KR102269622B1 - Elevator maintenance work support device - Google Patents

Abstract

An object of the present invention is to provide a maintenance work support apparatus (1) for an elevator (2) capable of setting the priority of treatment in consideration of the accuracy of treatment. The maintenance work support apparatus 1 includes a history storage unit 102 , a search unit 103 , a calculation unit 107 , and a priority setting unit 108 . The history storage unit 102 stores information on the failure and information on treatment for the failure. The search unit 103 searches the history storage unit 102 for a failure similar to the failure indicated by the input failure data. The calculation unit 107 calculates a recurrence rate for each failure treated by the search unit 103 for the failure searched by the search unit 103 . The recurrence rate indicates the rate of occurrence of similar failures within a predetermined period after the failure. The priority setting unit 108 sets the priority of the treatment with respect to the failure indicated by the failure data based on the recurrence rate calculated by the calculation unit 107 for each treatment.

Description

Elevator maintenance work support device

The present invention relates to an elevator maintenance work support device.

The example of the maintenance work support apparatus is described in patent document 1. The maintenance work support apparatus memorize|stores the information of the abnormality which occurred and the information of the treatment with respect to the said abnormality as a past case. The maintenance work support apparatus sets priorities to past cases based on the correlation with input data.

Japanese Patent No. 5820072 Publication

However, the maintenance work support apparatus described in patent document 1 does not consider the accuracy of a treatment. For this reason, it is possible to set a high priority to a treatment with a high probability of recurrence of a similar failure after a temporary recovery.

The present invention has been made to solve such a problem. It is an object of the present invention to provide an elevator maintenance work support apparatus capable of setting the priority of treatment in consideration of the accuracy of treatment.

The apparatus for supporting maintenance work for an elevator according to the present invention comprises: a history storage unit for storing information on failures and treatment information for the failure; and a search unit for retrieving failures similar to failures indicated by input data from the history storage unit; , based on a calculation unit that calculates the rate of occurrence of a failure similar to the failure within a predetermined period after the failure, for each failure searched by the search unit, for each treatment for the failure, and the recurrence rate calculated by the calculation unit for each treatment Accordingly, a priority setting unit for setting the priority of treatment for the failure indicated by the data is provided.

According to the present invention, the priority setting unit sets the priority of treatment based on the recurrence rate for each treatment. Thereby, it is possible to set the priority of the treatment in consideration of the accuracy of the treatment.

BRIEF DESCRIPTION OF THE DRAWINGS It is a block diagram of the maintenance work support apparatus which concerns on Embodiment 1. FIG.
2 is a diagram showing an example of a failure history database according to the first embodiment.
3 is a diagram showing an example of an elevator attribute database according to the first embodiment.
4 is a diagram showing an example of a pseudo-failure data table according to the first embodiment.
5 is a diagram showing an example of a combined data table according to the first embodiment.
6 is a diagram showing an example of data processing in the maintenance work support apparatus according to the first embodiment.
7 is a flowchart showing an example of the operation of the maintenance work support apparatus according to the first embodiment.
8 is a flowchart showing an example of the operation of the determination unit according to the first embodiment.
9 is a flowchart showing an example of the operation of the classification unit according to the first embodiment.
Fig. 10 is a diagram showing a hardware configuration of a main part of the maintenance work support apparatus according to the first embodiment.
It is a block diagram of the maintenance work support apparatus which concerns on Embodiment 2. FIG.
12 is a diagram showing an example of a recurrence rate database according to the second embodiment.
13 is a diagram showing an example of the recurrence rate calculated by the calculator according to the third embodiment.

EMBODIMENT OF THE INVENTION The form for implementing this invention is demonstrated, referring an accompanying drawing. In each drawing, the same code|symbol is attached|subjected to the same or corresponding part, and overlapping description is simplified or abbreviate|omitted suitably.

Embodiment 1.

BRIEF DESCRIPTION OF THE DRAWINGS It is a block diagram of the maintenance work support apparatus which concerns on Embodiment 1. FIG.

The maintenance work support device 1 is applied to the elevator 2 .

An elevator 2 is provided in the building 3 .

The building 3 has a plurality of floors. The hoistway 4 passes through each floor of the building 3 . Each of the plurality of platforms 5 is provided on each floor of the building 3 . Each of the plurality of platforms 5 faces the hoistway 4 . Each of the plurality of landings 5 is provided with a landing door 6 .

The elevator 2 has a car 7 , a counterweight 8 , a traction machine 9 , and a main rope 10 .

The car 7 is provided inside the hoistway 4 so that it can go up and down along a guide rail (not shown). The car 7 has a car door 11 . The car door 11 is comprised so that the boarding point door 6 can be linked|interlocked and it can open and close, when the car 7 is stopped on any floor among a plurality of floors. The counterweight 8 is provided inside the hoistway 4 to be able to move up and down along a guide rail (not shown). The hoisting machine 9 is provided in the upper part of the hoistway 4 . The main rope 10 is wound around the hoisting machine 9 and is hung. The main rope 10 is held at both ends by the car 7 and the counterweight 8, respectively.

The terminal device 12 is carried by the maintenance person 13 .

The maintenance work support apparatus 1 includes an input unit 101 , a history storage unit 102 , a search unit 103 , a determination unit 104 , an attribute storage unit 105 , and a classification unit 106 , , a calculation unit 107 , a priority setting unit 108 , and an output unit 109 .

The input unit 101 is connected to the terminal device 12 via the communication line 14 so that failure data can be input. The communication line 14 is, for example, an Internet line. The failure data is data indicating a failure occurring in the elevator 2 . The failure data includes information such as the type and condition of the failure. The failure data is input from the terminal device 12 by the maintenance person 13, for example. The fault data includes, for example, a fault code set for each type of fault or a free description text.

The history storage unit 102 is configured to store a failure history database. The failure history database is a database storing failure history data. The failure history data is data including information on failures that have occurred in the past and information on measures to be taken for the failures.

The search unit 103 is connected to the input unit 101 so as to acquire the failure data. The search unit 103 is connected to the history storage unit 102 so as to be able to access the failure history database. The search unit 103 is configured to be able to obtain a similar failure data table corresponding to the failure data from the failure history database by searching. The similar failure data table is a data table including a plurality of similar failure data of the corresponding failure data. The similar failure data is data including information on a failure similar to the failure indicated by the failure data and information on a treatment for the similar failure.

The determination unit 104 is connected to the search unit 103 so as to obtain a similar failure data table. The judging unit 104 is configured to be able to determine the presence or absence of recurrence for each of a plurality of similar failure data included in the similar failure data table. The occurrence of recurrence is a value indicating whether a failure similar to the failure has occurred within the recurrence determination period after the treatment for the failure. The pseudo-failure data includes the value of the presence or absence of recurrence. The recurrence determination period is a predetermined period for the determination unit 104 .

The attribute storage unit 105 is configured to be able to store an elevator attribute database. The elevator attribute database is a database storing elevator attribute data. The elevator attribute data is data including information on attributes of the elevator 2 .

The classification unit 106 is connected to the determination unit 104 so as to obtain a similar failure data table in which the occurrence of recurrence is determined for each of the similar failure data. The classification unit 106 is connected to the attribute storage unit 105 so as to be able to access the elevator attribute database. The classification unit 106 is configured to generate a combined data table from the similar failure data table and the elevator attribute database. The combined data table is a data table including a plurality of combined data. The combined data is data in which the similar failure data and the elevator attribute data of the elevator 2 in which the failure included in the similar failure data are combined are combined. The classification unit 106 is configured to classify each of the plurality of similar failure data included in the similar failure data table into a group corresponding to the attribute of the elevator 2 by the combined data table.

The calculation unit 107 is connected to the classification unit 106 so as to obtain each of the plurality of similar failure data classified into groups. The calculation unit 107 is configured to be able to calculate the recurrence rate for each treatment based on the value of the presence or absence of recurrence included in the pseudo-failure data.

The priority setting unit 108 is connected to the calculation unit 107 so as to obtain data indicating the relapse rate calculated for each treatment. The priority setting unit 108 is configured to set the priority of treatment based on the recurrence rate.

The output unit 109 is connected to the priority setting unit 108 so as to obtain data indicating the priority of the set treatment. The output unit 109 is connected to the terminal device 12 via the communication line 14 so as to transmit data indicating the treatment and the priority set for the treatment.

In the normal operation of the elevator (2), the main rope (10) is driven by the hoisting machine (9) to move. The car 7 and the counterweight 8 follow the movement of the main rope 10 and go up and down. The car 7 responds to a call from a user by going up and down in the hoistway 4 . The car 7 stops on the floor where the platform 5 is provided. The landing door 6 opens in conjunction with the car door 11 . The user of the elevator 2 gets on or off the car 7 from the platform 5 .

When a failure occurs in the elevator 2 , the maintenance person 13 inputs the failure data to the maintenance work support device 1 via the terminal device 12 . The maintenance work support apparatus 1 sets a priority to a treatment based on the recurrence rate of a failure similar to the failure indicated by failure data. The maintenance work support device 1 outputs to the terminal device 12 data indicating a treatment and a priority set for the treatment. The terminal device 12 presents the treatment to the maintenance person 13 according to the set priority.

Next, failure history data will be described with reference to FIG. 2 .

2 is a diagram showing an example of a failure history database according to the first embodiment.

The failure history data includes, for example, information identifying the elevator 2 in which the failure has occurred, a failure occurrence date and time, a failure code, and a failure condition as failure information. Information identifying the elevator 2 is, for example, a building number and a room number. The building number is a number identifying each of the one or more buildings 3 . The room number is a number identifying each of the one or more elevators 2 provided in the building 3 . The failure occurrence date is the date and time the failure occurred. The fault code is a code set for each type of fault. A fault condition is a free descriptive text representing the condition of a fault. Further, the failure history data may include recurrence data indicating that the same failure occurred in the same elevator during a predetermined period. Recurrence data is data in code or free description text representing the situation of relapse.

The failure history data includes, for example, a treatment code and treatment details as treatment information. The treatment code is a code set for each type of treatment. The treatment content is free descriptive text representing the content of the treatment.

Next, data of elevator properties will be described using FIG. 3 .

3 is a diagram showing an example of an elevator attribute database according to the first embodiment.

The elevator attribute data is information for identifying the elevator 2, and includes, for example, a building number and a room number. Elevator attribute data includes, for example, a model, a door closing method, and the number of years completed as attribute information.

Next, similar failure data will be described using FIG. 4 .

4 is a diagram showing an example of a pseudo-failure data table according to the first embodiment.

In Fig. 4, a pseudo-failure data table obtained from the failure history database in Fig. 2 is shown as an example. In this example, the failure data to which the pseudo-failure data table corresponds indicates a failure similar to the failure of the failure condition "door opening/closing failure".

The quasi-failure data table contains quasi-failure data of a failure of the failure state "door opening/closing failure" as a failure similar to the failure indicated by the corresponding failure data. The similar failure data includes information included in the failure history data and a value of whether or not there is a recurrence.

Next, the combined data will be described using FIG. 5 .

5 is a diagram showing an example of a combined data table according to the first embodiment.

In Fig. 5, the similar failure data table of Fig. 4 and the combined data table generated from the elevator attribute database of Fig. 3 are shown as examples.

The combined data is data in which the elevator attribute data obtained from the elevator attribute database using information identifying the elevator 2 as a key is combined with the similar failure data.

For example, in the similar failure data of the failure occurring in the elevator 2 identified by the building number "1234567" and the unit number "001", the model "A" and the door closing method "CO" as the attributes of the elevator 2 , the number of years of completion “10” is added. When there are a plurality of similar failure data of a failure occurring in the elevator 2, attribute information is added to each of the plurality of similar failure data. Similarly, in the similar failure data of the failure that occurred in the elevator 2 identified by the building number "2345678" and the unit number "001", the model "B", the door closing method "2S", the construction as an attribute of the elevator 2 Number of years "6" is added.

Next, the function of the maintenance work support apparatus 1 is demonstrated using FIG.

6 is a diagram showing an example of data processing in the maintenance work support apparatus according to the first embodiment.

The maintenance person 13 inputs the failure data to the input unit 101 via the terminal device 12 . In this example, the failure of the failure code "F001" and the failure condition "door opening/closing failure" is occurring in the elevator with model "A", door closing method "CO", and number of years completed "4".

The search unit 103 acquires failure data from the input unit 101 . The search unit 103 obtains a similar failure data table corresponding to the failure data from the failure history database by searching. For example, when the failure codes of a plurality of failures match, the search unit 103 searches for similar failure data assuming that the plurality of failures are similar.

The determination unit 104 acquires the similar failure data table from the search unit 103 . The determination unit 104 determines whether or not there is a recurrence for each of the plurality of similar failure data included in the acquired similar failure data table.

The determination unit 104 determines the presence or absence of recurrence, for example as follows. The determination unit 104 initializes the value of the presence/absence of recurrence by setting the value of the reoccurrence of all pseudo-failure data included in the pseudo-failure data table to "none". The determination unit 104 searches for a pair of pseudo-failure data in the pseudo-failure data table in which the interval of the failure occurrence date and time does not exceed the recurrence determination period. If the failure indicated by each of the searched pair of similar failure data is a failure occurring in the same elevator, the determination unit 104 determines the value of the occurrence of recurrence of the pair of similar failure data with the earliest failure occurrence date. There is”. Here, the determination unit 104 calculates the interval of the failure occurrence date and time as a unit by discarding units of hours or less. The recurrence determination period is k days with k being an integer. Further, when the failure history data includes recurrence data, the determination unit 104 determines the presence or absence of recurrence by searching for the presence or absence of a code or text indicating recurrence.

The classifying unit 106 acquires, from the determining unit 104, the similar failure data table in which the reoccurrence is determined. The classification unit 106 creates a combined data table from the acquired similar failure data table and the elevator attribute database. The classification unit 106 classifies each of the plurality of similar failure data included in the similar failure data table into a group corresponding to the attribute of the elevator 2 by the combined data table.

The classification unit 106 classifies each of the plurality of similar failure data as follows, for example.

The classification unit 106 has a group list. The group list is a list containing groups. The group list is an empty list in its initial state. A group has a list of usage attributes. The use attribute list is a list of attributes of the elevator 2 previously used for classification of groups having the list.

The classification unit 106 stores all of the plurality of similar failure data included in the similar failure data table as one unclassified group in the group list. The group has an empty list of usage attributes.

The classification unit 106 repeats the following processing until the group list contains no unclassified groups. The classification unit 106 extracts one unclassified group from the group list. The classification unit 106 searches for one classification attribute from among the attributes not included in the retrieved group use attribute list. The classification attribute is an attribute with a significant difference in the recurrence rate among the plurality of subgroups when the extracted group is classified into a plurality of subgroups according to the classification attribute. Here, the recurrence rate is calculated based on the value of whether or not the similar failure data included in each of the plurality of subgroups has recurrence. The classification unit 106 sets the use attribute list that each of the plurality of subgroups has as a list in which the classification attribute is added to the use attribute list of the extracted group. The classification unit 106 adds each of the plurality of subgroups to the group list as an unclassified group. On the other hand, in the case where the classification attribute is not searched, the classification unit 106 returns the retrieved group to the group list as the classification completed group.

Here, the classification unit 106 determines whether there is a significant difference in the recurrence rate between the subgroups based on the p-value, which is the significance probability of the null hypothesis that "there is no significant difference between the recurrence rates of subgroups". The classification unit 106 calculates a p-value for each of the attributes not included in the used attribute list of the extracted group. The classification unit 106 sets the attribute having the lowest p value as the classification attribute among the attributes having the p value lower than the significance level. When there is no attribute whose p-value is lower than the significance level, the classification unit 106 determines that the classification attribute is not searched for. The classification unit 106 integrates the subgroups having no significant difference in the recurrence rate among the plurality of subgroups classified according to the classification attribute.

In this example, classification by the classification unit 106 will be described more specifically. The classification unit 106 acquires a table of 500 similar failure data. The classification unit 106 creates a combined data table. The classification unit 106 stores an unclassified group including 500 pieces of combined data as an upper group in the group list. The use attribute list of the upper group is empty.

The classification unit 106 extracts the upper group from the group list. The classification unit 106 searches for a classification attribute from among the attributes not included in the use attribute list of the upper group. When the p-value for the door closing method is lower than the p-value for the significance level and other attributes, the classifying unit 106 determines the door closing method as the classification attribute. The classification unit 106 classifies the upper group into subgroups such as “CO” and “2S” according to the door closing method. When the door closing method has no statistically significant difference in the recurrence rates of subgroups other than “CO” and “2S”, the classification unit 106 sets subgroups other than “CO” and “2S” as subgroups of “Other”. unite as a group. The classification unit 106 stores a subgroup including 200 pieces of combined data whose door closing method is "CO" in the group list as an unclassified group. The classification unit 106 stores a subgroup including 200 pieces of combined data whose door closing method is "2S" in the group list as an unclassified group. The classification unit 106 stores a subgroup including 100 combined data of which the door closing method is "Other" in the group list as an unclassified group. The list of usage attributes of these groups includes a door closing method.

The classification unit 106 takes out a group whose door closing method is "CO" from the group list. The classification unit 106 determines a model as a classification attribute among attributes other than the door closing method included in the use attribute list. The classification unit 106 classifies the extracted group into subgroups of “A”, “B”, “C” and “others” according to the model. The classification unit 106 stores a subgroup including 50 combined data of which the door closing method is "CO" and the model is "A" in the group list as an unclassified group. The list of use attributes of the corresponding group includes a door closing method and a model. The classification unit 106 similarly stores the subgroups of "B", "C", and "other" by model as unclassified groups in the group list.

The classification unit 106 takes out a group whose door closing method is "2S" from the group list. The classification unit 106 determines the number of years completed as the classification attribute among attributes other than the door closing method included in the use attribute list. The classification unit 106 classifies the taken out group into subgroups of "5 years or less" and "6 years or more" according to the number of years of completion. The classification unit 106 stores, as an unclassified group, a subgroup including 120 combined data of which the door closing method is "CO" and the completion year is "5 years or less" in the group list. The list of use attributes of the corresponding group includes the door closing method and the number of years completed. The classification unit 106 similarly stores a subgroup whose completion year is "6 years or more" as an unclassified group in the group list.

The classification unit 106 takes out a group whose door closing method is "Other" from the group list. The classification unit 106 searches for a classification attribute among attributes other than the door closing method included in the use attribute list. When there is no attribute with a p-value for classification by the corresponding attribute lower than the significance level among attributes other than the door closing method included in the use attribute list, the classification unit 106 determines that the classification attribute is not searched. The classification unit 106 returns the subgroup including the combined data of 100 cases in which the door closing method is "Other" to the group list as the classified group.

The classification unit 106 repeats the hierarchical classification of groups according to the properties of the elevator 2 until the group list does not include the uncategorized list.

The calculation unit 107 acquires each of the plurality of similar failure data classified into groups from the classification unit 106 . The calculation unit 107 searches for a group corresponding to the attribute of the elevator 2 in which the failure has occurred. The calculation unit 107 calculates a recurrence rate for each treatment for the searched group. The recurrence rate is the ratio of the number of recurrences to the number of failures. The number of failures is the number of data included in the group. The number of recurrences is the number of data in which the value of the presence or absence of recurrence is "yes" among data included in the group. The calculation unit 107 calculates the recurrence rate for each treatment even when there is no significant difference in the recurrence rate.

In this example, a group whose door closing method is "CO" and whose model is "A" is searched for. The calculation unit 107 calculates the number of recurrences as 50% for 20 failures for which the treatment "check" was performed in the searched group. Similarly, the calculation unit 107 calculates the number of recurrences as 0% for 10 failures for which the treatment "substrate exchange" was performed in the searched group.

The priority setting unit 108 obtains, from the calculation unit 107, data indicating the relapse rate calculated for each treatment. The priority setting unit 108 sets the high priority in order from the treatment with the low recurrence rate. The priority setting unit 108 integrates lower-order treatments with low priority as "Other".

In this example, the priority setting unit 108 sets the highest priority to the treatment "substrate exchange". The priority setting unit 108 sets the next highest priority to the treatment "check".

The output unit 109 acquires data indicating the priority of the set treatment from the priority setting unit 108 . The output unit 109 transmits to the terminal device 12 data indicating the treatment and the priority set for the treatment.

The terminal device 12 presents the high-priority treatment to the attendant 13 by displaying the treatments in order of priority.

Next, the operation|movement of the maintenance work support apparatus 1 is demonstrated using FIG. 7-9.

7 is a flowchart showing an example of the operation of the maintenance work support apparatus according to the first embodiment. 8 is a flowchart showing an example of the operation of the determination unit according to the first embodiment. 9 is a flowchart showing an example of the operation of the classification unit according to the first embodiment.

In FIG. 7, the whole operation|movement of the maintenance work support apparatus 1 is shown.

In step S101, the input unit 101 acquires failure data. After that, the operation of the maintenance work support device 1 proceeds to step S102.

In step S102, the search unit 103 obtains a similar failure data table corresponding to the failure data by searching. Thereafter, the operation of the maintenance work support device 1 proceeds to step S103.

In step S103, the determination unit 104 determines whether or not there is a recurrence for each of the pseudo-failure data in the pseudo-failure data table. After that, the operation of the maintenance work support device 1 proceeds to step S104.

In step S104, the classification unit 106 creates a combined data table from the similar failure data table and the elevator attribute database. Thereafter, the operation of the maintenance work support device 1 proceeds to step S105.

In step S105 , the classification unit 106 classifies each of the plurality of similar failure data into groups corresponding to the attributes of the elevator 2 according to the combined data table. Thereafter, the operation of the maintenance work support device 1 proceeds to step S106.

In step S106, the calculation unit 107 calculates a recurrence rate for each treatment for a similar failure included in a group corresponding to the attribute of the elevator 2 in which the failure occurred. Thereafter, the operation of the maintenance work support device 1 proceeds to step S107.

In step S107, the priority setting unit 108 sets the priority of the treatment for the failure based on the calculated recurrence rate. Thereafter, the output unit 109 transmits data indicating the treatment and the priority set for the treatment. Then, the operation|movement of the maintenance work support apparatus 1 is complete|finished.

In FIG. 8, the operation|movement of the determination part 104 in step S103 of FIG. 7 is shown.

In step S201, the determination unit 104 acquires a pseudo-failure data table including m similar-failure data. Thereafter, the determination unit 104 initializes the value of the occurrence of recurrence of the similar failure data table. Thereafter, the operation of the determination unit 104 proceeds to step S202.

In step S202, the determination unit 104 sorts the similar failure data table in ascending order by failure occurrence date and time. Thereafter, the operation of the determination unit 104 proceeds to step S203.

In step S203, the determination unit 104 substitutes 0 into the loop variable i. Thereafter, the operation of the determination unit 104 proceeds to step S204.

In step S204, the determination unit 104 determines whether i < m. When the determination result is Yes, the operation of the determination unit 104 proceeds to step S205. When the determination result is No, the operation of the determination unit 104 ends.

In step S205, the determination unit 104 substitutes i+1 into the loop variable j. Thereafter, the operation of the determination unit 104 proceeds to step S206.

In step S206, the determination unit 104 determines whether j≤m. When the determination result is No, the operation of the determination unit 104 proceeds to step S207. When the determination result is Yes, the operation of the determination unit 104 proceeds to step S208.

In step S207, the determination unit 104 adds 1 to the loop variable i. Thereafter, the operation of the determination unit 104 proceeds to step S204.

In step S208, the determination unit 104 calculates an interval of the failure occurrence date and time for the i-th and j-th pseudo-failure data. Then, the determination unit 104 determines whether the calculated interval is k days or less, which is the recurrence determination period. When the determination result is No, the operation of the determination unit 104 proceeds to step S207. When the determination result is Yes, the operation of the determination unit 104 proceeds to step S209.

In step S209, the determination unit 104 determines whether the failure indicated by each of the i-th and j-th similar failure data is a failure occurring in the same elevator. When the determination result is Yes, the operation of the determination unit 104 proceeds to step S210. When the determination result is No, the operation of the determination unit 104 proceeds to step S211.

In step S210, the determination unit 104 sets the value of whether or not the i-th pseudo-failure data reoccurs or not. Thereafter, the operation of the determination unit 104 proceeds to step S211.

In step S211, the determination unit 104 adds 1 to the loop variable j. Thereafter, the operation of the determination unit 104 proceeds to step S206.

In FIG. 9, the operation|movement of the classification|classification part 106 in step S105 of FIG. 7 is shown.

In step S301, the classification unit 106 adds all of the plurality of similar failure data included in the similar failure data table to the group list initialized as one unclassified group. Thereafter, the operation of the classification unit 106 proceeds to step S302.

In step S302, the classification unit 106 determines whether an unclassified group is included in the group list. When the determination result is Yes, the operation of the classification unit 106 proceeds to step S303. When the determination result is No, the operation of the classification unit 106 ends.

In step S303, the classification unit 106 selects and takes out one unclassified group from the group list. Thereafter, the operation of the classification unit 106 proceeds to step S304.

In step S304, the classification unit 106 selects one attribute of the elevator 2 that has not yet been selected for the retrieved group. Thereafter, the operation of the classification unit 106 proceeds to step S305.

In step S305, the classification unit 106 determines whether the selected attribute is included in the use attribute list of the retrieved group. When the determination result is No, the operation of the classification unit 106 proceeds to step S306. When the determination result is Yes, the operation of the classification unit 106 proceeds to step S310.

In step S306, the classification unit 106 classifies the extracted group into a plurality of subgroups according to the selected attribute. Thereafter, the operation of the classification unit 106 proceeds to step S307.

In step S307, the classification unit 106 calculates the recurrence rate for each of the plurality of subgroups. Thereafter, the classification unit 106 calculates a p-value, which is a significance probability of the null hypothesis that "there is no significant difference between the recurrence rates of subgroups classified by the selected attribute". Thereafter, the operation of the classification unit 106 proceeds to step S308.

In step S308, the classification unit 106 determines whether the calculated p-value is lower than the significance level. When the determination result is Yes, the operation of the classification unit 106 proceeds to step S309. When the determination result is No, the operation of the classification unit 106 proceeds to step S310.

In step S309, the classification unit 106 stores the selected attribute and the p-value for the attribute in the retrieved temporary list for the group. Thereafter, the operation of the classification unit 106 proceeds to step S310.

In step S310, the classification unit 106 determines whether all the attributes of the elevator 2 have been selected for the retrieved group. When the determination result is No, the operation of the classification unit 106 proceeds to step S304. When the determination result is Yes, the operation of the classification unit 106 proceeds to step S311.

In step S311, the classification unit 106 determines whether or not an attribute and one or more p values for the attribute are stored in the retrieved temporary list for the group. When the determination result is No, the operation of the classification unit 106 proceeds to step S312. When the determination result is Yes, the operation of the classification unit 106 proceeds to step S313.

In step S312, the classification unit 106 determines that no classification attribute has been searched for. Thereafter, the classification unit 106 adds the extracted group to the group list as a classification completed group. Thereafter, the operation of the classification unit 106 proceeds to step S302.

In step S313, the classification unit 106 sets the attribute with the lowest p-value stored in the temporary list for the retrieved group as the classification attribute. Thereafter, the classification unit 106 adds the attribute stored in the extracted group use attribute list to the subgroup use attribute list classified by the classification attribute. Then, the classification unit 106 adds the classification attribute to the list of use attributes of the subgroup classified by the classification attribute. Thereafter, the operation of the classification unit 106 proceeds to step S314.

In step S314, the classification unit 106 integrates a plurality of subgroups classified by the classification attribute, among the plurality of subgroups having no significant difference in the recurrence rate, as one of the subgroups classified by the classification attribute. Thereafter, the operation of the classification unit 106 proceeds to step S315.

In step S315, the classification unit 106 adds each of the plurality of subgroups classified by the classification attribute to the group list as an unclassified group. Thereafter, the operation of the classification unit 106 proceeds to step S302.

As described above, the maintenance work support apparatus 1 according to the first embodiment includes the history storage unit 102 , the search unit 103 , the calculation unit 107 , and the priority setting unit 108 . be prepared The history storage unit 102 stores information on the failure and information on treatment for the failure. The search unit 103 searches the history storage unit 102 for a failure similar to the failure indicated by the input failure data. The calculation unit 107 calculates a recurrence rate for each failure treated by the failure search unit 103 . The recurrence rate indicates the rate of occurrence of similar failures within a predetermined period after the failure. The priority setting unit 108 sets the priority of treatment for the failure indicated by the failure data based on the recurrence rate calculated by the calculation unit 107 for each treatment.

The maintenance work support apparatus 1 can determine the treatment with a high possibility that a similar failure will recur after a temporary restoration by the recurrence rate calculated for each treatment. Thereby, the maintenance work support apparatus 1 can set the priority of a treatment in consideration of the accuracy of a treatment. The maintenance work support device 1 can present the maintenance worker 13 with an appropriate treatment in consideration of the accuracy of the treatment through the terminal device 12 .

Moreover, the maintenance work support apparatus 1 is equipped with the determination part 104. The determination unit 104 determines whether or not there is a recurrence of the failure searched by the search unit 103 . The occurrence of recurrence indicates whether a failure similar to the corresponding failure occurred within a predetermined period after the failure. The calculation unit 107 calculates a recurrence rate for each treatment based on the presence or absence of recurrence determined by the determination unit 104 .

The maintenance person 13 or the manager of the elevator 2 does not need to add information indicating the presence or absence of recurrence to the failure history database. Thereby, the maintenance worker 13 or the manager of the elevator 2 can use the maintenance work support apparatus 1 more easily. The history storage unit 102 does not need to store information indicating the presence or absence of recurrence. As a result, the storage capacity required for the history storage unit 102 is reduced.

Moreover, the maintenance work support apparatus 1 is provided with the classification part 106. As shown in FIG. The classification unit 106 classifies the failures searched for by the search unit 103 into groups corresponding to the attributes of the elevator 2 in which the failure occurs. The calculation unit 107 calculates a recurrence rate for each treatment for a failure classified into a group corresponding to the attribute of the elevator 2 in which the failure indicated by the failure data has occurred.

The calculator 107 calculates the recurrence rate in consideration of the properties of the elevator 2 in which the failure has occurred. Thereby, the priority setting unit 108 can set the priority of treatment with higher precision.

Further, when there is a significant difference in the recurrence rate between failures occurring in the elevators 2 having different attributes, the classification unit 106 classifies them into groups according to the attributes.

The classification unit 106 and the calculation unit 107 calculate the recurrence rate by classifying groups having a significant difference in the recurrence rate. Thereby, the priority setting unit 108 can set the priority of treatment with higher precision.

In addition, the failure data may be input from the monitoring center outside the building 3 instead of the maintenance person 13 . The failure data may be signal values of one or more sensors provided in the elevator 2 . Failure data may be entered automatically when signal values of one or more sensors are within a range of values indicative of failure.

When the failure conditions of a plurality of failures are similar, the search unit 103 may search for similar failure data assuming that the plurality of failures are similar. At this time, the search unit 103 calculates the degree of similarity between failure situations, for example, by natural language processing.

The search unit 103 may search for a similar failure based on a preset similarity determination table. The similarity determination table is a table indicating whether or not one set of failures indicated by one set of failure codes are similar.

The search unit 103 may search for similar failures according to the code system of the failure codes. At this time, the code system of the fault code is set so that, for example, faults with close fault codes are similar to each other.

When the failure data are signal values of one or more sensors, the search unit 103 may search for similar failures based on differences in signal values. The search unit 103 calculates the difference between the signal values by, for example, the Euclidean distance.

The classification unit 106 may search for a numerical value in which a significant difference in the recurrence rate occurs when classifying into subgroups according to an attribute represented by a numerical value. For example, the classification unit 106 classifies the subgroups into "n or less" or "n+1 or more" subgroups while changing the numerical value n of the attribute, and searches for a numerical value n at which a significant difference occurs in the recurrence rate of the subgroups. The attribute represented by a numerical value is the number of years of completion, for example.

In the case where a group including a number of combined data less than a predetermined threshold is obtained, the classification unit 106 need not perform classification. For example, when the number of combined data included in the unclassified group extracted from the group list is less than the threshold, the classification unit 106 may return the extracted group to the group list as it is as a classified group. If a subgroup including the number of cases of combined data less than the threshold is obtained by classifying according to the selected attribute, the classification unit 106 does not need to classify the subgroup according to the attribute. Here, the threshold is predetermined as the lower limit of the number of combined data for which a reliable recurrence rate is calculated for the classified group. Thereby, when, for example, a group of 100 cases is divided into 95 cases and 5 cases by classification, the case where the reliability of the probability of recurrence cannot be secured because the number of divided cases is as small as 5 cases is prevented.

The terminal device 12 may present the high-priority treatment to the attendant 13 by displaying the treatment with a different size or color according to the priority. The terminal device 12 may display the recurrence rate along with the treatment. The terminal device 12 may present the high-priority treatment to the attendant 13 by not displaying the low-priority treatment.

After presenting the treatment to the maintenance worker 13 , the terminal device 12 may accept input of treatment data by the maintenance worker. At this time, the terminal device 12 may add the failure data and the treatment data to the failure history database stored in the history storage unit 102 as failure history data.

Next, an example of the hardware configuration of the maintenance work support device 1 will be described with reference to FIG. 10 .

Fig. 10 is a diagram showing a hardware configuration of a main part of the maintenance work support apparatus according to the first embodiment.

Each function of the maintenance work support device 1 can be realized by a processing circuit. The processing circuit comprises at least one processor 1b and at least one memory 1c. The processing circuit may include at least one dedicated hardware 1a together with or as a substitute for the processor 1b and the memory 1c.

When the processing circuit includes the processor 1b and the memory 1c, each function of the maintenance operation support apparatus 1 is realized by software, firmware, or a combination of software and firmware. At least one of software and firmware is described as a program. The program is stored in the memory 1c. The processor 1b reads and executes the program stored in the memory 1c, thereby realizing each function of the maintenance work support device 1 .

The processor 1b is also called a CPU (Central Processing Unit), a processing unit, an arithmetic unit, a microprocessor, a microcomputer, and a DSP. The memory 1c is constituted of, for example, a nonvolatile or volatile semiconductor memory such as RAM, ROM, flash memory, EPROM, and EEPROM, magnetic disk, flexible disk, optical disk, compact disk, mini disk, DVD, or the like. do.

When the processing circuit is provided with dedicated hardware 1a, the processing circuit is realized as, for example, a single circuit, a complex circuit, a programmed processor, a parallel programmed processor, an ASIC, an FPGA, or a combination thereof.

Each function of the maintenance work support apparatus 1 can be implemented by a processing circuit, respectively. Alternatively, each function of the maintenance work support device 1 can be integrated and realized by a processing circuit. For each function of the maintenance work support device 1, a part may be realized by the dedicated hardware 1a, and the other part may be realized by software or firmware. In this way, the processing circuit realizes each function of the maintenance operation support apparatus 1 by hardware 1a, software, firmware, or a combination thereof.

Embodiment 2.

In Embodiment 2, the difference from the example disclosed in Embodiment 1 is demonstrated in detail. For features not described in the second embodiment, any features of the examples disclosed in the first embodiment may be employed.

The structure of the maintenance work support apparatus 1 which concerns on Embodiment 2 is demonstrated.

It is a block diagram of the maintenance work support apparatus which concerns on Embodiment 2. FIG.

The maintenance work support device 1 includes a generation unit 110 and a recurrence rate storage unit 111 .

The generation unit 110 is configured to be able to generate fault data comprehensively for fault codes and fault conditions, for example. The generation unit 110 is connected to the search unit 103 so as to provide the generated failure data.

The relapse rate storage unit 111 is configured to store a relapse rate database. The relapse rate database is a database that stores relapse rate data. The recurrence rate data is data including information on the recurrence rate calculated by the calculation unit 107 for each treatment.

The search unit 103 is connected to the generation unit 110 so as to obtain the failure data.

The calculation unit 107 is connected to the recurrence rate storage unit 111 so as to be able to access the recurrence rate database.

The priority setting unit 108 is connected to the input unit 101 so that failure data can be acquired. The priority setting unit 108 is connected to the recurrence rate storage unit 111 so as to obtain the recurrence rate data. The priority setting unit 108 is configured to set the priority of treatment based on the recurrence rate.

The output unit 109 is connected to the priority setting unit 108 so as to obtain data indicating the priority of the set treatment. The output unit 109 is connected to the terminal device 12 via the communication line 14 so as to transmit data indicating the treatment and the priority set for the treatment.

Next, the recurrence rate data according to the second embodiment will be described.

12 is a diagram showing an example of a recurrence rate database according to the second embodiment.

The recurrence rate data includes, for example, a failure code and a failure condition as failure information. The relapse rate data includes a first attribute, a first item, a second attribute, and a second item as information on the attribute used for classification. The relapse rate data includes a treatment code and treatment details as treatment information. The recurrence rate data includes the number of failures, the number of recurrences, and the recurrence rate as information on the recurrence rate.

The first attribute is an attribute for classifying the entire similar failure data table into a group. The first item is a value of the first attribute. The second attribute is an attribute for further classifying the group classified by the first attribute into a lower group. The second item is a value of the second attribute. When not classified into a lower group than the group classified by the first attribute, the data of the second attribute and the second item are empty. The relapse rate data includes information on an attribute for further classifying the group classified by the second attribute into a lower group.

The number of failures is the number of similar failure data retrieved for each treatment. The number of recurrences is the number of similar-failure data in which the occurrence of recurrence is determined to be "yes" among the searched similar-failure data. The recurrence rate is the ratio of the number of recurrences to the number of failures.

Relapse rate data is generated, for example, as follows. The generation unit 110 transmits the generated failure data to the search unit 103, for example, when the calculation load of the maintenance work support device 1 is low. The search unit 103 obtains a similar failure data table corresponding to the failure data from the failure history database by searching. The determination unit 104 determines whether or not there is a recurrence for each of a plurality of similar failure data included in the similar failure data table. The classification unit 106 creates a combined data table from the similar failure data table and the elevator attribute database. The classification unit 106 classifies each of the plurality of similar failure data included in the similar failure data table into a group corresponding to the attribute of the elevator 2 by the combined data table. The calculation unit 107 calculates the recurrence rate for each treatment based on the value of the presence or absence of recurrence included in the pseudo-failure data. The calculation unit 107 generates, as recurrence rate data, failure information, attribute information used for classification, treatment information, and recurrence rate information.

Relapse rate data are used, for example, as follows. The input unit 101 receives, for example, the input of the failure data from the terminal device 12 by the maintenance person 13 . The priority setting unit 108 acquires failure data from the input unit 101 . The priority setting unit 108 acquires recurrence rate data for each treatment with respect to a failure indicated by the acquired failure data, for a group of attributes corresponding to the elevator 2 in which the failure occurred. The priority setting unit 108 sets the priority of the treatment for the failure indicated by the failure data based on the recurrence rate data.

As described above, the maintenance work support apparatus 1 according to the second embodiment includes the recurrence rate storage unit 111 . The recurrence rate storage unit 111 stores the recurrence rate calculated by the calculation unit 107 for each treatment. The priority setting unit 108 sets the priority of the treatment for the failure indicated by the failure data based on the recurrence rate stored in the recurrence rate storage unit 111 .

The maintenance work support apparatus 1 can perform a process, such as determination of the presence or absence of recurrence, or a classification, before input of failure data. For this reason, the calculation load of the maintenance work support apparatus 1 from failure data input until prioritization is reduced is reduced. Thereby, the maintenance work support apparatus 1 can set the priority to a treatment quickly after failure data is input.

In addition, the generation unit 110 may transmit the failure data to the search unit 103 at predetermined time intervals. That is, the recurrence rate database is updated at predetermined time intervals. Alternatively, the recurrence rate database may be updated when data is added to the history storage unit 102 .

Embodiment 3.

In the third embodiment, differences from the examples disclosed in the first or second embodiment will be described in detail. For the features not described in the third embodiment, any features of the examples disclosed in the first or second embodiment may be employed.

13 is a diagram showing an example of the recurrence rate calculated by the calculator according to the third embodiment.

In FIG. 13 , the horizontal axis represents the number of days elapsed after the treatment for the failure. The vertical axis represents the recurrence rate for the corresponding failure after treatment. Treatment α and treatment β indicate different treatments for failures classified into the same group. In this example, the scheduled maintenance date for the elevator 2 in which the failure occurred is 20 days after the input of failure data indicating the failure. The relapse rate after 20 days was higher for treatment α than treatment β. The relapse rate after 50 days was higher for treatment β than treatment α.

The determination unit 104 determines the interval until the failure occurs again as the value of whether or not there is a recurrence. The determination unit 104 initializes the value of the presence or absence of recurrence by setting the value of the reoccurrence of all pseudo-failure data included in the pseudo-failure data table to an integer value 0 indicating "none". When the interval of the failure occurrence date and time of the pair of similar failure data is d days of k days or less, the determination unit 104 determines the value of the reoccurrence as an integer value d.

The calculation unit 107 calculates the recurrence rate each time one day has elapsed from the failure. The recurrence rate every 1 day after the failure is an example of the recurrence rate for each elapsed time from the failure. The calculation unit 107 calculates the ratio of the number of failures in which a similar failure occurred until d days after the treatment to the number of failures in which the treatment was performed for each treatment group classified by the classification unit 106, after d days. It is calculated as the recurrence rate.

The priority setting unit 108 sets a high priority to a treatment with a low recurrence rate on the next maintenance day. That is, the priority setting unit 108 sets the priority of treatment β with a low recurrence rate after 20 days to be higher than treatment α.

As described above, the calculation unit 107 of the maintenance work support apparatus 1 according to the third embodiment calculates the recurrence rate for each elapsed time from the failure.

If a failure does not recur by the maintenance day, the maintenance person 13 takes a fundamental action on the repair day, and the recurrence of the said failure is suppressed. The priority setting unit 108 can set a high priority even for an emergency treatment in which the recurrence rate increases after a long period of time has elapsed, if the recurrence rate until the repair date is low. For this reason, the priority setting unit 108 can set the priority suitable for the treatment for the failure according to the interval until the scheduled maintenance date for the elevator 2 in which the failure has occurred.

In addition, the calculation unit 107 may calculate the recurrence rate every three days from the failure, for example, as the recurrence rate for each elapsed time from the failure.

The priority setting unit 108 may set the priority of the treatment remotely possible without the maintenance worker 13 to be high when the recurrence rate on the next maintenance day is lower than a predetermined ratio. The remotely possible treatment is, for example, a treatment for transmitting a maintenance control signal to the elevator 2 .

The terminal device 12 may present the change in the recurrence rate for each treatment with respect to the number of elapsed days to the maintenance worker 13 by, for example, displaying with a graph.

Industrial Applicability

The maintenance work support apparatus which concerns on this invention is applicable to an elevator.

1: maintenance work support device 101: input unit
102: history storage unit 103: search unit
104: judgment unit 105: attribute storage unit
106: classification unit 107: calculation unit
108: priority setting unit 109: output unit
110: generation unit 111: recurrence rate memory unit
1a: Hardware 1b: Processor
1c: memory 2: elevator
3: building 4: hoistway
5: Platform 6: Platform door
7: car 8: counterweight
9: hoisting machine 10: main rope
11: car door 12: terminal device
13: repairman 14: communication line

Claims (6)

a history storage unit for storing information on the failure and information on measures for the failure;
a search unit that searches the history storage unit for a failure similar to the failure indicated by the input data;
a calculation unit for calculating a recurrence rate indicating a rate of occurrence of a failure similar to the failure within a predetermined period after the failure with respect to the failure searched by the search unit for each treatment for the failure;
and a priority setting unit configured to set a priority of treatment for a failure indicated by the data based on the recurrence rate calculated by the calculation unit for each treatment. The method according to claim 1,
a determination unit for judging whether or not a failure similar to the failure has occurred within a predetermined period after the failure with respect to the failure searched by the search unit;
The apparatus for supporting maintenance work of an elevator, wherein the calculation unit calculates the recurrence rate for each treatment based on the occurrence of recurrence determined by the determination unit. The method according to claim 1 or 2,
A classification unit for classifying the failure searched by the search unit into a group corresponding to the property of the elevator in which the failure has occurred,
The calculator is configured to calculate the recurrence rate for each treatment for a failure classified into a group corresponding to an attribute of the elevator in which the failure indicated by the data has occurred. 4. The method according to claim 3,
The classification unit, when there is a significant difference in the recurrence rate between failures occurring in elevators having different attributes, the elevator maintenance work support device for classifying the group according to the attribute. The method according to claim 1 or 2,
a recurrence rate storage unit for storing the recurrence rate calculated by the calculation unit for each treatment;
The elevator maintenance work support device, wherein the priority setting unit sets a priority of treatment for a failure indicated by the data based on the recurrence rate stored in the recurrence rate storage unit. The method according to claim 1 or 2,
The calculation unit is an elevator maintenance work support device for calculating the recurrence rate for each elapsed time since the failure.

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