JP6793601B2 - Monitoring device, monitoring system, abnormality detection method - Google Patents

Description

The present invention relates to a system that detects an abnormality in an elevator based on data from a sensor that measures the operating state of the elevator.

Elevators are important as a transportation infrastructure in modern urban spaces, and are required to operate 24 hours a day, 365 days a year, excluding down periods due to planned maintenance. In order to achieve this, in recent years, systems incorporating IoT (Internet of Things) technology have been put into practical use in elevators as well.

In the system, for example, sensors are connected to devices constituting an elevator (hereinafter referred to as elevator devices) such as a main motor, a brake, and a door device. Then, the system collects the measured values of each elevator device measured by the sensor in a data center or the like via the Internet and centrally manages them. The system finds a sign of an abnormality in the equipment, that is, a failure from the measurement data of the equipment collected in the data center, and remotely controls the elevator equipment as needed.

As a method or device for finding abnormalities in the device from the measured values of the device, the measured values of the monitored device in normal times are accumulated, and these data are compared with the measured values of the current monitored device to determine the degree of deviation. (For example, Patent Document 1).

Japanese Unexamined Patent Publication No. 2013-214292

The conventional method does not consider the difference between the status value of the operation data of the monitored device and the general operation data of the same type of device group to which the device belongs. In other words, if there are differences in the operating environment of the equipment (installation location, temperature / humidity, sunlight, corrosive gas concentration in the air, etc.), even if the equipment is exactly the same, there are equipment with a good operating environment and equipment with a severe operating environment. The susceptibility to failure is different, but this is not taken into account. Therefore, there is a problem that the accuracy of abnormality detection is poor and it is difficult to identify the cause of failure (whether it is an environmental factor or a device factor).

The present invention has been made in view of the above problems, and an object of the present invention is to provide a technique for improving the accuracy of abnormality detection.

In order to solve the above problems, a typical monitoring device of the present invention is a monitoring device that detects an abnormality in the elevator, and is a device to be watched based on an output signal from a sensor that measures the devices constituting the elevator. The first state value indicating the operating state is derived, and the derived state value of the gaze target device represents the distribution of the state value at the time of calculation of each of a plurality of devices of the same type as the gaze target device. The first value, which is a value indicating how much the gaze target device deviates from the distribution region, is calculated, and the state value of the gaze target device derived from the distribution region shows the distribution of each of the past state values of the gaze target device. A second value, which is a value indicating how much the second distribution region is deviated from, is calculated, and based on the first value and the second value, whether or not the gaze target device is abnormal. The processing unit that determines whether or not the device, the output unit that notifies the user when it is determined that the gaze target device is abnormal, and the representative value of the first distribution region as the origin with the two state values as axes, respectively. Each quadrant on the graph is divided into a plurality of regions by a straight line passing through the origin, and each region has a storage unit that stores information describing the cause of the abnormality of the gaze target device as a deviation cause. , The processing unit calculates a third value indicating the total degree of deviation based on the first value and the second value, and when the third value exceeds a predetermined threshold value. , The output unit is further specified by identifying the cause of deviation stored in the storage unit in association with the region in which the third value exists on the graph. The cause of the deviation is also notified to the user.

According to the present invention, it is possible to detect an abnormality with high accuracy.
Issues, configurations and effects other than those described above will be clarified by the description of the following embodiments.

It is a figure which shows the configuration example of the monitoring system which concerns on 1st Embodiment. It is a figure which shows the hardware configuration example of the remote monitoring control apparatus which concerns on 1st Embodiment. It is a flowchart which shows the operation example of the process which collects and aggregates the measurement data which concerns on 1st Embodiment. It is a figure which illustrates the list of all the door torque values collected by the day which concerns on 1st Embodiment. It is a figure which shows an example of the table which aggregated the torque value by month which concerns on 1st Embodiment. It is a flowchart which shows the operation example of the abnormality detection processing which concerns on 1st Embodiment. It is a figure which shows an example of the result of extracting the torque value of the door of the same type as the door to be watched which concerns on 1st Embodiment. It is a figure which shows the torque value of the gaze target door which concerns on 1st Embodiment in the past 1 year. It is a figure which shows the concept of the abnormality detection method which concerns on 1st Embodiment. It is a figure which shows the concept of the abnormality cause estimation method which concerns on 1st Embodiment. It is a figure which shows the relationship of the door opening and closing times in a normal state which concerns on 2nd Embodiment. It is a figure which shows the relationship of the door opening and closing times including an abnormal state which concerns on 2nd Embodiment.

Hereinafter, the monitoring device, the monitoring system, and the abnormality detection method of each embodiment will be described with reference to the drawings. The elevator in the embodiment refers to a so-called elevator, an escalator, a moving walkway (auto line), or the like.

(First Embodiment)
FIG. 1 shows an overall configuration example of the monitoring system of the first embodiment. The monitoring system 900 includes at least a sensor 31, an elevator monitoring control device 3, and a remote monitoring control device 1. The remote monitoring and control device 1 is typically installed in the control center of an elevator and is a monitoring device that remotely monitors and controls a plurality of elevators 5. The remote monitoring and control device 1 is connected to a plurality of elevator monitoring and control devices 3 installed on the elevator 5 side via the communication network 2.

The elevator monitoring and control device 3 is a device that monitors and controls the operation of the elevator equipment 4 constituting the elevator 5, such as the main motor, brake, and door device of the elevator 5, which is the management target. The elevator monitoring and control device 3 inputs and monitors the measurement signal of the elevator device 4 read and output by the sensor 31, and controls the elevator device 4 to perform a given operation based on this signal. .. The elevator monitoring and control device 3 may be a computer including a processor, a main storage device, an auxiliary storage device, and the like, and may be a device in which some or all of its functions are mounted by an integrated circuit such as an ASIC (application specific integrated circuit). Good.

Each of the sensors 31 is associated with the elevator device 4, and indicates the operating state of the elevator device 4, such as current value, electric energy value, torque value, rotation speed, number of operations, operating time, speed, acceleration, and temperature. The represented value is measured and output to the elevator monitoring and control device 3. It should be noted that each of these measured values is just an example, and the measured values also differ depending on the type of the elevator device 4.

The communication network 2 may be an IP network such as the Internet or an intranet, an analog line, or a dedicated cable.

The remote monitoring control device 1 includes a communication processing unit 11, a remote monitoring control processing unit 12, a storage unit 13, and an output unit.

The communication processing unit 11 is a communication unit connected to a plurality of elevator monitoring and control devices 3 via the communication network 2, receives measurement data of the elevator equipment 4 obtained from the sensor 31, and is a remote monitoring and control processing unit. Hand over to 12. In the present embodiment, the measurement data received by the communication processing unit 11 is numerical data obtained by digitally converting the signal output by the sensor 31 by the elevator monitoring and control device 3.

The remote monitoring control processing unit 12 determines the abnormality of the elevator device 4 to be monitored based on the measurement data of the elevator device 4 received by the communication processing unit 11 and the data related to the elevator 5 stored in the storage unit 13. The presence or absence is judged regularly or continuously. The output unit 14 is controlled by the remote monitoring control processing unit 12, and when an abnormality is detected in the elevator device 4, the administrator 6 is notified by voice or screen display, and the output unit 14 is managed on a Web basis or the like via a communication means. Notify person 6.

RDBMS (Relational Database Management System, hereinafter simply referred to as a database) is introduced in the storage unit 13, and queries for registering, updating, and deleting various data issued by the remote monitoring control processing unit 12 are executed. It has a function of accepting and executing this, and searching data according to search conditions.

At least the management data of all the elevators 5 to be managed and the measurement data of each elevator device 4 obtained via the communication processing unit 11 are recorded in the storage unit 13. The data structure of the measurement data and how the measurement data is stored and used in the storage unit 13 will be described again with reference to the drawings of FIGS. 3 and 3. The other management data manages the elevator 5 such as at least the identification information of the elevator 5 (hereinafter, "identification information" is referred to as "ID"), the customer ID, the model of the elevator 5, and the configuration table of equipment and parts. Contains the information needed to do this. Further, the management data may include a delivery destination, a delivery date, a contract type, an administrator, a sales office in charge, a rated speed, a load capacity, a history of inspection and maintenance, and a failure history. The management data is not limited to these.

FIG. 2 is a diagram showing an example of the hardware configuration of the remote monitoring control device 1. The remote monitoring control device 1 has a controller 101 and peripheral devices of an input device 110 and an output device 111.

The controller 101 controls each hardware operating inside the remote monitoring control device 1. The controller 101 has the following configuration.

The CPU 102 (CPU: Central Processing Unit) expands the program stored in the ROM 104 (ROM: Read only memory) or the HDD 105 (HDD: Hard Disk Drive) into the RAM 103 (RAM: Random access memory) and executes the calculation. It is a processing device. The CPU 102 comprehensively controls each hardware inside the controller 101 by executing a program. The RAM 103 is a volatile memory and is a work memory when the CPU 102 processes. The RAM 103 temporarily stores necessary data while the CPU 102 calculates and executes the program.

The ROM 104 is a non-volatile memory, and stores the BIOS (Basic Input / Output System) executed by the CPU 102 when the remote monitoring control device 1 is started, and the firmware. The HDD 105 is an auxiliary storage device that stores data non-volatilely. The HDD 105 stores a program executed by the CPU 102 and control data. In the present embodiment, a database is introduced in the HDD 105 in advance, and various data are stored and managed. Further, in order to provide information to the administrator 6 and other users, the HDD 105 is preliminarily provided with a WEB server program that transmits data described in a markup language such as HTML to the outside via an HTTP or HTTPS protocol. It may be introduced in.

The network I / F 106 (I / F: Interface) is an interface board that controls data communication performed with an external device.

The input I / F 107 is an interface that controls the input / output of signals to / from the input device 110. The output I / F 108 receives an instruction from the CPU 102 and causes the output device 111 to draw an image.

The input device 110 is, for example, a keyboard or mouse, and the output device 111 is a monitor or display. The touch panel display may be configured by the input device 110 and the output device 111. Further, the output device 111 may be connected to a printer that forms an image on the sheet. In this case, the output device 111 corresponds to a printer.

In each functional block of the communication processing unit 11, the remote monitoring control processing unit 12, the storage unit 13, and the output unit 14 shown in FIG. 1, the CPU 102 in the controller 101 calculates and executes a program stored in the HDD 105 in advance. It is realized by working in cooperation with each hardware.

Hereinafter, the operation of the remote monitoring control device 1 of the present embodiment will be described in more detail. In the following description, as an example, the door opening / closing device of the elevator 5 will be described as the elevator device 4 to be monitored, but the application target is not limited to this. In the present embodiment, any device constituting the elevator, such as a main motor of the elevator, a brake, a governor (speed governor), a control device, and a load detection device, can detect an abnormality by targeting the device as a monitoring target.

Further, in the following description, it is assumed that the sensor 31 measures the torque value of the door opening / closing device, and the average value is calculated for the measurement data acquired with the passage of time on a daily or monthly basis. The mode of summarizing will be described. Specifically, the sensor 31 attached to the door opening / closing device measures the door torque value for each door opening / closing operation over time, and the elevator monitoring / control device 3 accumulates the door torque values for one day. At the end of the day, the elevator monitoring and control device 3 obtains the average door torque value of the day by dividing this cumulative value by the number of times the door is opened and closed on the day. The elevator monitoring and control device 3 transmits the obtained average door torque value to the communication processing unit 11 of the remote monitoring and control device 1 via the communication network 2. The communication processing unit 11 receives the average door torque values from all the elevators 5 to be managed, and delivers them to the remote monitoring control processing unit 12.

FIG. 3 is a flowchart showing an operation example of the remote monitoring control processing unit 12, and is a flowchart showing an operation example of accumulating and totaling the delivered average door torque value in the storage unit 13. The solid line arrow in FIG. 3 indicates the processing flow, and the broken line arrow indicates the data flow.

The remote monitoring control processing unit 12 integrates the daily measurement data of all the elevators to be managed delivered from the communication processing unit 11 and stores them in the database of the storage unit 13 (S001).

FIG. 4 is a diagram showing an example of a table configuration of data stored in the storage unit 13. In the table 400 shown in FIG. 4, each row (record) includes at least a column of elevator ID, door ID, door type, data measurement date, and average door torque value of the day. For example, in the first line (record), the door type of the elevator (ID: A123) door (ID: A123-01) is CO-800, and the average door torque on March 31, 2017 is 102 (%). ). Each row (record) corresponds to each door of the elevator 5 to be managed. Further, as shown in FIG. 4, the data of all the elevators 5 to be managed are integrated and accumulated in the table 400.

Returning to the description of FIG. The remote monitoring control processing unit 12 determines whether or not the measurement date is the last day of the month (the closing date of the monthly data aggregation) (S002). In this example, the data measurement date is March 31, 2017, which corresponds to the last day of the month (S002: Yes), so the remote monitoring control processing unit 12 executes S003. If it does not correspond to the last day of the month (S002: No), the process returns to S001 and waits for data from the communication processing unit 11.

The remote monitoring control processing unit 12 refers to the table 400 stored in the storage unit 13 and extracts the measurement data for the current month of all the elevator doors to be managed (S003). Then, the remote monitoring control processing unit 12 obtains the average value and standard deviation of the measurement data of the current month for all the elevator doors to be managed, and stores them in the storage unit 13 (S004).

FIG. 5 is an example of a table stored in the storage unit 13 including the result of the process of step S004. In the table 500 shown in FIG. 5, each row (record) includes at least columns for elevator ID, door ID, door type, data aggregation date, monthly average door torque value, and standard deviation of door torque. For example, in the first row (record), the door type of the elevator (ID: A123) door (ID: A123-01) is CO-800, and the monthly average door torque in March 2017 is 106 (%), which is standard. It shows that the deviation is 5.8 (%). The storage unit 13 stores a plurality of rows (records) including the monthly average door torque value and the standard deviation of the door torque, and each row (record) corresponds to each door of all the elevators 5 to be managed.

The remote monitoring control processing unit 12 repeatedly executes the flowchart shown in FIG. 3 to accumulate the measurement data of all the elevators 5. In addition, the remote monitoring control processing unit 12 calculates the average value and standard deviation of the door torque value for the current month, and stores these as well.

In the example of FIG. 3, the measurement data acquired over time is averaged on a daily basis and summarized, and this is used as the minimum unit and aggregated on a monthly basis, but the present invention is not limited to this mode. The administrator can arbitrarily decide at what interval the measurement data is collected and at what period the measurement data is aggregated according to the measurable cycle and period. Depending on the type of elevator device 4, for example, it is possible to collect measurement data in seconds and aggregate it in minutes, or aggregate measurement data in weeks and aggregate it in months or years. Further, the measurement data may be collected and aggregated in a cycle or period arbitrarily specified by a user such as the administrator 6.

Further, the processing of the flowchart shown in FIG. 3 may be performed by an external computer different from the remote monitoring control device 1, and each data table collected and aggregated is also stored in an external storage unit different from the storage unit 13. , May be managed. In this case, the remote monitoring control processing unit 12 acquires data from an external storage unit as needed, and executes each operation described below.

FIG. 6 is a flowchart showing an example of an abnormality detection process executed by the remote monitoring control processing unit 12. The process of FIG. 6 may be performed regularly, such as once a day or once a month, or every time the database of the storage unit 13 is updated. However, in the following, for the sake of simplicity, the explanation will be simplified. It will be explained assuming that it will be carried out once at the end of every month.

The remote monitoring control processing unit 12 selects the IDs of the gaze target doors in a predetermined order (S101). A user such as the administrator 6 may select the ID at an arbitrary timing. Here, it is assumed that the door with the ID of A123-01 is selected as the gaze target door.

In step S102, the remote monitoring control processing unit 12 calculates how much the current state value of the gaze target door deviates from the standard state value of the group to which the gaze target door belongs.

The state value is the measurement data itself obtained from the sensor 31, or data obtained by processing the measurement data, and is a numerical value of the operation and state of the elevator device 4 (door opening / closing device), that is, the operating state. In this example, the average value of the door torque value and the standard deviation of the door torque value are derived from the door torque value which is the measurement data, and these are used as the state values. Which data is used as the state value is determined by a user such as a designer, a developer, or an administrator according to the type of the target elevator device 4 and the measurement data obtained from the sensor 31. Further, the door group is a group of doors of the same type, and typically is a group of devices having the same type of door opening / closing device (door types shown in FIGS. 4 and 5).

The details of step S102 will be described. The remote monitoring control processing unit 12 shows the data of the target month (March 2017) of the door of the same type (CO-800) as the gaze target door (A123-01) among the managed doors, as shown in FIG. Extract from table 500. Then, the remote monitoring control processing unit 12 creates a table 700 by listing a plurality of extracted records (S102A).

FIG. 7 is an example of the table 700 created by S102A. Each row (record) of the table 700 contains at least each column of elevator ID, door ID, door type, data measurement date, monthly average door torque value, and standard deviation of door torque. Table 700 is data in which records having the same door type (CO-800) and data measurement date (March 2017) are extracted from table 500 shown in FIG. 5 and listed. In step S102A, the remote monitoring control processing unit 12 also calculates the average value and standard deviation of the monthly average door torque value and the door torque standard deviation, respectively. In step S102A, the remote monitoring control processing unit 12 excludes and extracts peculiar numerical values, and calculates an average value and a standard deviation.

Returning to the description of FIG. Using the table 700, the remote monitoring control processing unit 12 obtains the degree of deviation D1 of the state value of the gaze target door (A123-01) from the normal state of the CO-800 group to which the door belongs. The degree of deviation D1 is, in the present embodiment, how much the state value indicating the operating state of the gaze target device deviates or deviates from the first distribution region representing the distribution of the state values of each of a plurality of devices of the same type as the gaze target device. It is numerical data (first value) indicating whether or not it is done.

For the calculation of the deviance D1, a general statistical value such as the Mahalanobis distance may be used. In the following, the case where the Mahalanobis distance is used will be described for reference, but the specific calculation method is not limited to this.

Here, the current state value X of the door to be watched is the average value of the door torque of the current month (March 2017) (hereinafter referred to as x1) recorded in the storage unit 13, and the standard deviation value of the door torque of the current month (hereinafter referred to as x1). Hereafter, consider the case where it is represented by two variable vectors (referred to as x2). That is, the state value X is expressed by the following equation.
X = (x1, x2) ^T (Equation 1)
The T on the right shoulder of the vector represents the transpose matrix.

Furthermore, the state value U (= average value vector) of the door torque average value (u1) and standard deviation (u2) of the current month of the door of the group to which the gaze target door belongs is set.
U = (u1, u2) ^T (Equation 2)
And. If the covariance matrix of X and U (the matrix in which the covariance between each variable is arranged) is Σ _U , the state value X of the gaze target door is the standard state value U of the door of the group to which the gaze target door belongs. The Mahalanobis distance (= deviance D1) with respect to is defined as follows.
D1 = {(X-U) ^T · Σ _U ^-1 · (X-U)} ^1/2 (Equation 3)
-1 on the right shoulder of the matrix represents the inverse matrix. Here, the larger the Mahalanobis distance (D1) is, the more the state value X of the gaze target door is separated from the standard state value U of the door of the group to which the gaze target door belongs. That is, D1 represents the degree of deviation from the normal state of the same type of group.

Here, from the data of FIG. 7, the state value X of the gaze target door (A123-01) is X = (110, 6.1) ^T , and the result of obtaining the Mahalanobis distance (D1) in the normal state of the same type door. , D1 = 1.7 is obtained.

Returning to the description of FIG. In step S103 of FIG. 6, the remote monitoring control processing unit 12 calculates how much the state value of the gaze target door (A123-01) deviates from the past state value of the door.

The details of step S103 will be described. The remote monitoring control processing unit 12 extracts the state values of the gaze target door (A123-01) for the past one year (February 2016 to March 2017) from the table 500 shown in FIG. 5, and performs a plurality of corresponding records. Table 800 is created by acquiring and listing (S103A).

FIG. 8 is a diagram showing an example of the table 800 created in step S103A. Each row (record) of the table 800 includes at least each column of elevator ID, door ID, door type, data measurement date, monthly average door torque value, and door torque standard deviation, as in the above table 700. Table 800 is data obtained by extracting records having the same door ID of A123-01 from Table 500 shown in FIG. 5 during the period from February 2016 to March 2017 and listing them. .. In addition, the remote monitoring control processing unit 12 also calculates the average value and standard deviation of each of the monthly average door torque value and the door torque standard deviation in step S103A. In step S103A, the remote monitoring control processing unit 12 excludes and extracts peculiar numerical values, and calculates an average value and a standard deviation.

The remote monitoring control processing unit 12 uses the table 800 to obtain the degree of deviation D2 of the state value of the gaze target door (A123-01) from the state value of the door for the past one year. In the present embodiment, the deviance degree D2 is a numerical value indicating how much the state value of the gaze target device deviates or deviates from the second distribution region representing the distribution of each past state value of the gaze target device. Data (second value).

For the calculation of the deviance D2, a general statistical value such as the Mahalanobis distance may be used. In the following, the case where the Mahalanobis distance is used will be described for reference, but the calculation method is not limited.

The state value X of the door to be watched for the current month is set in the same manner as in step S102A.
X = (x1, x2) ^T (Equation 4)
The state value V (= average value vector) of the torque average value (v1) and standard deviation (v2) of the door for the past year is set to
V = (v1, v2) ^T (Equation 5)
Let Σ _V be the covariance matrix of X and V (the matrix in which the covariance between each variable is arranged), and the state value X of the gaze target door is the standard state value V of the gaze target door for the past year. The Mahalanobis distance (= deviance D2) with respect to is defined as follows.
D2 = {(XV) ^T・ Σ _V ^-1・ (XV)} ^1/2 (Equation 6)
The -1 on the right shoulder of the matrix represents the inverse matrix. Here, as the Mahalanobis distance (D2) becomes larger, it means that the state value X of the door to be watched in the current month is farther from the state value V of the door in the past year. That is, D2 represents the degree of deviation from the past state value of the door itself.

Here, from the data of FIG. 7, the Mahalanobis distance (D2) of the state value X = (110, 6.1) ^T of the gaze target door (A123-01) from the normal state for the past one year was obtained, and as a result, D2 It is assumed that = 1.5 is obtained.

Although the data of "the past one year" is used here for explanation, an appropriate period may be set according to the characteristics of the failure to be detected.

Returning to the description of FIG. The remote monitoring control processing unit 12 calculates the total deviance degree D3 by using the deviance degree D1 obtained in step S102 and the deviance degree D2 obtained in step S103 (S104).

FIG. 9 is a diagram showing a conceptual diagram of the abnormality detection method according to the present embodiment. Here, the position (state value) of the current gaze target door is A, the normal region of the same type of door group as the gaze target door is R1 (first distribution region), and the past normal region of the gaze target door is R2 (second distribution region). ). The points O and P are the center points of R1 and R2, respectively. In the present embodiment, the average value of the state values (mean value, standard deviation) included in each region is set as the center point.

FIG. 9 shows an example in which the position A of the gaze target door is outside the respective regions of R1 and R2 and deviates from each other. Here, the degree of deviation of the gaze target door A is preferably determined based on the degree of deviation D1 from R1 and the degree of deviation D2 from R2. A large D1 means that, for example, the installation environment and usage conditions of the gaze target door A are different from those of other doors of the same type, and caution is required when monitoring. However, even if D1 is large, if D2 is small, the state of the gaze target door A is stable and cannot be said to be abnormal. On the contrary, when D1 is small but D2 is large, it means that the behavior of the gaze target door A is different from the original operation of the gaze target door A, so that it is necessary to closely examine the state. In this way, by determining the deviation degree of the gaze target door A based on the values of both D1 and D2, highly accurate abnormality detection becomes possible.

In the present embodiment, the total deviance D3 of the gaze target door A is calculated as follows by using an arbitrary function F with D1 and D2 as arguments.
D3 = F (D1, D2) (Equation 7)
Here, F is a function for obtaining the total deviance using D1 and D2, and should be set in consideration of the characteristics of the failure of the target device. For example, in addition to the function for obtaining the linear sum of D1 and D2, a learning type calculation model such as a multidimensional function, a trigonometric function, or a neural network may be used. In the present embodiment, the square root of the sum of the squares of D1 and D2 is used for the calculation of D3, but the calculation method of D3 is not particularly limited.

Using the square root of the sum of the squares of D1 and D2 for the calculation of D3, the following formula is obtained.
D3 = (D1 ² + D2 ² ) ^1/2 (Equation 8)
In this example, since D1 = 1.7 and D2 = 1.5, D3≈2.3.

Returning to the description of FIG. The remote monitoring control processing unit 12 determines whether or not the total deviation degree D3 obtained in step S104 exceeds the threshold value T given in advance (S105). Here, it is assumed that an abnormal value is set when D3 exceeds 2.0. In this example, since D3 is 2.3, D3 exceeds the threshold value T (S105: Yes). Therefore, the remote monitoring control processing unit 12 records in the storage unit 13 that an abnormality in the data is found, and also controls the output unit 14 to perform the abnormality processing for notifying the administrator 6 (S107). On the other hand, when D3 does not exceed the threshold value (S105: No), the remote monitoring control processing unit 12 performs a normal process of recording in the storage unit 13 that the data is in a normal value (S106).

After step S106 or step S107, the process returns to S101, and the process of steps S101 to S107 described above is repeated, for example, with another door as the gaze target door. This makes it possible to periodically check the condition of all managed doors.

In the determination of step S105, not only the total deviance D3 but also the deviances D1 and D2 are provided with the thresholds T1 and T2, respectively, the comparison result between the threshold T1 and the deviance D1, and the threshold T2 and the deviance. The comparison result with D2 may also be considered. For example, if D3> threshold value T is satisfied in step S105, an abnormal process is performed in the example of FIG. However, if the degree of deviation D2 from the normal state of the gaze target door (A123-01) itself for the past year is small, as described above, it cannot be said that it is abnormal, so it should not be regarded as an abnormal state. In some cases it is better to treat it as normal. Therefore, even if D3> threshold value T is satisfied, if D2 <threshold value T2 is satisfied, it may be regarded as a normal state. In addition to this, even when the deviance degree D2 is small, when the deviance degree D1 is extremely large (D1> threshold value T1), it may be changed to an abnormal state instead of a normal state. Further, in step S105, the overall deviation degree D3 may not be determined, but may be determined only by the comparison result between the threshold value T1 and the deviation degree D1 and the comparison result between the threshold value T2 and the deviation degree D2. In this case, the process of step S104 for calculating the total deviance D3 is also unnecessary. Further, an implementation that determines only by comparing the threshold value T1 and the deviance degree D1 or an implementation that determines only by comparing the threshold value T2 and the deviance degree D2 may be used.

The flowchart shown in FIG. 6 has been described as being carried out once at the end of each month as described above, but the embodiment is not limited to this. Each time the elevator monitoring and control device 3 inputs the output signal of the sensor 31, the flowchart shown in FIG. 6 may be executed in real time. In this case, when the output signal of the sensor 31 is changed, or at a predetermined cycle such as seconds, minutes, or hours, the elevator monitoring and control device 3 communicates the measurement data together with necessary information such as the door ID. It is transmitted to unit 11. The remote monitoring control processing unit 12 of the remote monitoring control device 1 that has received the measurement data implements the flowchart of FIG. In this case, in step S101, the door ID currently received is selected as the gaze target door. The remote monitoring control processing unit 12 obtains an average value and a standard deviation using the measurement data obtained from the specified time before to the present, and calculates the deviation degrees D1, D2, and D3 with respect to this state value. An abnormality is determined in step S105. Further, in order to speed up the processing of FIG. 6, the center points, state values U, V, etc. of each area of the table 700, the table 800, R1 and R2, or each area are obtained and defined in advance. May be good. In this case, steps S102 to S103 are only operations for obtaining the deviance degrees D1 and D2 using these defined data.

By the monitoring device, monitoring system, and abnormality detection method described above, the status values of all the elevator equipment 4 to be managed are continuously collected and accumulated, and the comprehensive deviation of the status values of the elevator equipment 4 is performed at any time or periodically. By inspecting the degree, highly accurate abnormality detection becomes possible.

Subsequently, the estimation of the cause of deviation (cause of abnormality) of the present embodiment will be described. When the measured value collected from the elevator device 4 deviates from the normal region, an abnormality determination is made based on the magnitude of the deviation, which is the degree of deviation. From the degree of deviation calculated above, not only the magnitude of deviation but also the positional relationship between each region of R1 and R2 and the deviated data and information on the direction of deviation can be obtained. It is also possible to estimate the cause of deviation by using the information on the positional relationship and the deviation direction.

FIG. 10 is a conceptual diagram of a method for estimating the cause of deviation. In the graph shown in FIG. 10, a distribution diagram is shown in which the average door torque value, which is one of the state values, is on the x1 axis (horizontal axis), and the standard deviation of the door torque, which is the other state value, is on the x2 axis (vertical axis). There is. Further, the case where the state value of the door to be watched deviates in the x1 axis direction is defined as the deviation case C1, and the case where the state value deviates in the x2 axis direction is defined as the deviation case C2. Here, the total deviance D3 is the vector sum of D1 and D2.

In the case of the door opening / closing device, the fact that x1 increases more than usual (deviation case C1 in FIG. 10) means that the operation of the door becomes heavier. In the case of the deviation case C1, it is presumed that there is a foreign substance such as sand or a plastic piece in the sill groove of the elevator door, for example. Further, the fact that x2 increases more than usual (deviation case C2 in FIG. 10) means that the weight of the door operation has changed. In the case of deviation case C2, it is presumed that, for example, a foreign matter caught in the door was removed later, or a draft phenomenon (a strong wind pressure applied to the door hinders the operation of the door) occurred due to a change in the weather. ..

An implementation example for estimating the cause of deviation from the direction of the degree of deviation will be described below. In the present embodiment, the change direction (angle) of the state value of the elevator device 4 and the cause of the abnormality are associated and registered in the storage unit 13 in advance. Specifically, one lap (360 degrees) is divided into eight, for example, separated by 45 degrees, and text data describing the cause of deviation is associated with each division and registered in the storage unit 13. Here, as an example, it is divided into 8 sections by 45 degrees, but the mode is not limited to this.

When a deviation occurs, the remote monitoring control processing unit 12 derives the deviation direction from the average value (x1) and the standard deviation value (x2) of the door torque. In the present embodiment, the remote monitoring control processing unit 12 sets the direction of the deviance D3 as the deviation direction when the center point O, which is a representative value of the region R1, is the starting point and the x1 axis is the reference angle (0 degree). To do.

The remote monitoring control processing unit 12 searches for a cause candidate corresponding to the deviation direction from the above correspondence. Then, the remote monitoring control processing unit 12 operates the output unit 14 to display text data describing the angle of the deviation direction and the cause of the deviation, which is the search result, on the output device 111 such as a monitor. It is also possible to print out on paper. With this implementation example, it is possible not only to detect an abnormality but also to estimate the cause thereof, and it is possible to quickly identify the cause of the abnormality and take a subsequent action.

In the above example, the deviation direction is derived from the representative value (point O) of R1, but the deviation direction may be derived from the representative value (point P) of R2 depending on how the coordinate axes are taken. The center point of O and the point P may be the starting point.

In the above example, the center point of each region is used as a representative value, and the magnitude and direction of the degree of deviation from this representative value are derived, but the method of taking the representative value is not limited to this. For example, among the data in the region, the closest value or the farthest value from the deviated data may be regarded as a representative value of the region to derive the distance or direction. In addition to this, the magnitude and direction of the deviation degrees D1, D2, and D3 may be calculated from any data as long as it is data in the region. In other words, the remote monitoring control processing unit 12 calculates the magnitude and direction of the deviance degrees D1, D2, and D3 based on the positional relationship between the deviation data and each area on the graph, determines an abnormality, and determines the cause of the deviation. The written text data may be acquired from the storage unit 13 and output to the output unit 14.

Further, the remote monitoring control processing unit 12 creates image data of the graphs shown in FIGS. 9 and 10, that is, the distribution map showing the regions R1 and R2 and the deviated state values. The remote monitoring control processing unit 12 operates the output unit 14 to display the created distribution map on the output device 111, display it on a specified mobile terminal via a network, or print it out on paper. .. The remote monitoring control processing unit 12 can also combine numerical data of the magnitude and direction of the deviation degree and text data describing the cause of the deviation with the distribution map, display it, or print it out.

By displaying and printing out in this way, for example, it can be used as explanatory material when an abnormality occurs for a customer who is a delivery destination of the elevator 5. Further, as described with reference to FIG. 10, since it is possible to estimate the cause of the abnormality, it can also be used for a proposal for suppressing this cause. For example, it can be used as a material for proposing the installation of a member that prevents foreign matter from being caught in the sill groove of an elevator door, or for the installation of an outer wall member that is less susceptible to the effects of wind pressure. it can.

In the above embodiment, the mean value and the standard deviation are adopted as the state values, but the present invention is not limited to this, and for example, the median value, the mode value, and the variance may be used as the state values. Further, in the above embodiment, the number of elements of the state value is set to two, the average value and the standard deviation, but the mode is not limited to this. The number of elements may be one, three or more. When the number of elements of the state value is one, it may be a linear graph, and when it is three or more, it may be a three-dimensional graph. The graph notation method is not limited to the embodiment of the present embodiment.

In the above embodiment, the elevator has been described as an example of the monitoring target, but the present invention is not limited to this, and can be applied to a mode in which a mechanically operating device or device such as an automatic door is targeted for monitoring.

(Second Embodiment)
In the first embodiment, the implementation example in which the sensor 31 measures the torque value of the door opening / closing device and makes an abnormality determination based on the measured value has been described. In the second embodiment, as another example, an implementation example in which the sensor 31 measures the “opening count” and the “closing count” of the door will be described. As for the configuration of the monitoring system, the hardware configuration in the device, and the processing flow, those described in the first embodiment will be diverted.

FIG. 11 is a distribution diagram showing the relationship between the “number of times the door is opened” and the “number of times the door is closed” in the normal state. The horizontal axis is the number of times the door is closed, and the vertical axis is the number of times the door is opened. It is a plot of the number of closings and the number of openings per day.

The "opening count" and the "closing count" of the door are closely correlated with each other, and ideally, it is 1: 1. Therefore, when the points of the same type of door of the elevator 5 are plotted with the daily "opening count" and "closing count" as coordinates, they usually ride on one straight line as shown in FIG. This straight line is the normal range for the door of this type of elevator 5.

FIG. 12 is a distribution diagram when an abnormal value is included. If there is an abnormality or malfunction in the door, the operation of the door becomes unstable, and as shown in the broken line frame 1201 in FIG. 12, the plot points of the day deviate from the straight line (= deviation from the normal range occurs). In the second embodiment, the degree of deviation from the straight line is D1. D1 is an index that indicates the possibility of a door malfunction.

Further, when focusing on the specific elevator 5, even if the plot points are in the normal range (on a straight line), the "number of closed times" and "number of times of opening" are the same as shown by the circular frames 1202, 1203 and the arrow 1204. It may increase or decrease significantly from the normal value of the elevator 5 (circular frame 1202). FIG. 12 shows that the number of times the door to be watched is opened and closed has changed from the value in the circular frame 1202 to the value in the circular frame 1203, which is greatly reduced. In this case, it indicates the possibility that a door abnormality has occurred, such as a sudden change in the way the door of the elevator 5 is used, or the door does not open. This amount of change is defined as the deviance degree D2. D2 can also be used as an index showing the possibility of a door malfunction.

Also in the second embodiment, the deviation degree D1 is numerical data indicating how much the deviation is from the first distribution region (straight line) showing the distribution of the measured values of the same type of equipment. Further, the deviance degree D2 indicates how much the deviation degree D2 deviates from the second distribution region (data near the circular frame 1202 in FIG. 12) showing the distribution of the measured values from the past of the target device itself to be watched. It is the numerical data shown.

The remote monitoring control processing unit 12 of the second embodiment comprehensively determines the abnormality of the door based on the above D1 and D2. The flowcharts shown in FIGS. 3 and 6 can be applied to the operation of the remote monitoring control processing unit 12. Further, as in the first embodiment, the remote monitoring control processing unit 12 can estimate the cause of deviation. Similar to the first embodiment, the output unit 14 can present the distribution map illustrated in FIG. 12 to the customer as a material, explain the abnormality or malfunction, and propose preventive measures.

There are many such correlations other than the "opening count" and "closing count" of the door. That is, the aspect of the second embodiment is just an example as in the first embodiment.

In the first and second embodiments, the regions R1 and R2 are limited to regions excluding peculiar state values, that is, normal state values, but regions including peculiar state values are included. R1 and R2 may be specified.

Each function provided by the remote monitoring control processing unit 12 described in the first and second embodiments may be divided into a plurality of functions. Further, the functions divided in this way may be divided into a plurality of devices or housings (servers) for each function in a system configuration.

The processing unit, the first processing unit, the second processing unit, the third processing unit, and the fourth processing unit correspond to the remote monitoring control processing unit 12 of the first and second embodiments. The communication unit corresponds to the communication processing unit 11 of the first and second embodiments.

As described in detail above, according to the present embodiment, the degree of deviation from the normal region of the same type of device group and the degree of deviation from the past normal region of the device to be watched are determined in determining the current state value of the device to be watched. Calculate the overall deviance based on. This makes it possible to perform highly accurate abnormality determination based on the individuality of the device to be watched. In addition, since the cause of deviation can be estimated based on the deviation direction, it is possible to speed up the response in the event of an abnormality.

The present invention is not limited to the above-described embodiment, and includes various modifications. For example, the above-described embodiment has been described in detail in order to explain the present invention in an easy-to-understand manner, and is not necessarily limited to the one including all the described configurations. Further, it is possible to replace a part of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of one embodiment. Further, it is possible to add / delete / replace a part of the configuration of each embodiment with another configuration. Further, each of the above configurations, functions, processing units, processing means and the like may be realized by hardware by designing a part or all of them by, for example, an integrated circuit. Further, each of the above configurations, functions, and the like may be realized by software by the processor interpreting and executing a program that realizes each function. Information such as programs, tables, and files that realize each function can be stored in a memory, a hard disk, a recording device such as an SSD (Solid State Drive), or a recording medium such as an IC card, an SD card, or a DVD.

1: Remote monitoring and control device, 2: Communication network, 3: Elevator monitoring and control device,
4: Elevator equipment, 5: Elevator, 6: Administrator, 11: Communication processing unit,
12: Remote monitoring control processing unit, 13: Storage unit, 14: Output unit, 31: Sensor,
900: Surveillance system.

Claims (7)

It is a monitoring device that detects abnormalities in elevators.
Based on the output signals from the sensors that measure the equipment that makes up the elevator, two state values that indicate the operating state of the equipment to be watched are derived.
It shows how much the state value of the gaze target device to be derived deviates from the first distribution region representing the distribution of the state value at the time of calculation of each of a plurality of devices of the same type as the gaze target device. Calculate the first value, which is the value,
The state value of the gaze target device is derived is, the second value from the second distribution area is a value indicating a divergence representing the state value of each distribution past the gaze target device Calculate and
A processing unit that determines whether or not the gaze target device is abnormal based on the first value and the second value.
When it is determined that the device to be watched is abnormal, an output unit that notifies the user and
Each quadrant on the graph with the two state values as axes and the representative value of the first distribution region as the origin is divided into a plurality of regions by a straight line passing through the origin, and each region has an abnormality of the gaze target device. A storage unit that stores information describing the cause of the deviation as the cause of deviation,
Have a,
The processing unit
Based on the first value and the second value, a third value indicating the total deviance is calculated.
When the third value exceeds a predetermined threshold value, it is determined to be abnormal, and the deviation stored in the storage unit in association with the area where the third value exists on the graph. Identify the cause and
The output unit is a monitoring device that also notifies the user of the identified cause of deviation . The monitoring device according to claim 1.
The processing unit further creates an image in which the first distribution region, the second distribution region, the first value, and the second value are illustrated on a graph.
The output unit is a monitoring device that further outputs the image created by the processing unit. The monitoring device according to claim 1.
Said storage unit further stores the state values of the devices constituting the elevator,
Wherein the processing unit from the storage unit, the state value to obtain multiple, based on a plurality of the state values obtained, to derive the said first distribution area and the second distribution area, the monitoring device. The monitoring device according to any one of claims 1 to 3.
A monitoring device that does not determine that the processing unit is abnormal even when the third value exceeds the threshold value when the second value is less than a predetermined second threshold value. The monitoring device according to claim 4.
Even if the second value is less than the second threshold value, the processing unit determines that the abnormality is abnormal if the first value is equal to or higher than the predetermined first threshold value. apparatus. A monitoring system that detects abnormalities in elevators
Sensors that measure the equipment that makes up the elevator,
A communication unit that receives the measurement data measured by the sensor, and
Based on the measurement data received by said communication unit, a first processing unit for deriving the two state values indicating the operating state of the gaze target device,
A storage unit for said each of the state values plurality of devices gaze target devices the same type stored, a plurality accumulates past the status value of the gaze target device,
From the storage unit, acquires the status value at a plurality of devices each of calculation target of the gaze target device the same type, from the first distribution area represents the distribution of the acquired state value derived by the first processing unit a second processing section for calculating a first value is a value indicating whether the state value is how much deviation is,
From the storage unit, the acquired past the state value each gaze target device, from the second distribution region representing the distribution of the acquired state value, the state value how much derived by the first processing unit A third processing unit that calculates a second value, which is a value indicating whether or not it deviates,
Based on the first value and the second value, a fourth processing unit that determines whether or not the gaze target device is abnormal, and
When the fourth processing unit determines that the gaze target device is abnormal, an output unit that notifies the user and
Have a,
The storage unit divides each quadrant on the graph having the two state values as axes and the representative value of the first distribution region as the origin into a plurality of regions by a straight line passing through the origin, and the storage unit divides each quadrant into a plurality of regions. Further memorize the information describing the cause of the abnormality of the device to be gazed as the cause of deviation,
The fourth processing unit
Based on the first value and the second value, a third value indicating the total deviance is calculated.
When the third value exceeds a predetermined threshold value, it is determined to be abnormal, and the deviation stored in the storage unit in association with the area where the third value exists on the graph. Identify the cause and
The output unit is a monitoring system that also notifies the user of the identified cause of deviation . It is an abnormality detection method that detects an abnormality in an elevator.
Based on the output signals from the sensors that measure the equipment that makes up the elevator, two state values that indicate the operating state of the equipment to be watched are derived.
It shows how much the state value of the gaze target device to be derived deviates from the first distribution region representing the distribution of the state value at the time of calculation of each of a plurality of devices of the same type as the gaze target device. Calculate the first value, which is the value,
The state value of the gaze target device is derived is, the second value from the second distribution area is a value indicating a divergence representing the state value of each distribution past the gaze target device Calculate and
Based on the first value and the second value, a third value indicating the total deviance is calculated.
When the third value exceeds a predetermined threshold value, it is determined to be abnormal and it is determined to be abnormal.
The cause of deviation stored in the storage unit is identified by associating the area where the third value exists on the graph with the two state values as axes and the representative value of the first distribution area as the origin.
When it is determined that the device to be watched is abnormal, the computer executes a process of notifying the user and notifying the user of the identified cause of deviation .
In the storage unit, each quadrant of the graph is divided into a plurality of the regions by a straight line passing through the origin, and information describing the cause of the abnormality of the gaze target device is stored as the deviation cause in each region. abnormality detection method it is.