JP7362573B2 - Elevator failure recovery method and elevator failure recovery support system - Google Patents

Description

The present invention relates to an elevator failure recovery method and an elevator failure recovery support system for recovering an elevator when it fails.

Lifts and escalators such as elevators and escalators are composed of multiple devices, and failures can occur for a wide variety of reasons. Therefore, advanced knowledge and skills are required to restore elevators from failure. Therefore, inexperienced maintenance personnel may not be able to adequately handle the problem, and it may take a long time to determine the cause of the failure. Furthermore, if fault recovery measures are taken incorrectly, the true cause of the fault may remain and there is a risk that the same fault will occur again.

In order to solve this problem, techniques described in Patent Documents 1 and 2, for example, have been proposed.

Patent Document 1 discloses that when a maintenance worker inputs a failure state from a mobile terminal, past causes of similar failures are extracted from information stored in a database, and information regarding the extracted failure causes and countermeasures is maintained in order of probability of occurrence. A technology has been disclosed that reduces the time required for failure recovery by transmitting information to employees' mobile terminals.

Additionally, Patent Document 2 discloses a technology that sets priorities based on the recurrence rate of similar failures, presents countermeasures to maintenance personnel according to the set priorities, and prevents recurrence of the same failure. Disclosed.

Japanese Patent Application Publication No. 2018-100176 International patent publication WO2019/229946

However, in Patent Documents 1 and 2, since the investigation proceeds from the cause with a high probability of failure occurrence, for example, if a failure of an elevator occurs due to a cause with a low probability of failure occurrence, it takes time to identify the cause of the failure, There was a problem that the elevator stopped for a long time . In addition , if maintenance work such as adjustment and lubrication is performed on the equipment that caused the failure before the failure occurs, the condition will improve and the failure will be temporarily resolved, making it possible to overlook any signs of a recurring failure. It may become unstable until you get used to the adjusted state. In such a case, it is difficult to identify the cause of the failure simply based on the number of past failures.

An object of the present invention is to provide an elevator failure recovery method and an elevator failure recovery support system that solve the above-mentioned problems, speed up the identification of the cause of elevator failure, and suppress long-term suspension of the elevator and recurrence of the failure.

In order to achieve the above object, the present invention extracts a similar past failure history group from stored failure history information when a failure occurs in an elevator, classifies the extracted failure history group by failure cause equipment, and In an elevator failure recovery method in which a failure probability is calculated for each failure-causing device and the failure-causing device is investigated , a long-term weighting coefficient is calculated in chronological order based on the failure history information, and the stored elevator Calculating repeated weighting coefficients in a time-series manner based on maintenance history information, extracting long-term weighting coefficients and repeated weighting coefficients on the day of failure from the long-term weighting coefficients and the repeated weighting coefficients calculated in a time-series manner, The method is characterized in that the failure probability is corrected using a long-term weighting coefficient and a repeated weighting coefficient of the failure occurrence date.

The present invention also provides a failure history database for storing failure history information of elevators, a similar failure search means for extracting a similar past failure history group from the failure history database when a failure occurs in the elevator, and the similar failure search In an elevator failure recovery system, the elevator failure recovery system includes a failure cause device candidate extracting means for classifying the failure history group by failure cause device and calculating a failure probability from the failure history group extracted by the means, a maintenance history database for storing maintenance history information of the elevator; , calculating long-term weighting coefficients in time series based on the failure history information stored in the failure history database, and repeatedly calculating weighting coefficients in time series based on the maintenance history information stored in the maintenance history database. a weighting coefficient that extracts a long-term weighting coefficient and a repeated weighting coefficient on the day of failure from the long-term weighting coefficient and the repeated weighting coefficient calculated in a time series by the weighting coefficient generating means; The present invention is characterized by comprising: a calculating means; and a fault-causing device probability correction processing means for correcting the failure probability using the long-term weighting coefficient and the repeated weighting coefficient calculated by the weighting coefficient calculating means.

Advantageous Effects of Invention According to the present invention, it is possible to provide an elevator failure recovery method and an elevator failure recovery support system that quickly identify the cause of an elevator failure and prevent the elevator from stopping for a long time and from recurrence of the failure.

1 is a system configuration diagram of an elevator failure recovery support system according to an embodiment of the present invention. 3 is a flowchart showing the operation of the elevator failure recovery support system according to the embodiment of the present invention. It is a figure which shows the fluctuation|variation of the weighting coefficient of the chain which is an example of the adjustment part based on the Example of this invention. FIG. 3 is a diagram showing variations in weighting coefficients of the safety switch according to the embodiment of the present invention. FIG. 6 is a diagram showing the variation of the weighting coefficient with respect to the failure probability of the rotary encoder provided on the shaft of the hoisting machine according to the embodiment of the present invention. FIG. 7 is a diagram showing failure probabilities and investigation order before and after correction of a failure-causing device according to an embodiment of the present invention. FIG. 3 is a diagram showing the effect of improving the efficiency of failure response of the present invention over the conventional technology.

Embodiments of the present invention will be described below with reference to the accompanying drawings. Similar components are given the same reference numerals, and similar descriptions will not be repeated.

The various components of the present invention do not necessarily have to exist independently, and one component may be made up of multiple members, multiple components may be made of one member, or a certain component may be different from each other. It is allowed that a part of a certain component overlaps with a part of another component, etc.

FIG. 1 is a system configuration diagram of an elevator failure recovery support system according to an embodiment of the present invention. The elevator failure recovery support system is comprised of an elevator 1, a monitoring center 2 installed at a location away from the elevator 1, and a mobile terminal 5 carried by a maintenance worker 4.

Elevators 1 such as elevators and escalators are always connected to an elevator control panel 11 that controls the operation of the elevator 1 and an error code output terminal of the elevator control panel 11, so that at least when a failure occurs in the elevator 1, the elevator identification data and the details of the failure are transmitted. An elevator communication terminal 12 is provided which transmits an abnormality signal including an error code indicating the error code.

The monitoring center 2 is connected to the elevator communication terminal 12 installed in the elevator 1 through a dedicated line, the Internet, other wired transmission lines, or wireless transmission lines, and receives elevator identification data and error codes sent from the elevator communication terminal 12. A monitoring center communication means 21 is provided for receiving abnormality reporting signals including: The monitoring center communication means 21 is composed of a receiving section 21a and a transmitting section 21b.

The failure history DB 22 (hereinafter referred to as DB) contains at least identification data such as the elevator model and serial number, error codes, failure conditions, failure causes, recovery work details, cause investigation time, etc. for failures that have occurred in the past. Failure history information such as countermeasure time data is stored.

The elevator information DB 23 is a database in which information such as identification data, model, equipment specifications, etc. of all elevators to be maintained is accumulated. Specification information can be extracted.

When the receiving unit 21a of the monitoring center communication means 21 receives an abnormality notification of the elevator 1 (when a failure occurs), the similar failure history search means 24 performs a failure search using software using, for example, a CPU. At the same time, it has a function of extracting a similar past failure history group from the failure history DB 22 based on identification data such as the model of the elevator 1 and an error code. For example, when vibration occurs in an elevator or an abnormal noise occurs in an elevator, the cause of failure is not limited to one, but there are multiple causes of failure. Further, even if the failure is similar, the device that is the source of the failure is not limited to a specific device. The similar failure history search means 24 extracts, for a given failure, a plurality of failure causes and a plurality of devices considered to be the source of the failure cause as a similar failure group.

The failure cause device candidate extraction means 25 has a function of classifying and totaling the similar failure history group extracted by the similar failure history search means 24 by failure cause device and calculating a failure probability. For example, when vibration occurs in an elevator or when an abnormal noise occurs in an elevator, the cause of failure is not limited to one, but there are various causes of failure. In addition, the sources of failures can be found in various devices. The failure-causing device candidate extracting means 25 classifies and totals the sources of failure and devices by causative device, and calculates the probability.

The maintenance history DB 26 is a database that accumulates and stores maintenance history information such as elevator inspection equipment, equipment replacement history, and equipment maintenance history. Ru. Inspection equipment, equipment replacement history, and equipment maintenance history are accumulated for each elevator.

If the failed machine has a record of maintenance and parts replacement before the failure, the weighting coefficient generation means 27 generates a weight that becomes a probability correction value for correcting the probability for each failed device derived by the failure cause device candidate extraction means 25. Equipped with a function to calculate coefficients over time. The weighting coefficient is calculated based on the failure history information stored in the failure history DB 22 and the maintenance history information stored in the maintenance history DB 26.

The weighting coefficient calculating means (the relevant machine) 28 compares the weighting coefficient calculated by the weighting coefficient generating means 27 with the failure occurrence date obtained from the receiving unit 21a of the monitoring center communication means 21, and extracts the weighting coefficient on the failure occurrence date. Equipped with functions.

The failure-causing device probability correction processing means 29 has a function of correcting the probability by multiplying the classification and aggregation of the similar failures extracted by the failure-causing device candidate extraction means 25 by the weighting coefficient calculation means (the relevant machine) 28.

The investigation work position order optimization means 30 has a function of optimizing the investigation order in descending order of probability based on the failure cause device probability correction value calculated by the failure cause device probability correction processing means 29.

The information regarding the investigation order outputted by the investigation work position order optimization means 30 is transmitted from the transmitter 21b of the monitoring center communication means 21, and is received by the mobile terminal communication means of the mobile terminal 5 carried by the maintenance worker 4. The received investigation order is displayed on the display screen of the mobile terminal 5, and the maintenance worker 4 confirms the investigation order on the display screen. The elevator failure recovery support system is configured as described above.

Next, the operation of the elevator failure recovery support system will be explained using FIG. 2. FIG. 2 is a flowchart showing the operation of the elevator failure recovery support system according to the embodiment of the present invention.

When a failure occurs in the elevator 1, the elevator control panel 11 issues failure state (abnormality) information including identification data for identifying the elevator 1 and an error code via the elevator communication terminal 12 (step S1). Next, the monitoring center 2 receives, at the transmitting unit 21b of the monitoring center communication means 21, the failure state information including the identification data of the elevator 1 and the error code, which are notified via the elevator communication terminal 12 (step S2). . Furthermore, even if there is no abnormality notification from the elevator control panel 11, if a customer such as a resident or manager 3 calls due to a failure of the elevator 1 (step S1'), the monitoring center 2 performs monitoring (not shown). Based on the information obtained from the resident/manager 3, the center staff inputs the failure status such as elevator identification data and malfunction information. Alternatively, the maintenance person 4 goes to the site, checks the status of the elevator 1, and inputs the failure status into the mobile terminal 5 (step S2').

Next, the monitoring center 2 searches the elevator information DB 23 for specification information such as the model of the elevator in question based on the elevator identification data included in the above-mentioned failure state information, and uses the extracted information to search for similar failure history. 24 (step S3).

The similar failure history search means 24 determines whether there is a failure similar to the corresponding failure in the failure history DB 22 using the model of the elevator 1 and failure states such as error codes and malfunction events as search conditions (step S4). .

Next, if there is a past failure history similar to the corresponding failure in the failure history DB 22, the similar failure history search means 24 extracts a similar failure group and classifies the extracted failure history by failure cause device. The information is totaled and transmitted to the failure-causing device candidate extraction means 25 (step S5).

Here, as a result of the determination process in step S4, if there is no failure history similar to the failure in question, it is determined that there is no point in utilizing past similar cases for failure cause investigation, and the support center (not shown) is notified. A specialist engineer (not shown) and the local maintenance worker 4 interact to investigate the cause of the failure (step S5').

Next, the failure-causing device candidate extracting means 25 determines whether there is a plurality of failure-causing devices as a result of classifying the failure-causing devices in the extracted similar failure history group (step S6).

Next, if there are multiple failure-causing devices, the failure-causing device candidate extraction means 25 uses the extracted similar failure group as a parameter and calculates the proportion of each classified failure-causing device as the failure probability for each failure-causing device. (Step S7). Note that if there is only one failure-causing device, that failure-causing device is designated as an investigation device (step S12).

Next, the weighting coefficient generation means 27 calculates a weighting coefficient in chronological order based on the similar failure history information extracted from the failure history DB 22 and the maintenance history information of the similar failure elevators extracted from the maintenance history DB 26. (Step S8). The weighting coefficients of this embodiment are a long-term weighting coefficient calculated based on failure history information and a repetition weighting coefficient calculated based on maintenance history information.

The long-time weighting coefficient calculation method uses the number of past long-term non-operational failures in which failure cause investigation took three hours (predetermined time) or more to create an occurrence probability curve in time series. Further, regarding the occurrence probability curve, time-series variations are corrected using a maintenance cycle based on maintenance history information. The repetition weighting coefficient performs the same process as the long-term weighting coefficient using the number of failures with a failure interval of half a year or less.

Next, the weighting coefficient calculation means (the relevant machine) 28 calculates the long-term weighting coefficient and the repetition weighting coefficient at the time of the failure occurrence date from the results generated by the weighting coefficient generation means 27 (step S9).

Next, the failure-causing device probability correction processing means 29 repeatedly weights the failure probabilities for each failure-causing device calculated by the failure-causing device candidate extraction means 25 with the long-term weighting coefficient calculated by the weighting coefficient calculation means (the relevant machine) 28. The coefficients are integrated to correct the failure probability (step S10).

Next, the investigation work position order optimization means 30 calculates the result of integrating the failure probabilities for each failure-causing device (step S7) with a failure probability weighting coefficient (step S9) based on the failure history and maintenance history data. Based on this, the order in which the causes are to be investigated is determined in descending order of probability (step S11).

Next, an instruction for the order of fault investigation is transmitted from the transmitter 21b of the monitoring center communication means 21 to the mobile terminal 5 carried by the maintenance worker 4 (step S12), and the maintenance worker 4 investigates the cause and takes countermeasures according to the investigation order. When the failure is recovered (step S14), the maintenance engineer 4 inputs the failure state, cause of failure, and countermeasure details from the mobile terminal 5 and registers them in the failure history DB 22 (step S15).

Next, the calculation results of the weighting coefficients will be explained using FIGS. 3 to 5. FIG. 3 is a diagram showing variations in weighting coefficients of a chain, which is an example of an adjustment part according to an embodiment of the present invention. In FIG. 3, a long-term weighting factor 40 and a repeating weighting factor 41 are shown.

A movable device such as a chain does not fit well with other parts at the initial stage of operation, so the weighting coefficient becomes high, but as time passes, the parts get used to each other and the weighting coefficient decreases. Chains stretch over time, so tension adjustment is necessary as a regular maintenance task. Equipment that is regularly adjusted in this manner is in good condition immediately after adjustment, and therefore is less likely to be out of operation for a long period of time, and the long-term weighting coefficient 40 becomes small (position A in the figure). On the other hand, as the next adjustment period approaches, the condition of the equipment deteriorates, increasing the possibility that it will be out of operation for a long period of time, and the long-term weighting coefficient 40 increases (position B in the figure). Further, since the elongation speed of the repetition weighting coefficient 41 becomes stable when the initial elongation region is excluded, the repetition weighting coefficient 41 tends to gradually decrease over time.

FIG. 4 is a diagram showing variations in the weighting coefficients of the safety switch according to the embodiment of the present invention. Although the operation of the safety switch can be checked by inspection, it is difficult to check the contact failure of the contacts, so the repetition weighting coefficient 41 is set constant. Note that failures in switch contacts are classified as minor failures because recovery measures are simple, and a general lifespan curve should be applied to the long-term weighting coefficient.

FIG. 5 is a diagram showing the variation of the weighting coefficient with respect to the failure probability of the rotary encoder provided on the shaft of the hoisting machine according to the embodiment of the present invention. Hoisting machines require regular lubrication to maintain the lubricity of their shafts. Here, excess grease applied to the shaft may adhere to the rotary encoder and cause an abnormality in the rotary encoder. Immediately after greasing the shaft, excess grease tends to drip, so the repeated weighting coefficient 41 increases for a while after greasing, but as the amount of excess grease decreases, the possibility of grease adhering to the rotary encoder decreases, so the repeated weighting coefficient 41 increases. Coefficient 41 decreases. Since the rotary encoder is not in operation for a long period of time due to a malfunction of the rotary encoder itself, it is preferable to apply a general life curve to the long-term weighting coefficient 40.

FIG. 6 is a diagram showing the failure probabilities and investigation order of the failure-causing device before and after correction according to the embodiment of the present invention. Figure 6 shows an example of an escalator.

In FIG. 6, a similar failure history search means 24 extracts a failure-causing device from the failure history DB 22, and a failure-causing device candidate extraction means 25 calculates a failure probability and an investigation ranking. In Figure 6, "steps", "chains", "handrails", and "driving machines" are extracted as failure-causing devices, and the respective probabilities are 35% for "steps," 25% for "chains," and 20% for "handrails." %, and ``Driving Machine'' is calculated as 10%. The survey ranking is in descending order of probability. This order of investigation is the confidence level before correction. The present embodiment is characterized in that, as described above, the probability calculated from the past failure history is corrected by multiplying it by a long-term weighting coefficient and a repetition weighting coefficient. "Step", which has the first place in the survey ranking before correction, has a long-term weighting coefficient of 1.0 and a repetition weighting coefficient of 0.6, and when these coefficients are multiplied to correct the probability, it is 0.35 * 1.0 * 0.6 = It becomes 0.21. In other words, the probability after correction is 21%. Similarly, "Chain", which ranks second in the survey ranking before correction, has a long-term weighting coefficient of 2.4 and a repetition weighting coefficient of 0.6, and when these coefficients are multiplied to correct the probability, it is 0.25 * 2.0 *0.6=0.30. In other words, the probability after correction is 30%.

When the probability calculated from past failure history is corrected by multiplying it by the long-term weighting coefficient and the repetition weighting coefficient, the corrected probability is 21% for "step", 30% for "chain", and 30% for "handrail". 28%, "driving machine" 4%, and the investigation order is changed to "chain", "handrail", "step", and "driving machine".

Regarding the probability of each extracted causative device, the weighting coefficient calculated by the weighting coefficient calculation means (the relevant machine) 28 is integrated by the failure-causing device probability correction processing means 29, and the investigation order is assigned in descending order of the probability after the integration, This result is transmitted to the mobile terminal 5 of the maintenance worker 4 who conducts cause investigation and countermeasure work. In this embodiment, the probability correction is calculated by integrating, but instead of adding, functions may be created for each device importance.

FIG. 7 is a diagram showing the effect of the present invention on improving the efficiency of failure handling over the conventional technology. For example, when vibration occurs on an escalator, conventionally, only the step with the highest failure probability for each failure-causing device is adjusted to recover from the failure condition. However, after a certain amount of time has elapsed, a failure may occur in which the chain breaks, which takes time to restore. This could have been prevented if the chain had been checked in addition to the steps when responding to the failure.

In contrast, in the present invention, the weighting coefficients calculated by the weighting coefficient calculating means (the relevant machine) 28 are integrated with respect to the probabilities of each causative device by the failure-causing device probability correction processing means 29, and the probabilities are sorted in descending order of the probabilities after the integration. Since the inspection order is allocated, the inspection order is optimized, and by inspecting not only steps but also chains, it is possible to prevent oversight of inspection targets, and as a result, to prevent long periods of non-operation and repeated failures. can do.

Note that the present invention is not limited to the embodiments described above, and includes various modifications. The embodiments described above are described in detail to explain the present invention in an easy-to-understand manner, and the present invention is not necessarily limited to those having all the configurations described.

1...Elevator, 2...Monitoring center, 3...Resident/manager, 4...Maintenance worker, 5...Mobile terminal, 11...Elevator control panel, 12...Elevator communication terminal, 21...Monitoring center communication means, 22...Failure history DB , 23...Elevator information DB, 24...Similar failure history search means, 25...Failure cause device candidate extraction means, 26...Maintenance history DB, 27...Weighting coefficient generation means, 28...Weighting coefficient calculation means (the relevant machine), 29... Failure cause equipment probability correction processing means, 30... Investigation work position order optimization means, 40... Long time weighting coefficient, 41... Repetition weighting coefficient

Claims (8)

When a failure occurs in an elevator, a similar past failure history group is extracted from the saved failure history information, the extracted failure history group is classified by failure-causing device, and the failure probability is calculated for each failure-causing device to detect a failure. In an elevator failure recovery method that involves investigating the device that caused the problem,
calculating a long-term weighting coefficient chronologically based on the failure history information; repeatedly calculating a weighting coefficient chronologically based on the saved maintenance history information of the elevator;
A long-term weighting coefficient and a repeated weighting coefficient on the failure occurrence date are extracted from the long-term weighting coefficient and the repetition weighting coefficient calculated in a time-series manner, and the long-term weighting coefficient and the repetition weighting coefficient on the failure occurrence date are used to calculate the An elevator failure recovery method characterized by correcting failure probability. In claim 1,
The elevator failure recovery method is characterized in that the maintenance history information includes inspection equipment, equipment replacement history, and equipment maintenance history of the elevator. In claim 1,
An elevator failure recovery method characterized by determining an investigation order of equipment to be investigated based on a corrected failure probability. 4. The elevator failure recovery method according to claim 3 , wherein the determined investigation order is transmitted to a mobile terminal. a failure history database for storing failure history information of elevators; a similar failure search means for extracting a similar past failure history group from the failure history database when a failure occurs in the elevator; In an elevator failure recovery system, the elevator failure recovery system includes a failure cause device candidate extraction means for classifying each failure cause device from a failure history group and calculating a failure probability.
a maintenance history database that stores maintenance history information of the elevator;
Calculating long-term weighting coefficients in time series based on the failure history information stored in the failure history database, and repeatedly calculating weighting coefficients in time series based on the maintenance history information stored in the maintenance history database. weighting coefficient generating means for calculating the weighting coefficient;
Weighting coefficient calculating means for extracting long-term weighting coefficients and repeated weighting coefficients on the day of failure from the long-term weighting coefficients and the repeated weighting coefficients calculated in time series by the weighting coefficient generating means;
An elevator failure recovery system characterized by comprising failure-causing equipment probability correction processing means for correcting the failure probability using the long-term weighting coefficient and the repeated weighting coefficient calculated by the weighting coefficient calculation means. In claim 5 ,
The elevator failure recovery system is characterized in that the maintenance history information includes inspection equipment, equipment replacement history, and equipment maintenance history of the elevator. In claim 5 ,
An elevator failure recovery system characterized by determining an investigation order of equipment to be investigated based on a corrected failure probability. The elevator failure recovery system according to claim 7 , wherein the determined investigation order is transmitted to a mobile terminal.

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