a value that relatively represents how much the combination of the f-th item of the condition list L2 and the g-th item of the relationship list L1 is related to the failure risk of the component 111. Set. For example, the combination of kinetic energy transfer rate (for example, the 4th item in the relationship list) and vibration abnormality level (5th item in the condition list) has a strong influence on the risk of failure. The relationship coefficient between these items is a large value (for example, α ₅₄ =0.7), and the kinetic energy transfer rate (the fourth item in the relationship list) and the abnormality of the amount of noise in the electrical signal (for example, in the state degree list) Since the combination with the sixth item) has a weak influence on the risk of failure, the relationship coefficient between this item is a small value (α ₆₄ =0.1).

Each relationship coefficient α _fg of the coefficient matrix M3 is a value determined by the contents of the items in the relationship list L1 and the item in the status list L2, and depends on the component 111 to be calculated or the component 111 determined to be abnormal. It is a value that does not occur.
<Risk Matrix>
In order to calculate the degree of risk in step S4, the abnormality prediction unit 22 first performs a calculation in which the degree of abnormality of the component A determined to be abnormal is multiplied by the above-mentioned relationship list L1, state degree list L2, and coefficient matrix M3. conduct. In the calculation, the abnormality degree of component A is multiplied by each component in the list, the relationship list L1 and the state degree list L2 are tensor-producted, and the product of the tensor product result and the coefficient matrix M3 is calculated between the corresponding components. Let it be the multiplication of . Through such calculation, a risk matrix M10 having the number of components of (number of items in relationship list L1) x (number of items in state list L2) is obtained. The value of each item in the relationship list L1 is normalized, the value of each item in the state degree list L2 is normalized, and the relationship coefficients α _fg are set to relative values. Therefore, the values of each component of the risk matrix M10 are values that can be compared with each other in terms of the degree of risk. That is, the value of each component is an index representing how much the items in the relationship list L1 and the items in the state list L2 corresponding to each component contribute to the risk of causing the next abnormality.

After the risk matrix M10 is calculated in step S4, the abnormality prediction unit 22 further converts the risk matrix M10 into a scalar, and sets this value as the risk that an abnormality will occur in component B next. As a method of scalarization, for example, the sum of all components of the risk matrix M10, the sum of all components and its standardization, etc. can be adopted.

In step S4, the abnormality prediction unit 22 similarly calculates the risk matrix M10 and the scalar risk for each of the other components C, D, and E. FIG. 4 is a diagram illustrating the degree of risk of each component calculated by the abnormality prediction unit. As shown in FIG. 4, the degree of risk calculated for each component B to E is a value calculated taking into account the relationship between component A, which has been determined to be abnormal, and each of the other components B to E. , represents the possibility that an abnormality will occur next due to an abnormality in component A. The values of the relationship list L1 and the state list L2 used in the calculation of the risk matrix M10 differ for each component to be calculated, so the risk matrix M10 and the risk values differ for each of the components B to E.

<Reason for the degree of risk>
After calculating the risk matrix M10 and risk level of each component B to E, the abnormality prediction unit 22 extracts the reason for the risk level from the reason database DB2 (step S5). In the reason database DB2, descriptions of reasons that match the corresponding component are registered in association with the position (number of rows and number of columns) of each component of the risk matrix M10. In other words, the value of each component of the risk matrix M10 indicates how much a certain item in the relationship list L1 and a certain item in the status list L2 corresponding to each component contribute to the risk of causing the next abnormality. It is an indicator of whether the Therefore, when any component shows a high value, it means that the synergistic effect between the relationship degree item and the state degree item corresponding to this component is increasing the risk level. Therefore, the content showing this synergistic effect is adopted as the reason for corresponding to this component.

In step S5, the abnormality prediction unit 22 searches the risk matrix M10 of each component B to E for components whose component values exceed the threshold value, and extracts the reason corresponding to the component value exceeding the threshold value from the reason database DB2. do. If the values of all components of the risk matrix M10 for a certain component 111 are smaller than the threshold value, no reason is extracted for the component 111.

<Output of risk ranking table>
FIG. 5 is an image diagram showing an example of the output of a component predicted to cause an abnormality next. Next, the abnormality prediction unit 22 creates data for a risk ranking table H1 that associates each component B to E with the risk calculated in step S4 and the reason extracted in step S5, It is output from the display device 30 (step S6). The risk ranking table H1 may be sorted in order of risk. The risk ranking table H1 may not include information on all components, but may include information only on components with a high risk. When outputting the display of the risk ranking table H1, the notification display J1 of the abnormal component in step S3 may also be performed. The notification display J1 may include a display item of the degree of abnormality of the abnormal component. By displaying the degree of abnormality, the severity of the abnormality can be shown to the user. The risk ranking table H1 in FIG. 5 shows that the abnormality prediction unit 22 has predicted component C as the component that will cause an abnormality next because it has the highest degree of risk.

By finding the component C with the highest degree of risk from the risk ranking table H1, the operator can recognize that the component C is the component predicted to cause an abnormality next after the abnormality of component A. . Furthermore, from the description of the reason, the operator can recognize what is having a major influence and increasing the risk of the abnormality occurring. The operator can plan an efficient maintenance schedule or an efficient parts replacement schedule for the diagnosis target device 100 from this support information.

<Setting input processing>
FIG. 6 is a flowchart of setting input processing executed by the input processing section.

In the diagnostic system 1 of this embodiment, the operator can change the settings related to the calculation of the degree of risk described above using the function of the input processing section 24 (FIG. 1). The objects whose settings can be changed are the arithmetic expression of "failure degree based on expert knowledge" in the state degree list L2 and each relational coefficient α _fg of the coefficient matrix M3.

FIG. 7 is an image diagram showing a setting input screen for an arithmetic expression.

When the operator selects execution of setting input processing from the system menu, the input processing section 24 starts the setting input processing. In the setting input process, first, the input processing unit 24 performs a selection input process in which the operator selects between changing the settings of the arithmetic expression and changing the settings of the coefficient matrix M3 (step S11). As a result, when the setting change of the arithmetic expression is selected, the input processing unit 24 outputs the setting input screen G1 of FIG. 7 to the display device 30 (step S12). The setting input screen G1 includes an explanatory notation N1 of variables that can be used in the arithmetic expression, an input box N2 in which the arithmetic expression can be input, and a command button N3 in which input completion or input cancellation can be selected. Here, the operator inputs a newly applied arithmetic expression into the input box N2 of the setting input screen G1, and selects and operates the input completion command button N3.

Then, the input processing unit 24 determines the selection operation (step S13), and if the input is completed, the input content of the input box N2 is used as the failure degree calculation formula based on expert knowledge and is stored in the state degree calculation control data DB1. The data of the arithmetic expression is rewritten (step S14). Then, the setting input process ends.

In addition, in the above-mentioned setting input process, an example was shown in which the setting of the arithmetic expression for the failure degree based on expert knowledge is changed among the data included in the state degree calculation control data DB1. However, if the state degree calculation control data DB1 includes an arithmetic expression for calculating the state degree of another item, the setting of this arithmetic expression may also be changed by the same process.

FIG. 8 is an image diagram showing a coefficient matrix setting input screen.

In the selection input process of step S1, when the operator selects to change the settings of the coefficient matrix M3, the input processing section 24 first outputs the setting input screen G2 of FIG. 8 to the display device 30 (step S15). The setting input screen G2 includes a table N11 having the number of rows and columns corresponding to the coefficient matrix M3, and a command button N12 that allows selection of completion of input or cancellation of input. Furthermore, each cell of the table N11 includes a display N14 of the item name of the relationship degree and the item name of the state degree corresponding to the relationship coefficient α _fg , and an input box N15 for the relationship coefficient α _fg . Here, the operator inputs the updated value of each relationship coefficient α _fg into each input box N15 in table N11, and selects the input completion command button N12.

Then, the input processing unit 24 determines the selection operation (step S16), and if the input is completed, rewrites each relationship coefficient α _fg of the coefficient matrix M3 in the storage device 27 with the input contents of the input box N15 (step S17). ). The value of the relationship coefficient α _fg is a value that determines the calculation formula for the degree of risk, and changing the setting of the relationship coefficient α _fg corresponds to changing the setting of the calculation formula for the degree of risk. Then, the setting input process ends.

According to the calculation formula setting input process shown in FIG. 7, if improvements are found in the calculation formula representing the degree of failure or the calculation formula representing the degree of some kind of condition based on expert knowledge, the calculation formula is corrected. As a result, it becomes possible to express a more accurate failure degree or state degree.

According to the setting input process of the coefficient matrix M3 shown in FIG. 8, how much the synergy between each item in the relationship list L1 and each item in the state list L2 is related to the risk of causing the component 111 to malfunction. If there is new knowledge, or if it is desired to adjust the relationship coefficient α _fg based on the actual calculation results, each relationship coefficient α _fg of the coefficient matrix M3 can be modified as appropriate. Thereby, the accuracy of the risk matrix M10 calculated using the coefficient matrix M3 can be further improved.

<Calculation and output of influence between components>
FIG. 9 is a flowchart of the influence degree output process executed by the influence degree calculation unit 23. FIG. 10 is a diagram illustrating the process of calculating the degree of influence by the degree of influence calculating section. FIG. 11 is an image diagram showing an example of the influence degree output screen. The diagnostic system 1 of this embodiment uses the function of the influence calculation unit 23 (FIG. 1) to determine the relationship between a certain component 111 and each other component 111 at a stage when no abnormality has occurred in any of the plurality of components 111. It is possible to calculate and display the degree of influence between In the following, a case will be described in which component A is designated as an influence source and the degree of influence from component A to each of the other components B to E is calculated and output. The designated component is also referred to as a "designated component."

When the operator selects execution of the influence degree output process from the system menu, the influence degree calculation unit 23 starts the influence degree output process shown in FIG. Then, the influence calculation unit 23 first outputs the influence output processing screen G3 of FIG. 11 from the display device 30 (step S21). Note that screen G3 in FIG. 11 shows the display output after calculating the degree of influence, and in step S21, input box N21 and result display frame N23 are left blank.

The influence level output processing screen G3 includes an input box N21 for selecting a designated component, a command button N22 for indicating completion or cancellation of the designation, and a result display frame N23 for outputting the calculation result. On this screen, first, the operator specifies the component 111 that will have an influence using the input box N21. Then, the user selects and operates the command button N22 to complete the designation. Then, the influence calculation unit 23 determines the selection operation (step S22), and if the designation is complete, calculates the influence from the designated component A to each of the other components B to E (step S23). Further, when the command button N22 for completing the specification is selected, the abnormality prediction unit 22 or the influence calculation unit 23 calculates the abnormality degree of the specified component A, and calculates the abnormality degree of the specified component A as the abnormality in the result display frame N23. It may be output to the degree column N23a. The degree of abnormality in the degree of abnormality column N23a may be displayed as a percentage or in stages.

Next, a method for calculating the degree of influence on one component B will be explained. The calculation of the degree of influence is similar to the calculation of the degree of risk described above. The influence calculation unit 23 multiplies the relationship list L1 in FIG. 10, the status list L2, and the coefficient matrix M3 to obtain an influence matrix M20. The relationship list L1 is a list obtained by extracting a portion indicating the relationship between the designated component A and the calculation target component B from the relationship data table T1, and is the same as the relationship list L1 in FIG. 3. The state degree list L2 is a list showing the state degrees of the component B to be calculated, and is the same as the state degree list L2 in FIG. Coefficient matrix M3 is the same as coefficient matrix M3 in FIG. The multiplication method is also the same, and the influence matrix M20 is the same as the value of the risk matrix M10 in FIG. 3, except that the value of each component does not include the product of the abnormality degrees of component A.

The influence calculation unit 23 further converts the influence matrix M20 in FIG. 11 into a scalar as in the case of FIG. 3, and obtains this value as the influence from the designated component A to the component B. In step S23, the influence calculation unit 23 similarly calculates the influence for the other components C to E. As a result of the calculation, the degrees of influence from the specified component A to each of the other components B to E are obtained.

Once the degree of influence on the other components B to E has been calculated, the degree of influence calculation unit 23 outputs the image Z24 showing the degree of influence from the display frame N23 (step S24). The image Z24 shows, for example, symbols P1 to P5 indicating a plurality of components A to E, a correspondence line L indicating a combination of two of the plurality of components A to E, and a correspondence relationship between the combinations for which the degree of influence has been calculated. and an influence degree display V attached to the line L. In the example of FIG. 11, since the degree of influence from the designated component A to each of the other components B to E has been calculated, the degree of influence display V is outputted along with the correspondence relationship line L between these components.

The operator can recognize how much a change in the state of designated component A will impose on each of the other components B to E from the calculation result of the degree of influence. Then, the operator can plan an efficient maintenance schedule or an efficient parts replacement schedule for the device 100 to be diagnosed using this information as support information.

As described above, according to the diagnostic system 1 of the present embodiment, the abnormality prediction section 22 or the influence degree calculation section 23 calculates the degree of risk or influence of a failure in consideration of the relationship between the plurality of components 111 of the device 100 to be diagnosed. Calculate. Therefore, based on these calculation results, it is possible to diagnose the diagnosis target device 100 in consideration of the influence between multiple components.

The embodiments of the present invention have been described above. However, the present invention is not limited to the above embodiments. For example, the items in the relationship list L1 used to calculate the risk of the next abnormality occurring are not limited to the example of the embodiment, and include various items as long as they are information indicating relationships between components. It may be The items in the status list L2 are not limited to the examples of the embodiment, and may include various items as long as they indicate the status of the component 111 obtained from the output of the sensor 11 attached to each component 111. It's okay. In addition, the details shown in the embodiments, such as the formula for calculating the degree of risk and the formula for calculating the degree of influence, can be changed as appropriate without departing from the spirit of the invention. In addition, in the above embodiment, a belt conveyor and a gear motor are taken as an example of a diagnosis target device and a plurality of components. may be applied, and various elements such as various mechanical parts such as a chain sprocket, various electrical parts such as a control board, etc. may be applied as the components.

1 Diagnostic System 11 Sensor 20 Computer 21 Abnormality Judgment Unit 22 Abnormality Prediction Unit 23 Influence Calculation Unit 24 Input Processing Unit 27 Storage Device T1 Relationship Data Table DB1 Control Data for State Level Calculation M3 Coefficient Matrix DB2 Reason Database L1 Relationship List L2 Status list M10 Risk matrix M20 Impact matrix J1 Abnormal component notification display H1 Risk ranking table G1, G2 Setting input screen G3 Impact output processing screen N21 Input box for specified component Z24 Image showing impact 30 Display Device 40 Input device 100 Diagnosis target device 111 Component

Claims (8)

a plurality of sensors that are provided corresponding to the plurality of components of the device to be diagnosed and output state detection information of each corresponding component;
an abnormality determination unit that determines whether or not there is an abnormality in each component based on the state detection information of each component;
an abnormality prediction unit that predicts the next component to be determined to have an abnormality when one component is determined to have an abnormality by the abnormality determination unit;
A diagnostic system equipped with The abnormality prediction unit includes relationship degree information indicating a relationship of each other component to the abnormal component determined to be abnormal by the abnormality determination unit among the plurality of components, and the state detection information of each of the other components. Predicting the component in which an abnormality will occur next based on the state degree information of the component obtained based on
The diagnostic system according to claim 1. The relationship degree information includes information regarding the distance between the abnormal component and each of the other components, information regarding the cross-correlation between the first physical quantity of the abnormal component and the second physical quantity of each of the other components, and the Information regarding the transmission rate of the third physical quantity between the abnormal component and each of the other components, or information on a plurality of these, is included.
The diagnostic system according to claim 2. The state degree information is indicated by a statistical state degree obtained by statistically processing the state detection information, a logical state degree obtained by applying the state detection information to a pre-stored arithmetic expression, and the state detection information. Contains the load degree indicating the current burden, or information on multiple of these,
The diagnostic system according to claim 2 or claim 3. The abnormality prediction unit calculates the degree of risk that an abnormality will occur next by applying the state degree information and the relationship degree information to a pre-stored calculation formula,
comprising an input processing unit that can change the calculation formula from outside the system;
The diagnostic system according to any one of claims 2 to 4. The abnormality prediction unit predicts the next component to be determined to have an abnormality, including the influence of the period during which the abnormality of the component continues, when one component is determined to have an abnormality by the abnormality determination unit.
The diagnostic system according to any one of claims 1 to 5. a plurality of sensors that are provided corresponding to the plurality of components of the device to be diagnosed and output state detection information of each corresponding component;
an influence calculation unit that calculates the influence between one specified component among the plurality of components and each other component;
A diagnostic system equipped with The influence degree calculation unit is configured to calculate the degree of influence based on relationship degree information indicating the relationship of each other component to the specified component, and state degree information of the component obtained based on the state detection information of each of the other components. and calculate the degree of influence.
The diagnostic system according to claim 7.

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