Information system and information acquisition method

Abstract

The invention relates to an information system and an information acquisition method, which can relax the acquisition conditions of information used for calculating a characteristic quantity associated with the torque of a traction machine. An information system (1) includes an information acquisition unit (22) and a regression formula generation unit (23). The information acquisition unit (22) acquires first information indicating the position of the car (11) and second information indicating the torque value of the hoist (15). A regression formula generation unit (23) generates a primary regression formula between the position of the car (11) and the torque value of the hoist (15) during constant-speed travel on the basis of the first information and the second information acquired by the information acquisition unit (22). On the basis of the primary regression equation, for example, a feature quantity that can reflect the monitoring result in a maintenance plan of the elevator device (10) can be calculated.

Description

Technical Field

[0001] The present invention relates to an information system and an information acquisition method. Background Art

[0002] Patent document 1 discloses an elevator control device. In the control device disclosed in Patent document 1, a torque current command value is obtained when the car is located at the middle position of the hoistway in a state where the car is carrying a balanced load. In the control device, an unbalanced current and a loss torque current are calculated based on the obtained values.

[0003] Prior art literature

[0004] Patent Document 1: International Publication No. 2018 / 100632 Summary of the invention

[0005] In the control device described in Patent Document 1, the torque current command value must be obtained in a state where the car is loaded with a balanced load. This has the problem that the conditions for obtaining information required for calculating characteristic quantities such as loss torque current are strict.

[0006] In addition, the control device described in Patent Document 1 acquires the torque current command value only when the car is loaded with a balanced load. The number of samples of this value is small, and there is a problem that characteristic quantities such as loss torque current cannot be calculated with high accuracy.

[0007] The present invention has been made to solve the above-mentioned problems.

[0008] An object of the present invention is to provide an information system and an information acquisition method capable of relaxing conditions for acquiring information that can be used to calculate a characteristic amount associated with the torque of a hoisting machine.

[0009] Another object of the present invention is to provide an information system and an information acquisition method capable of acquiring and storing information that can be used to calculate a characteristic amount associated with the torque of a hoisting machine with high accuracy.

[0010] The information system of the present invention comprises: an information acquisition unit, which acquires first information indicating the position of an elevator car and second information indicating the torque value of a traction machine driving the car; and a first regression equation generation unit, which generates a linear regression equation of the position of the car and the torque value of the traction machine during the first travel of the car at a constant speed in the hoistway based on the first information acquired by the information acquisition unit and the second information.

[0011] The information system of the present invention comprises: an information acquisition unit, which acquires third information and fourth information, wherein the third information indicates the travel direction of the elevator car during the first travel at a constant speed in the hoistway, and the fourth information indicates the load in the elevator car during the first travel at a constant speed in the hoistway; and a first storage unit, which associates the third information and the fourth information acquired by the information acquisition unit with the fifth information indicating the torque value of the traction machine driving the car as a set of state information and stores them. The fifth information is information indicating the torque value when the car is located in the middle position of the hoistway in the first travel. By performing the travel of the car at a constant speed multiple times, multiple sets of state information are stored in the first storage unit.

[0012] The information acquisition method of the present invention includes: an information acquisition step, acquiring first information indicating the position of an elevator car and second information indicating the torque value of a traction machine driving the car; and a first regression equation generating step, generating a linear regression equation of the position of the car and the torque value of the traction machine during the first travel of the car at a constant speed in the hoistway based on the first information and the second information obtained in the information acquisition step.

[0013] The information acquisition method of the present invention comprises: an information acquisition step of acquiring third information and fourth information, wherein the third information indicates the travel direction of the elevator car during the first travel at a constant speed in the hoistway, and the fourth information indicates the load in the elevator car during the first travel at a constant speed in the hoistway; and a first storage step of associating the third information and the fourth information obtained by the information acquisition step with the fifth information indicating the torque value of the traction machine driving the car as a group of state information and storing them in the first storage unit. The fifth information is information indicating the torque value when the car is located at the middle position of the hoistway in the first travel. By performing the car travel at a constant speed multiple times, multiple groups of state information are stored in the first storage unit.

[0014] Effects of the Invention

[0015] According to the present invention, it is possible to relax the conditions for obtaining information that can be used to calculate the characteristic amount associated with the torque of the hoisting machine.

[0016] Furthermore, according to the present invention, it is possible to acquire and store information that can be used to calculate the characteristic amount associated with the torque of the hoisting machine with high accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a diagram showing an example of an information system according to the first embodiment.

[0018] Figure 2 This is a diagram for explaining the function of the monitoring device.

[0019] Figure 3 It is a diagram for explaining the function of the analysis device.

[0020] Figure 4 This is a flowchart showing an example of the operation of the monitoring device.

[0021] Figure 5 It is a diagram showing an example of the first information and the second information acquired by the information acquisition unit.

[0022] Figure 6 This is a diagram showing the relationship between the position of the car and the torque value of the hoisting machine.

[0023] Figure 7 : is a figure which shows the example of the linear regression equation generated by the regression equation generation part.

[0024] Figure 8 This is a diagram for explaining the function of the regression equation generation unit.

[0025] Fig. 9 1 is a diagram showing examples of the linear regression equation for the rising state and the linear regression equation for the falling state generated by the regression equation generation unit.

[0026] Fig.10 This is a flowchart showing an example of the operation of the analysis device.

[0027] Fig.11 This is a diagram for explaining the function of the monitoring unit.

[0028] Fig.12 : is a figure which shows the example of the linear regression equation generated by the regression equation generation part.

[0029] Fig.13 2 is a diagram showing another example of a linear regression equation generated by the regression equation generation unit.

[0030] Fig.14 It is a graph showing an example of the coefficient of determination.

[0031] Fig.15 It is a diagram showing an example of the coefficient of determination based on the linear regression equation when the value is increasing and the coefficient of determination based on the linear regression equation when the value is decreasing.

[0032] Fig.16 This is a diagram showing an example of hardware resources of a monitoring device.

[0033] Fig.17 This is a diagram showing another example of the hardware resources of the monitoring device.

[0034] Description of symbols

[0035] 1: Information system; 2: Facilities; 3-4: Communication network; 5a: Head office; 5b: Branch office; 5c: Branch store; 5d: Sales office; 5e: Manufacturing plant; 10: Elevator device; 11: Car; 11a: Guide; 11b: Door; 11c: Motor; 11d: Weighing device; 12: Hoistway; 13: Counterweight; 13a: Guide; 14: Main rope; 15: Traction machine; 16-17: Guide rail; 18: Control device; 20: Monitoring device; 21: Storage unit ; 22: Information acquisition unit; 23: Regression equation generation unit; 24: Torque calculation unit; 25: Slope determination unit; 26: Regression equation generation unit; 27: Feature quantity calculation unit; 28: Communication unit; 30: Analysis device; 31: Server device; 32: Storage unit; 33: Communication unit; 34: Communication unit; 35: Monitoring unit; 36: Reproduction unit; 40: Maintenance database; 50: Specification database; 60: Processing circuit; 61: Processor; 62: Memory; 63: Dedicated hardware. DETAILED DESCRIPTION

[0036] The following is a detailed description with reference to the accompanying drawings. The repeated description is appropriately simplified or omitted. In each figure, the same reference numeral represents the same part or a corresponding part.

[0037] Implementation Method 1

[0038] Figure 1 This is a diagram showing an example of the information system 1 according to the first embodiment. Figure 1 The information system 1 shown includes an elevator device 10 , a monitoring device 20 , an analyzing device 30 , a maintenance database 40 , and a specification database 50 .

[0039] The elevator device 10 is applied to a facility 2. The facility 2 includes one or more buildings. The elevator device 10 can also be applied to a part of a building included in the facility 2. The facility 2 can be an outdoor facility or an indoor facility.

[0040] The elevator device 10 includes a car 11. The car 11 moves up and down in a hoistway 12. The hoistway 12 is a space extending up and down formed in a building of the facility 2. The hoistway 12 is formed so as to penetrate a plurality of floors of the building.

[0041] The elevator device 10 further includes a counterweight 13, a main rope 14, and a traction machine 15. The car 11 and the counterweight 13 are suspended from the hoistway 12 by means of the main rope 14. The traction machine 15 has a sheave. The main rope 14 is hung around the sheave of the traction machine 15. That is, the load of the car 11 is supported by the main rope 14 hung around the sheave of the traction machine 15. The load of the counterweight 13 is supported by the main rope 14 hung around the sheave of the traction machine 15. The main rope 14 is, for example, a strand rope. The main rope 14 may also be a belt rope.

[0042] The hoisting machine 15 is a device for driving the car 11. As an example, the hoisting machine 15 is installed in the upper part of the hoistway 12. The hoisting machine 15 may be installed in the lower part of the hoistway 12. If there is a machine room above the hoistway 12, the hoisting machine 15 may be installed in the machine room.

[0043] The hoisting machine 15 has an electric motor. The electric motor generates a torque for rotating a sheave. When the sheave of the hoisting machine 15 rotates, the main rope 14 moves. The car 11 moves up and down in the hoistway 12 according to the direction in which the sheave rotates. The movement of the car 11 is guided by a guide rail 16. The guide rail 16 is provided in the hoistway 12 over the range of movement of the car 11. The car 11 has a guide 11a. The guide 11a is a roller or a guide shoe. The guide 11a is opposite to the guide rail 16, thereby guiding the movement of the car 11.

[0044] The counterweight 13 moves up and down in the hoistway 12 in the direction opposite to the direction in which the car 11 moves. The counterweight 13 is a device for reducing the difference between the load acting on the portion of the main rope 14 extending from the sheave of the traction machine 15 to the car 11 side and the load acting on the portion extending to the opposite side of the portion. The movement of the counterweight 13 is guided by the guide rail 17. The guide rail 17 is provided in the hoistway 12 over the moving range of the counterweight 13. The counterweight 13 has a guide 13a. The guide 13a is a roller or a guide shoe. The guide 13a is opposite to the guide rail 17, thereby guiding the movement of the counterweight 13.

[0045] The elevator device 10 further includes a control device 18. As an example, the control device 18 is installed in the upper part of the hoistway 12. The control device 18 may also be installed in the lower part of the hoistway 12. If there is a machine room above the hoistway 12, the control device 18 may also be installed in the machine room.

[0046] The control device 18 controls various operations of the elevator. For example, the control device 18 controls the traction machine 15. That is, the travel of the car 11 is controlled by the control device 18. The control device 18 makes the car 11 respond to the call registered by the user, so that the user can take the car 11 to move between floors.

[0047] As another example, the control device 18 controls the equipment of the car 11. The car 11 also has a door 11b and a motor 11c. The door 11b opens and closes the entrance and exit of the car 11. The motor 11c drives the door 11b. The control device 18 controls the motor 11c. That is, the opening and closing of the door 11b is controlled by the control device 18.

[0048] The car 11 further includes a weighing device 11d. The weighing device 11d measures the load in the car 11. Information indicating the load measured by the weighing device 11d is transmitted to the control device 18.

[0049] The control device 18 holds information related to the control of the elevator. Hereinafter, this information is also referred to as control information.

[0050] As an example, the control information includes information required for controlling the equipment of the elevator device 10. The control information may also include a parameter that has a causal relationship with transient heating or cooling. The control information may also include a parameter that has a causal relationship with both transient heating and cooling. The parameter may be continuous. The parameter may also be discrete. The parameter may also be a parameter that represents any one of a plurality of options.

[0051] As another example, the control information may also include information indicating the time, information indicating the position of the car 11, information indicating the load in the car 11, and information indicating the value of the torque output by the traction machine 15. In the case where the car 11 only moves up and down, the position of the car 11 is synonymous with the height of the car 11 in the hoistway 12. The position of the car 11 may be an absolute position or a relative position. As the torque value output by the traction machine 15, a current command value for the motor of the traction machine 15 or a current feedback value of the motor may also be used.

[0052] As another example, the control information may include information indicating whether the car 11 is traveling at a constant speed. As this information, continuous value information such as a speed command value for the car 11, a speed command value for the motor of the traction machine 15, or a speed feedback value of the motor may be used. If there is a specific signal that can determine whether the car 11 is traveling at a constant speed, this signal may be used as this information.

[0053] As another example, the control information may include information indicating a position learning value of the hoistway 12 obtained by learning the positional relationship between the position where the car 11 stops at the bottom floor and the position where the car 11 stops at the top floor. The intermediate position of the hoistway 12 can be calculated based on the learning value.

[0054] As another example, the control information may also include information indicating the discontinuity of the state of the elevator. The information may include a drive command signal for the inverter, a drive command signal for the fan, a signal related to the door 11b, and a detection signal of the leveling plate. The inverter has a function of supplying a drive current to the motor of the traction machine 15. The fan has a function of supplying air to the air cooling device. The signal related to the door 11b may include a command signal for opening the door 11b, a command signal for closing the door 11b, a signal indicating that the door 11b is fully opened, and a signal indicating that the door 11b is fully closed.

[0055] As another example, the control information may also include continuous or discrete numerical information. The numerical information may include information indicating the floor where the car 11 is located, and information indicating the voltage command value for the motor of the traction machine 15. The numerical information may also include information indicating the time when the action of opening the door 11b starts, the time when the door 11b is fully opened and waiting, and the time when the action of closing the door 11b is completed. The numerical information may also include information indicating the speed command value for the door 11b, the speed feedback value of the door 11b, the current command value for the motor 11c, the current feedback value for the motor 11c, and the voltage command value for the motor 11c.

[0056] The monitoring device 20 is installed corresponding to the elevator device 10. If the information system 1 has a plurality of elevator devices, the monitoring device is installed for each elevator device. As an example, the monitoring device 20 is installed in the facility 2 to which the elevator device 10 is applied.

[0057] The monitoring device 20 has a function of acquiring information from the control device 18. The monitoring device 20 monitors the elevator device 10 based on the information acquired from the control device 18. In addition, it is preferred that the monitoring device 20 has a function of calculating a preset feature quantity based on the information acquired from the control device 18. It is preferred that the monitoring device 20 has a function of sending necessary information to the analysis device 30. The details of the functions of the monitoring device 20 will be described later.

[0058] The analysis device 30 is a device for analyzing and processing information received from the monitoring device 20. The analysis device 30 is connected to the monitoring device 20 via the communication network 3. If the information system 1 has a plurality of monitoring devices, the analysis device 30 is connected to each monitoring device via the communication network 3. As an example, the communication network 3 is a wide area communication network such as the Internet. The communication network 3 may also be a VPN (Virtual Private Network) or other secure network constructed on a public network such as the Internet.

[0059] The monitoring device 20 is, for example, an edge server having a microcomputer. On the other hand, the analysis device 30 is, for example, a computer system having one or more server devices 31. The analysis device 30 may also have three or more server devices 31. The analysis device 30 may also have a structure in which a plurality of server devices 31 having the same function are multiplexed.

[0060] As an example, part or all of the server devices 31 included in the analysis device 30 may be installed in a place far from the facility 2. The analysis device 30 may be installed in a country different from the country where the facility 2 is installed. The analysis device 30 may be installed in a region different from the region where the facility 2 is installed. When the analysis device 30 includes a plurality of server devices 31, a part of the server devices 31 may be installed in a country different from the country where the other server devices 31 are installed. A part of the server devices 31 may be installed in a region different from the region where the other server devices 31 are installed. When the analysis device 30 includes a plurality of server devices 31, the plurality of server devices 31 are connected to each other via the communication network 3.

[0061] Part or all of the functions of the analysis device 30 may be installed on a local server of a maintenance company that performs maintenance on the elevator device 10. Part or all of the functions of the analysis device 30 may be installed on a cloud server. Part or all of the functions of the analysis device 30 may also be implemented by processing and storage resources on a cloud service. The details of the functions of the analysis device 30 will be described later.

[0062] The analysis device 30 is connected to the communication network 4. As an example, the communication network 4 is an internal communication network such as an internal network or a private network of a maintenance company of the elevator device 10. When the function of the analysis device 30 is installed in a cloud server, the communication network 4 may be a VPN constructed on a public network such as the Internet.

[0063] The users who can access the communication network 4 are preset. The access rights to the communication network 4 can be granted to individual users or to roles assigned to users. The access rights to the communication network 4 can also be granted to the group to which the user belongs. Figure 1 The example in which the group includes the head office 5a, the branch office 5b, the branch store 5c, the sales office 5d, and the manufacturing office 5e of the maintenance company is shown. Other organizations such as the technical department of the maintenance company may also be included in the group.

[0064] The communication network 4 is a network capable of accessing the maintenance database 40 and the specification database 50. A part or all of the maintenance database 40 and the specification database 50 may be mounted on a local server of a maintenance company of the elevator device 10. A part or all of the maintenance database 40 and the specification database 50 may be mounted on a cloud server. A part or all of the maintenance database 40 and the specification database 50 may be mounted on the analysis device 30.

[0065] The maintenance database 40 stores maintenance information of each elevator device managed by a maintenance company. The elevator device 10 is an example of an elevator device managed by a maintenance company. The maintenance information of the elevator device 10 is also stored in the maintenance database 40. As an example, in the maintenance database 40, the identification number and the maintenance information are associated and stored for each elevator device managed by the maintenance company. The maintenance information includes information indicating the date and time of starting operation and the history of replacement parts.

[0066] The specification database 50 stores the specification information of each elevator device managed by the maintenance company. The specification information of the elevator device 10 is also stored in the specification database 50. As an example, in the specification database 50, the identification number and the specification information are associated and stored for each elevator device managed by the maintenance company. The specification information includes information indicating the control method of the elevator, the model name of the traction machine, the model name of the control device, the rated speed, the rated load, the lifting stroke, and the model name of the door. The specification information may also include information indicating the height of each floor station where the car stops.

[0067] Next, refer to Figure 2 to Figure 11 , the functions of the information system 1 are described in detail. Figure 2 2 is a diagram for explaining the function of the monitoring device 20. Figure 2 As shown, the monitoring device 20 includes a storage unit 21 (first storage unit), an information acquisition unit 22, a regression equation generation unit 23 (first regression equation generation unit), a torque calculation unit 24, a slope determination unit 25, a regression equation generation unit 26 (second regression equation generation unit), a feature quantity calculation unit 27, and a communication unit 28 (first communication unit). In addition, a part of the functions of the monitoring device 20 may be provided in a device of the elevator device 10, such as the control device 18.

[0068] Figure 3 30 is a diagram for explaining the function of the analysis device 30. Figure 3 As shown, the analysis device 30 includes a storage unit 32 (second storage unit), a communication unit 33 , a communication unit 34 (second communication unit), a monitoring unit 35 , and a reproduction unit 36 ​​.

[0069] First, the operation of the monitoring device 20 will be described. Figure 4 It is a flowchart showing an example of the operation of the monitoring device 20 .

[0070] In the monitoring device 20, the information acquisition unit 22 acquires the operation information of the elevator from the elevator device 10 (S101). As an example, the information acquisition unit 22 acquires a part or all of the control information from the control device 18 as the operation information. The information acquisition unit 22 may also acquire detailed information such as time series data as the operation information. The operation information may also include information indicating the time when the information acquisition unit 22 acquires the information from the control device 18.

[0071] For example, the information acquisition unit 22 acquires information indicating the position of the car 11 from the control device 18. Hereinafter, this information is also referred to as the first information. The information acquisition unit 22 acquires information indicating the torque value of the hoisting machine 15 from the control device 18. Hereinafter, this information is also referred to as the second information. As described above, the second information may also be information indicating a current command value for a motor provided in the hoisting machine 15 or a current feedback value of the motor.

[0072] Figure 5 2 is a diagram showing examples of the first information and the second information acquired by the information acquisition unit 22. Figure 5 In the example shown, the first information is information indicating the relationship between time and the position of the car 11. The second information is information indicating the relationship between time and the torque value of the hoisting machine 15. The time may be an absolute time or a relative time.

[0073] Next, in the monitoring device 20, a process for estimating the torque value of the hoisting machine 15 when the car 11 travels at a constant speed is performed. Specifically, in this process, it is preferred that the torque value when the car 11 is located at the middle position of the hoistway 12 is estimated. Hereinafter, this estimated value is also referred to as the middle position torque estimated value.

[0074] First, the reason for performing this processing will be described.

[0075] When the position of the car 11 is x and the load in the car 11 is L, the torque T(x, L) generated by the traction machine 15 which is the control target is expressed by equation (1).

[0076] T(x,L)=Tα(L)+Tub(L)+Tcmp(x)+Tloss…(1)

[0077] The value of the torque T(x, L) varies depending on the position x and the load L. Among them, Tα(L) is the torque generated by the acceleration and deceleration of the car 11. Tub(L) is the torque generated by the deviation between the weight of the car 11 and the weight of the counterweight 13. Hereinafter, this torque is also referred to as unbalanced torque. The value of the torque Tub(L) is determined by the load L.

[0078] Tcmp(x) is a torque generated by the difference between the weight of the compensating rope extending downward from the car 11 and the counterweight 13 on the car 11 side and the weight on the counterweight 13 side. The value of the torque Tcmp(x) is determined by the position x. In addition, regarding the torque Tcmp(x), equations (2) and (3) hold.

[0079] Tcmp(middle position)=0 …(2)

[0080] Tcmp(uppermost layer)=-Tcmp(lowermost layer) …(3)

[0081] Tloss is a torque generated by friction between the guide 11a and the guide rail 16. Hereinafter, this torque is also referred to as loss torque. Loss torque is a torque applied in the direction opposite to the direction in which the car 11 is traveling. The loss torque value when the car 11 is traveling upward is opposite to the loss torque value when the car 11 is traveling downward, but the absolute value is the same.

[0082] Furthermore, if the car 11 is traveling at a constant speed, no torque Tα(L) is generated. If the car 11 is located in the middle of the shaft 12, the torque Tcmp(x) is 0. Therefore, the torque T(x, L) generated by the hoisting machine 15 when the car 11 is traveling at a constant speed and the car 11 is located in the middle of the shaft 12 is expressed by equation (4).

[0083] T(x, L)=Tub(L)+Tloss… (4)

[0084] As shown in equation (4), the torque T(x, L) when the car 11 traveling at a constant speed is located at the middle position of the hoistway 12 is represented by the sum of the unbalanced torque Tub(L) and the loss torque Tloss. If the estimated value of the middle position torque can be obtained, equation (4) can be used.

[0085] Therefore, the regression formula generating unit 23 generates a linear regression formula (S102) of the position of the car 11 and the torque value of the traction machine 15 when the car 11 is performing the first travel at a constant speed in the hoistway 12. That is, the regression formula generating unit 23 generates a linear regression formula representing the torque value of the traction machine 15 relative to the position of the car 11 traveling at a constant speed based on the first information and the second information obtained by the information obtaining unit 22.

[0086] Figure 6 is a diagram showing the relationship between the position of the car 11 and the torque value of the hoisting machine 15. In S102, the regression equation generation unit 23 first uses Figure 5 The data showing "time vs. position of the car 11" shown in the lower section of Figure 5 The data showing "time vs torque value" shown in the upper section of Figure 6That is, the regression equation generation unit 23 generates data indicating the torque value of the hoisting machine 15 relative to the position of the car 11 during the first travel. This generation process can be realized by associating the "position of the car 11" and the "torque value" at the same time.

[0087] exist Figure 6 In the example shown, the car 11 moves from the lowest floor to the highest floor. In addition, in the interval s, the car 11 travels at a constant speed. Therefore, when generating Figure 6 When the data shown is obtained, the regression equation generation unit 23 extracts the data existing in the interval s. The regression equation generation unit 23 generates a linear regression equation of the position of the car 11 and the torque value of the hoisting machine 15 based on the extracted data.

[0088] For example, the torque T(x, L) when the car 11 travels at a constant speed is expressed by equation (5).

[0089] T(x,L)=Tub(L)+Tcmp(x)+Tloss…(5)

[0090] If the load L in the car 11 is fixed, the equation (5) can be approximately expressed as a linear function of the position x. Thus, the equation (6) can be obtained.

[0091] T(x,L)=Kcmp*(x-TR / 2)+Tub(L)+Tloss…(6)

[0092] In addition, formula (6) can also be rewritten as formula (6-1).

[0093] T(x,L)=Kcmp*x+Toffset_1(L)…(6-1)

[0094] Among them, Toffset_1(L) can be expressed by formula (6-2), which represents the intercept of the above-mentioned linear regression equation.

[0095] Toffset_1(L)=-Kcmp*TR / 2+Tub(L)+Tloss…(6-2)

[0096] Here, TR is the lifting stroke of the car 11. That is, if the car 11 is at the bottom floor, the position x = 0, if the car 11 is at the middle position, the position x = TR / 2, and if the car 11 is at the top floor, the position x = TR. In addition, Kcmp is the coefficient of the torque component that changes according to the position x. That is, Tcmp(x) = Kcmp*(x-TR / 2).

[0097] In this way, the regression equation generator 23 extracts only the data when the car 11 travels at a constant speed, and obtains the relationship between the torque value of the hoisting machine 15 and the position of the car 11 using a linear regression equation that is an approximation of a linear function. Figure 7 : is a diagram showing an example of a linear regression equation generated by the regression equation generation unit 23. The regression equation generation unit 23 may generate a linear regression equation based on the extracted data by any method. For example, the regression equation generation unit 23 may generate a linear regression equation by using the least squares method.

[0098] In addition, the determination of whether the car 11 is traveling at a constant speed can be performed using a traveling speed command value for the car 11 or the like.

[0099] Next, the torque calculation unit 24 calculates the torque value of the traction machine 15 when the car 11 is located at the middle position of the hoistway 12 in the first travel, that is, the intermediate position torque estimated value, based on the linear regression equation generated by the regression equation generation unit 23 in S102 (S103). Specifically, the torque calculation unit 24 calculates the intermediate position torque estimated value by substituting x=TR / 2 into the linear regression equation generated by the regression equation generation unit 23. The equation for calculating the intermediate position torque estimated value can be expressed by equation (7) obtained by substituting x=TR / 2 into equation (6).

[0100] T(TR / 2,L)=Tub(L)+Tloss…(7)

[0101] When the intermediate position torque estimate is calculated by the torque calculation unit 24 in S103, information indicating the intermediate position torque estimate is stored in the storage unit 21 (S104). Hereinafter, this information is also referred to as the fifth information. In addition, in S101, the information acquisition unit 22 acquires information indicating the travel direction of the car 11 during the first travel from the control device 18. Hereinafter, this information is also referred to as the third information. In S101, the information acquisition unit 22 acquires information indicating the load in the car 11 during the first travel from the control device 18. Hereinafter, this information is also referred to as the fourth information. In S104, the third information, the fourth information, and the fifth information are associated as a group of status information and stored in the storage unit 21. Table 1 shows an example of status information stored in the storage unit 21.

[0102] [Table 1]

[0103]

[0104] In the present embodiment, the travel of the car 11 from the departure floor to the destination floor is defined as one travel. In addition, the first travel is performed in one travel. When the processing shown in S101 to S103 is performed on one travel, one set of state information is stored in the storage unit 21 in S104. That is, one row is added to Table 1. By performing one travel in which the car 11 travels at a constant speed in a part of the hoistway 12 multiple times, multiple sets of state information are stored in the storage unit 21.

[0105] In addition, the detection of the end of one trip can also be performed based on the drive command signal for the inverter, etc. Regarding the trip of the car 11 without passing through the middle position of the hoistway 12, the processing shown in S101 to S103 can also be performed, and a set of state information is stored in the storage unit 21 in S104.

[0106] Table 1 shows an example in which the state information includes information indicating a time (timestamp) in addition to the third information, the fourth information, and the fifth information. The time may be any time as long as it is a predefined time. For example, the time may be the time when the driving starts. The time may also be the time when the driving ends.

[0107] Table 1 shows an example in which the travel direction of the car 11 is indicated by "up" or "down". As another example, the travel direction of the car 11 may be indicated by a numerical value including positive and negative values. Table 1 shows an example in which the load in the car 11 is indicated by a ratio [%] to the rated load capacity of the car 11. As another example, the load in the car 11 may be indicated by mass [kg].

[0108] Table 1 shows an example of information including a coefficient indicating a torque component that varies according to the position of the car 11 in the state information. The coefficient is the slope of the linear regression equation generated by the regression equation generation unit 23. When the linear regression equation is generated by the regression equation generation unit 23, the slope determination unit 25 determines the slope. The torque calculation unit 24 may also calculate the torque component that varies according to the position of the car 11 based on the slope determined by the slope determination unit 25. Table 1 shows information including a coefficient indicating a determination coefficient R based on the linear regression equation generated by the regression equation generation unit 23 in the state information. 2 This determination coefficient will be described later.

[0109] Next, in the monitoring device 20, processing is performed to extract a feature quantity associated with the travel of the car 11, more specifically, a feature quantity associated with the torque of the hoisting machine 15.

[0110] In this process, first, the regression equation generation unit 26 generates a linear regression equation for the ascent when the car 11 travels upward at a constant speed in the hoistway 12 (S105). In S105, the regression equation generation unit 26 generates a linear regression equation for the ascent of the load in the car 11 and the torque value of the hoisting machine 15 when the car 11 is located at the middle position of the hoistway 12. The linear regression equation for the ascent is an approximate equation representing a linear function of the torque value of the hoisting machine 15 relative to the load in the car 11. The generation of the linear regression equation for the ascent by the regression equation generation unit 26 is performed based on multiple sets of state information stored in the storage unit 21.

[0111] In addition, the regression equation generation unit 26 generates a linear regression equation for descent when the car 11 travels downward at a constant speed in the hoistway 12 (S106). In S106, the regression equation generation unit 26 generates a linear regression equation for descent of the load in the car 11 and the torque value of the hoisting machine 15 when the car 11 is located at the middle position of the hoistway 12. The linear regression equation for descent is an approximate equation representing a linear function of the torque value of the hoisting machine 15 relative to the load in the car 11. The generation of the linear regression equation for descent by the regression equation generation unit 26 is performed based on multiple sets of state information stored in the storage unit 21.

[0112] Figure 8 It is a diagram for explaining the function of the regression equation generation unit 26 . Figure 8 The horizontal axis represents the load factor in the car 11 . Figure 8 The vertical axis represents the intermediate position torque estimation value. Figure 8 The graph shows a graph in which one dot is plotted for a set of status information stored in the storage unit 21 .

[0113] Here, the above-mentioned formula (7) is considered. Since the unbalanced torque Tub (L) changes in proportion to the load L, the formula (7) can be expressed as the following formula (8).

[0114] T(TR / 2,L)=Kub*(L-Lbl)+Tloss…(8)

[0115] In the formula (8), Lbl is the load in the car 11 when a balanced load is placed. Kub is a coefficient of a torque component that varies according to the load L in the car 11.

[0116] When the car 11 is traveling upward, the loss torque Tloss acts downward. Therefore, the torque of the hoisting machine 15 for compensating the loss torque Tloss is output to the positive side. On the other hand, when the car 11 is traveling downward, the loss torque Tloss acts upward. Therefore, the torque of the hoisting machine 15 for compensating the loss torque Tloss is output to the negative side. Therefore, when the car 11 is traveling upward, equation (8) can be expressed as equation (9). Similarly, when the car 11 is traveling downward, equation (8) can be expressed as equation (10).

[0117] Tup_mid=Kubup*(L-Lbl)+Tloss…(9)

[0118] Tdn_mid=Kubdn*(L-Lbl)-Tloss…(10)

[0119] Among them, Tup_mid represents the estimated value of the torque at the middle position when the car 11 is moving upward. Tdn_mid represents the estimated value of the torque at the middle position when the car 11 is moving downward. Kubup is the coefficient of the torque component that changes according to the load L in the car 11 when the car 11 is moving upward. Kubdn is the coefficient of the torque component that changes according to the load L in the car 11 when the car 11 is moving downward.

[0120] Formula (9) represents the linear regression formula when rising. Formula (10) represents the linear regression formula when falling. In addition, Fig. 9 2 is a diagram showing an example of a linear regression equation for the rising state and a linear regression equation for the falling state generated by the regression equation generating unit 26. Fig. 9 As shown, the slope of the linear regression equation when rising, i.e., the coefficient Kubup, and the slope of the linear regression equation when falling, i.e., the coefficient Kubdn, represent substantially the same value. The method by which the regression equation generation unit 26 generates the linear regression equation when rising and the linear regression equation when falling may be any method. For example, the regression equation generation unit 26 may generate the linear regression equation when rising and the linear regression equation when falling by using the least square method.

[0121] As long as the number of sets of status information required for generating the approximate formula is stored in the storage unit 21, the linear regression formula for ascending and the linear regression formula for descending can be generated. For example, the linear regression formula for ascending and the linear regression formula for descending can be generated based on the status information for one day. The linear regression formula for ascending and the linear regression formula for descending can also be generated based on the status information for one week or one month. The generation of the linear regression formula for ascending and the linear regression formula for descending can be performed every time the status information of a predetermined number of sets is stored in the storage unit 21, or can be performed periodically. In the case where the information system 1 has a plurality of elevator devices, the conditions for generating the linear regression formula for ascending and the linear regression formula for descending can be arbitrarily set for each elevator device.

[0122] Next, the characteristic quantity calculation unit 27 calculates the characteristic quantity associated with the torque of the traction machine 15 based on the linear regression equation for the ascending state and the linear regression equation for the descending state generated by the regression equation generation unit 26 (S107). Hereinafter, an example in which the characteristic quantity calculation unit 27 calculates the loss torque value, the unbalanced torque value, and the estimated value of the balanced load in the car as the characteristic quantity will be described in detail. The characteristic quantity calculation unit 27 may also calculate only the loss torque value, only the unbalanced torque value, or only the estimated value of the balanced load in the car as the characteristic quantity. The characteristic quantity calculation unit 27 may also calculate other characteristic quantities. The characteristic quantity to be calculated by the characteristic quantity calculation unit 27 is preset.

[0123] The following specifically describes the feature value calculation function.

[0124] If equation (9) is transformed, equations (11) and (12) can be obtained.

[0125] Tup_mid=Kubup*L+Toffset_up_mid…(11)

[0126] Toffset_up_mid=Tloss-Kubup*Lbl…(12)

[0127] Among them, Toffset_up_mid represents the intercept of the first regression equation when rising.

[0128] If equation (10) is transformed, equations (13) and (14) can be obtained.

[0129] Tdn_mid=Kubdn*L+Toffset_dn_mid…(13)

[0130] Toffset_dn_mid=-Tloss-Kubdn*Lbl…(14)

[0131] Among them, Toffset_dn_mid represents the intercept of the first-order regression equation when descending.

[0132] Regarding (12) and (14), Tloss>0, so (15) holds.

[0133] Toffset_up_mid>Toffset_dn_mid…(15)

[0134] From the difference between equation (12) and equation (14), we can obtain equation (16).

[0135] (Toffset_up_mid-Toffset_dn_mid) / 2=Tloss-(Kubup-Kubdn)*Lbl / 2…(16)

[0136] According to the sum of equation (12) and equation (14), equation (17) can be obtained.

[0137] (Toffset_up_mid+Toffset_dn_mid) / 2=-Lbl*(Kubup+Kubdn) / 2…(17)

[0138] Here, -Lbl represents the load in the car 11 when there is no load in the car 11.

[0139] In addition, regarding formula (17), define formula (18).

[0140] Kub_ave=(Kubup+Kubdn) / 2…(18)

[0141] Here, Kub_ave is an average value of the coefficient of the torque component that varies according to the load L in the car 11. Specifically, Kub_ave represents an average value of the coefficient during constant speed ascending and the coefficient during constant speed descending.

[0142] By transforming equation (16), we can obtain equation (19).

[0143] Tloss=(Toffset_up_mid-Toffset_dn_mid) / 2+(Kubup-Kubdn)*Lbl / 2…(19)

[0144] If the coefficient Kubup during constant speed increase and the coefficient Kubdn during constant speed decrease are substantially the same, equation (19) can be approximated as equation (20).

[0145] Tloss≒(Toffset_up_mid-Toffset_dn_mid) / 2…(20)

[0146] Formula (19) means that the calculation of the loss torque Tloss requires the slope and intercept of the linear regression equation when rising and the slope and intercept of the linear regression equation when falling. That is, the characteristic quantity calculation unit 27 can calculate the value of the loss torque Tloss using information representing the slope and intercept of the linear regression equation when rising and information representing the slope and intercept of the linear regression equation when falling.

[0147] Formula (20) means that the intercept of the linear regression equation when rising and the intercept of the linear regression equation when falling are required in the approximate calculation of the loss torque Tloss. That is, the characteristic quantity calculation unit 27 can use the information representing the intercept of the linear regression equation when rising and the information representing the intercept of the linear regression equation when falling to approximately calculate the value of the loss torque Tloss. When the characteristic quantity calculation unit 27 calculates the loss torque Tloss according to formula (20), it is not necessary to use the information representing the slope of the linear regression equation when rising and the information representing the slope of the linear regression equation when falling. In addition, even when the torque Tloss is calculated according to formula (20), a calculation accuracy sufficient for practical use can be obtained.

[0148] In addition, by transforming equation (17), equation (21) can be obtained.

[0149] (Toffset_up_mid+Toffset_dn_mid) / 2=-Lbl*Kub_ave…(21)

[0150] Formula (21) means that the intercept of the linear regression equation when rising and the intercept of the linear regression equation when falling are required to calculate the unbalanced torque when there is no load in the car 11. That is, the characteristic quantity calculation unit 27 can calculate the value of the unbalanced torque using information representing the intercept of the linear regression equation when rising and information representing the intercept of the linear regression equation when falling.

[0151] Next, using equations (11) and (13), the conditions of the load L are studied in which the absolute value of the intermediate position torque estimate value when the car 11 is traveling upward is the same as the absolute value of the intermediate position torque estimate value when the car 11 is traveling downward. That is, the load condition L' of Tup_mid+Tdn_mid=0 is considered.

[0152] When equation (11) and equation (13) are added together and rearranged under the above conditions, equation (22) can be obtained.

[0153] L′=-(Toffset_up_mid+Toffset_dn_mid) / (Kubup+Kubdn)…(22)

[0154] L′ is ideally consistent with the load Lbl in the car 11 when it is carrying a balanced load. Therefore, L′ is also called the estimated value of the balanced load in the car. When the balance state of the car 11 and the counterweight 13 changes, the estimated value of the balanced load in the car L′ is no longer consistent with the load Lbl. In addition, when the measurement error, that is, the output deviation, of the weighing device 11d becomes larger, the estimated value of the balanced load in the car L′ is no longer consistent with the load Lbl. Therefore, based on the value of the estimated value of the balanced load in the car L′, it is possible to confirm the change in the above-mentioned balance state or the output deviation of the weighing device 11d.

[0155] Formula (22) means that the slope and intercept of the linear regression equation when rising and the slope and intercept of the linear regression equation when falling are required in the calculation of the estimated value L' of the balanced load in the car. That is, the characteristic quantity calculation unit 27 can calculate the estimated value L' of the balanced load in the car using the information representing the slope and intercept of the linear regression equation when rising and the information representing the slope and intercept of the linear regression equation when falling.

[0156] Next, in the monitoring device 20 , a process for transmitting the calculated feature amount to the analyzing device 30 is performed.

[0157] In this process, first, the communication unit 28 determines whether the communication load of the communication network 3 is high (S108). The criterion for determining that the load is high in S108 is preset. When the communication unit 28 determines that the communication load of the communication network 3 is not high ("No" in S108), the communication unit 28 sends information indicating the characteristic quantity calculated by the characteristic quantity calculation unit 27 in S107 to the analysis device 30 (S109). When the communication unit 28 determines that the communication load of the communication network 3 is high ("Yes" in S108), the communication unit 28 does not send information indicating the characteristic quantity to the analysis device 30.

[0158] In S108, it is also possible to determine whether the processing load of the analysis device 30 is high. The criterion for determining whether the processing load of the analysis device 30 is high is preset. In this case, the communication unit 28 may also inquire the analysis device 30 about the state of the processing load. The communication unit 28 determines whether the processing load of the analysis device 30 is high based on the response from the analysis device 30. When the communication unit 28 determines that the processing load of the analysis device 30 is not high ("No" in S108), the communication unit 28 sends information indicating the feature quantity calculated by the feature quantity calculation unit 27 in S107 to the analysis device 30 (S109). When the communication unit 28 determines that the processing load of the analysis device 30 is high ("Yes" in S108), the communication unit 28 does not send information indicating the feature quantity to the analysis device 30.

[0159] Furthermore, the communication unit 28 may transmit information during a time period when the elevator device 10 is less used, such as at night. The communication unit 28 may transmit information according to the schedule management of the analysis device 30. According to this example, it is possible to suppress the concentration of the processing load on the analysis device 30. The communication unit 28 may transmit information by receiving a regular or irregular polling trigger from the analysis device 30. After the communication unit 28 starts transmitting information, when it is detected that the communication load of the communication network 3 becomes high, the communication unit 28 may temporarily stop transmitting information. In this example, the monitoring of the communication load may be performed by the monitoring device 20 or by the analysis device 30.

[0160] Next, the operation of the analyzing device 30 will be described. Fig.10 It is a flowchart showing an operation example of the analysis device 30 .

[0161] In the analysis device 30, the communication unit 33 determines whether information indicating the feature quantity is received from the monitoring device 20 via the communication network 3 (S201). When the communication unit 33 receives the information indicating the feature quantity transmitted by the communication unit 28 in S109, it determines "yes" in S201.

[0162] If the determination in S201 is "yes", the information indicating the feature quantity received by the communication unit 33 in S201, i.e., the information indicating the feature quantity calculated by the feature quantity calculation unit 27, is associated with other information and stored in the storage unit 32 as monitored information (S202). Table 2 shows an example of the monitored information stored in the storage unit 32.

[0163] [Table 2]

[0164]

[0165] Table 2 shows a preferred example in which the monitored information is stored in the storage unit 32 in time series. In the example shown in Table 2, one piece of monitored information is stored in the storage unit 32 every month. That is, one row is added to Table 2 every month. The frequency of adding new monitored information to the storage unit 32 is not limited to the frequency shown in Table 2. In addition, since the amount of monitored information is not large, the monitored information during the entire operation period may be stored in the storage unit 32 for each elevator device.

[0166] In the example shown in Table 2, in addition to the information indicating the characteristic quantity described above, the monitored information also includes the number of groups (number of samples) of state information used to calculate the characteristic quantity and information indicating the period for obtaining the state information. In addition, the monitored information includes elevator information indicating the number of times the elevator device 10 is started, the cumulative number of starts, the cumulative travel distance, and the cumulative start time during the period, and environmental information indicating the average temperature and average humidity during the period. In addition, Table 2 shows an example in which the monitored information includes information indicating the determination coefficient based on the linear regression equation generated by the regression equation generation unit 26 during the ascending period and information indicating the determination coefficient based on the linear regression equation during the descending period. The determination coefficient will be described later.

[0167] The elevator information and the environmental information may also be obtained from the maintenance database 40 and the specification database 50. In this case, the communication unit 34 receives the required elevator information and environmental information from the maintenance database 40 and the specification database 50 via the communication network 4. In S202, the information indicating the feature quantity received by the communication unit 33 is associated with the elevator information and the environmental information received by the communication unit 34 and stored in the storage unit 32.

[0168] The communication unit 34 may also receive other maintenance information from the maintenance database 40. The communication unit 34 may also receive other specification information from the specification database 50. The monitored information may also include other maintenance information and other specification information received by the communication unit 34. In this case, the monitored information may also be classified according to each specific specification or each specific maintenance history record of the elevator and stored in the storage unit 32.

[0169] The monitoring unit 35 monitors the change of the feature quantity calculated by the feature quantity calculation unit 27 based on the monitored information stored in the storage unit 32 (S203). As an example, the monitoring unit 35 monitors the time change or seasonal change of the feature quantity stored in the storage unit 32.

[0170] The monitoring by the monitoring unit 35 may be performed for each elevator device or for each similar elevator device. For example, elevator devices of the same model may be regarded as similar elevator devices. As another example, elevator devices of the same rated load capacity, elevator devices of the same rated speed, elevator devices of the same traction machine model, and elevator devices of the same control device model may also be regarded as similar elevator devices. When the monitoring by the monitoring unit 35 is performed for each similar elevator device, comparison of average components or comparison of fluctuation amplitude components may also be performed.

[0171] Fig.11 This is a diagram for explaining the function of the monitoring unit 35 . Fig.11 The vertical axis of represents the loss torque value as an example of the characteristic quantity calculated by the characteristic quantity calculation unit 27. Fig.11 As shown, the loss torque value varies according to the season. The monitoring unit 35 may also determine whether the loss torque value stored in the storage unit 32 is within the allowable range. It is preferable to pre-set the upper limit and lower limit of the allowable range for determining whether the loss torque value is appropriate for each elevator device.

[0172] The monitoring result of the monitoring unit 35 can also be reflected in the maintenance plan of the elevator device 10. The monitoring result includes, for example, information indicating whether the loss torque value is within the allowable range. In a simple example, the monitoring result of the monitoring unit 35 is stored in the storage unit 32. In this case, for example, the maintenance personnel of the elevator device 10 observes the monitoring result stored in the storage unit 32 from the maintenance terminal via the communication network 4, thereby reflecting the monitoring result in the maintenance plan.

[0173] As another example, information for reflecting the monitoring result of the monitoring unit 35 in the maintenance plan of the elevator device 10 may be transmitted from the communication unit 34 to the maintenance database 40. In this case, the monitoring result of the monitoring unit 35 is written into the maintenance information of the elevator device 10 stored in the maintenance database 40.

[0174] For example, in the elevator device 10, lubricating oil is used to reduce friction between the guide 11a and the guide rail 16. If the lubricating oil is insufficient, the loss torque value is considered to be out of the allowable range. Maintenance personnel who have confirmed such a phenomenon can replenish the lubricating oil before it runs out.

[0175] In addition, the same function as the monitoring function of the monitoring unit 35, for example, a function for determining whether the loss torque value is within the allowable range, may be used in the determination of recovery after an earthquake. For example, when an earthquake occurs, in the elevator device 10, a control operation is performed to stop the car 11 at the nearest floor to rescue the user. In addition, in the elevator device 10, a diagnostic operation is performed after the control operation, and if no abnormality is detected in the diagnostic operation, automatic recovery is performed under certain conditions. At this time, as one of the conditions for recovery, it may be determined whether the loss torque value calculated by the feature quantity calculation unit 27 is within the allowable range.

[0176] In addition, various determinations related to maintenance may be performed based on the monitoring results of the monitoring unit 35. Hereinafter, this determination is also referred to as maintenance determination. The maintenance determination includes inspection cycle determination of the elevator device 10, inspection content change determination, maintenance cycle determination, maintenance content change determination, and equipment replacement determination.

[0177] The maintenance determination may also be performed by the analysis device 30. In this case, the analysis device 30 performs the maintenance determination based on a preset reference. The analysis device 30 may also perform the maintenance determination by relatively comparing the monitoring results of a plurality of elevator devices. As another example, the maintenance determination may also be performed by a user who can access the analysis device 30 via the communication network 4. The user also includes the maintenance personnel of the elevator device 10. By performing such a maintenance determination, it becomes easy to organize matters that should be dealt with in advance, such as the confirmation of the inspection cycle, the inspection content, the maintenance cycle and the maintenance content, the arrangement of the replacement of the equipment, and the adjustment of the schedule of the replacement project.

[0178] In addition, the monitoring results of the monitoring unit 35 can also be reflected in the renovation plan of the elevator device 10. For example, the current status of the elevator device 10 can be grasped based on the monitoring results stored in the storage unit 32, and the appropriateness of the renovation period can be determined. In addition, when the elevator device 10 is actually renovated, the determination of the equipment that needs to be replaced and the selection of the equipment to be used can also be determined based on the monitoring results of the monitoring unit 35.

[0179] In the example shown in the present embodiment, in the monitoring device 20, the information acquisition unit 22 acquires the first information and the second information, and the regression equation generation unit 23 generates a linear regression equation. By using this linear regression equation, the intermediate position torque estimate can be calculated with high accuracy. By using the calculated intermediate position torque estimate, various characteristic quantities associated with the torque of the hoisting machine 15 can be calculated. In addition, in order to generate a linear regression equation, the first information and the second information when the car 11 is traveling at a constant speed are required, but the car 11 frequently travels at the rated speed during normal operation. In addition, when the first information and the second information are obtained, there is no condition for the load in the car 11. Therefore, according to the example shown in the present embodiment, the conditions for obtaining information that can be used when calculating the characteristic quantities associated with the torque of the hoisting machine 15 can be relaxed.

[0180] Furthermore, since the information can be acquired during normal operation, the information can be acquired periodically and continuously. In addition, it is not necessary to run the car 11 only to acquire the information.

[0181] In the example shown in the present embodiment, the torque value of the traction machine 15, that is, the intermediate position torque estimated value is calculated based on the linear regression equation generated by the regression equation generation unit 23. Therefore, for example, even if a value caused by interference is superimposed on the torque value of the traction machine 15 at the moment when the car 11 actually passes through the intermediate position of the hoistway 12, the influence of the value caused by the interference can be suppressed. That is, according to the example shown in the present embodiment, the intermediate position torque estimated value can be calculated with high accuracy.

[0182] In the example shown in the present embodiment, the fifth information indicating the estimated value of the intermediate position torque calculated by the torque calculation unit 24 is associated with the third information and the fourth information as a set of state information and stored in the storage unit 21. By storing a plurality of sets of state information in the storage unit 21, the regression equation generation unit 26 can generate a linear regression equation for the rise and a linear regression equation for the fall. In addition, when the linear regression equation for the rise and the linear regression equation for the fall are generated by the regression equation generation unit 26, the feature quantity calculation unit 27 can calculate the feature quantity. Therefore, by storing a plurality of sets of state information in the storage unit 21, information that can be used when calculating the feature quantity associated with the torque of the traction machine with high accuracy can be obtained and stored. In addition, the information can be provided.

[0183] In addition, the present embodiment shows a preferred example of using information indicating the estimated value of the intermediate position torque calculated by the torque calculation unit 24 as the fifth information. Information indicating the torque value of the hoisting machine 15 obtained by other methods may also be used as the fifth information. In this case, the fifth information is also information indicating the torque value when the car 11 traveling at a constant speed is located at the middle position of the hoistway 12. Even in such an example, by storing multiple sets of state information in the storage unit 32, it is possible to obtain and store information that can be used when calculating the characteristic quantity associated with the torque of the hoisting machine with high accuracy.

[0184] In the example shown in this embodiment, a linear regression equation for the rising state and a linear regression equation for the falling state are generated using a plurality of sets of state information. By generating two linear regression equations, various feature quantities can be calculated using information indicating their slopes and information indicating their intercepts.

[0185] In the present embodiment, a description has been given of a preferred example in which the feature amount calculated by the feature amount calculation unit 27 is used to monitor the elevator apparatus 10. The feature amount may also be used in other methods.

[0186] As an example, the characteristic quantity calculated by the characteristic quantity calculation unit 27 can also be used in the control device 18 of the elevator device 10. In this case, for example, the communication unit 28 sends information indicating the characteristic quantity calculated by the characteristic quantity calculation unit 27 to the control device 18. In the control device 18, the information is used as a control parameter for controlling the traction machine 15. For example, the information may include a loss torque value, a torque component value that varies according to the position of the car 11, and an unbalanced torque value. For example, the information is reflected in the feedforward term of the speed controller of the control device 18. Thereby, the speed tracking performance of the traction machine 15 can be improved. In addition, the torque component that varies according to the position of the car 11 will be described later.

[0187] As an example of using the characteristic quantity in the control device 18, the loss torque value sent from the communication unit 28 can also be used in the control device 18 to calculate the inertia moment component as one of the control parameters. As a result, the calculation accuracy of the inertia moment component can be improved, and the speed following performance of the traction machine 15 can be improved. In addition, the loss torque value can also be used in the control device 18 to determine the acceleration and maximum speed as control parameters. That is, if the loss torque value is small, the acceleration and maximum speed can be set to larger values. As a result, the driving range of the traction machine 15 can be expanded.

[0188] In addition, as an example of using the characteristic quantity in the control device 18, the estimated value of the balanced load in the car sent from the communication unit 28 may be used in the control device 18 to correct the output value of the weighing device 11d. This can reduce the starting shock after the brake is released and improve the ride quality of the car.

[0189] As another example, the feature quantity calculated by the feature quantity calculation unit 27 may also be used to reproduce the travel phenomenon of the car 11 in cyberspace. The reproduction unit 36 ​​has a function of reproducing the travel phenomenon of the car 11 in cyberspace using the feature quantity calculated by the feature quantity calculation unit 27. As an example, the reproduction unit 36 ​​may also have the same function as the "reproduction unit 27" disclosed in International Application No. PCT / JP2023 / 013958.

[0190] For example, there is a high possibility that the loss torque value differs from the theoretically calculated value and the actually measured value. This is because the loss torque value varies depending on the season or the maintenance status. Therefore, the reproduction unit 36 ​​can improve the accuracy of the reproduction by reproducing the running phenomenon of the car 11 using the loss torque value calculated by the feature value calculation unit 27.

[0191] When the unbalanced torque value when there is no load in the car 11 is known, the unbalanced torque when the car 11 is traveling can be known more accurately. In addition, if the information related to the torque output by the traction machine 15 uses the information related to the current of the motor of the traction machine 15, the relationship between the torque value and the current value can be known. There is a variable element that changes according to the temperature in the relationship between the torque and the current of the traction machine 15. Therefore, the reproduction unit 36 ​​can improve the accuracy of the reproduction by reproducing the traveling phenomenon of the car 11 by using the relationship between the unbalanced torque value and the current value calculated by the feature quantity calculation unit 27.

[0192] In addition, when the estimated value of the balanced load in the car is known, the state of balance with the counterweight 13 or the state of the output deviation of the weighing device 11d is known. In the case of adopting a reproduction method in which the supply current to the traction machine 15 is determined based on the output value of the weighing device 11d when reproducing the running phenomenon, when the estimated value of the balanced load in the car is known, the increase or decrease of the supply current can be corrected based on this value. Therefore, the reproduction unit 36 ​​can improve the accuracy of the reproduction by reproducing the running phenomenon of the car 11 using the estimated value of the balanced load in the car calculated by the feature quantity calculation unit 27.

[0193] Information indicating the reproduction result of the reproduction unit 36 ​​and information indicating the analysis result obtained based on the reproduction result may be transmitted from the communication unit 34 to the maintenance database 40. In this case, the reproduction result of the reproduction unit 36 ​​and the analysis result obtained based on the reproduction result are written to the maintenance information of the elevator device 10 stored in the maintenance database 40.

[0194] In the example shown in the present embodiment, the analysis device 30 may also grant specific permissions to users who can access the communication network 4. The permissions include, for example, a reading access permission for information stored in the storage unit 32 and an update access permission for the monitoring model used by the monitoring unit 35. The permissions may also include a reading permission and an access permission for the reproduction results and analysis results related to the reproduction unit 36. The granting of the permissions by the analysis device 30 may also be set for each user, each group, or each role. The analysis device 30 may cancel the granting of permissions according to the situation, or may change the type of permissions granted. For example, the analysis device 30 may also grant a reading access permission for information stored in the storage unit 32 and an update access permission for the monitoring model to a user of the production office 5e. The analysis device 30 may also grant only a reading access permission for information stored in the storage unit 32 to a user of the business office 5d.

[0195] In this embodiment, a preferred example is described in which the monitoring device 20 includes the feature quantity calculation unit 27. That is, in the example shown in this embodiment, the feature quantity is calculated in the monitoring device 20. As another example, the feature quantity may be calculated in the analysis device 30.

[0196] In this case, the communication unit 28 of the monitoring device 20 transmits information required for calculating the feature quantity to the analysis device 30 via the communication network 3. In addition, the analysis device 30 includes a calculation unit having the same function as that of the feature quantity calculation unit 27. When the communication unit 33 receives the above information transmitted from the communication unit 28, the calculation unit calculates the feature quantity based on the information received by the communication unit 33. In S202, the information indicating the feature quantity calculated by the calculation unit is associated with other information and stored in the storage unit 32 as monitored information.

[0197] In this example, when the characteristic amount is used in the control device 18 of the elevator device 10, information indicating the characteristic amount calculated by the calculation unit of the analysis device 30 is transmitted from the communication unit 33 to the elevator device 10 via the communication network 3. In this case, the information indicating the characteristic amount may be transmitted to the elevator device 10 via the monitoring device 20.

[0198] In addition, the above example is a preferred example in which the characteristic quantity associated with the torque of the traction machine 15 can be calculated in the information system 1. However, it is not necessary for the information system 1 to have a function to calculate the characteristic quantity. The information system 1 may not have a function to calculate the characteristic quantity as long as the conditions for obtaining information that can be used to calculate the characteristic quantity are relaxed. For example, the calculation of the characteristic quantity may also be performed in other systems or devices.

[0199] In addition, in the above content, an example of generating a linear regression equation for the rising time and a linear regression equation for the falling time by acquiring the first information to the fifth information and calculating the characteristic quantity such as the loss torque using the linear regression equation for the rising time and the linear regression equation for the falling time is described in detail. As another example, a linear regression equation such as Figure 7 The linear regression formula shown is used to calculate other characteristic quantities related to the torque of the hoisting machine 15.

[0200] Furthermore, when acquiring the first to fifth information, as described above, the load in the car 11 during travel can be acquired without conditions, for example, during normal operation. Therefore, in the above example and the following example, the acquisition conditions of the information required for calculating the characteristic quantity associated with the torque of the traction machine 15 can be relaxed.

[0201] For example, in the example shown in the present embodiment, the slope of the linear regression equation generated by the regression equation generating unit 23 is determined by the slope determining unit 25. Based on the slope, the torque component (e.g., variation ratio) of the torque value of the hoisting machine 15 that varies according to the position of the car 11 can be calculated, and therefore, the slope can be regarded as one of the important characteristic quantities.

[0202] As an example, the torque component that changes according to the position of the car 11 is calculated based on the slope determined by the slope determination unit 25. By monitoring the change of the calculated torque component by the monitoring unit 35, it is also possible to determine whether there is an obstacle in the middle of the hoistway 12. This determination can also be reflected in the maintenance plan.

[0203] In addition, the calculated torque component is correlated with the mass component of the main rope 14 alone. That is, when the mass of the main rope 14 alone changes, the calculated torque component, that is, the slope of the linear regression generated by the regression generation unit 23 changes. Therefore, the slope can also be stored in the storage unit 32 as monitored information, and the slope can be used as one of the monitoring objects of the monitoring unit 35. The monitoring unit 35 can also determine whether the value of the slope is within a preset allowable range. In addition, the monitoring unit 35 can monitor the temporal change of the slope and reflect the monitoring result in the maintenance plan of the elevator device 10, more specifically, in the maintenance plan of the main rope 14.

[0204] As another example, the calculated torque component can also be used in the control device 18 of the elevator device 10. In this case, for example, the communication unit 28 sends information indicating the slope determined by the slope determination unit 25 to the control device 18. In the control device 18, the information is used as a control parameter for controlling the traction machine 15. For example, the torque component that changes according to the position of the car 11 is calculated based on the slope determined by the slope determination unit 25. The calculated torque component is reflected in the feedforward term of the speed controller included in the control device 18. As a result, the speed tracking performance of the traction machine 15 can be improved.

[0205] As another example, the calculated torque component can also be used to reproduce the travel of the car 11 in the network space. In this case, the reproduction unit 36 ​​reproduces the travel phenomenon of the car 11 in the network space using the calculated torque component. As a result, the accuracy of estimating the current value of the traction machine 15 can be improved, and the accuracy of reproducing the travel phenomenon of the car 11 can be improved.

[0206] In addition, the characteristic quantity calculation unit 27 may calculate a value related to the residual based on the linear regression equation generated by the regression equation generation unit 23 as a characteristic quantity associated with the torque of the hoisting machine 15. That is, the characteristic quantity calculation unit 27 calculates a value related to the residual between the torque value (observed value) of the hoisting machine 15 obtained by the information acquisition unit 22 from the control device 18 and the value (predicted value) obtained based on the linear regression equation as the characteristic quantity.

[0207] As an example, the value associated with the residual is the coefficient of determination R 2 Based on the coefficient of determination R of the linear regression equation generated by the regression equation generation unit 23 2 _1(L) is expressed by formula (23).

[0208] R 2 _1(L) = 1-residual sum of squares / total sum of squares (23)

[0209] The residual sum of squares is the sum of the square values ​​of the residuals between the observed value and the predicted value. The total sum of squares is the sum of the square values ​​of the difference between the observed value and the average value of the entire observed value. As another example, the feature value calculation unit 27 can also calculate the root mean square error (RMSE: Root Mean Squared Error), the mean square error (MSE: Mean Squared Error) or the mean absolute error (MAE: Mean Absolute Error) as a value related to the residual.

[0210] Coefficient of determination R 2 It is obtained by subtracting the ratio of the residual change to the total change from 1. In other words, the coefficient of determination R 2The minimum value of _1(L) is 0 and the maximum value is 1. The closer it is to 1, the higher the accuracy of the linear regression equation generated by the regression equation generation unit 23.

[0211] Fig.12 is a diagram showing an example of a linear regression equation generated by the regression equation generation unit 23, which is equivalent to Figure 7 . Fig.12 Shown with Figure 7 Compared with the example shown in FIG. 1 , the vibration of the car 11 increases when the car 11 is traveling at a constant speed. When the vibration of the car 11 increases, the determination coefficient R 2 _1(L) becomes smaller. Therefore, according to Fig.12 The example shown calculates the coefficient of determination R 2 _1(L) ratio according to Figure 7 The example shown calculates the coefficient of determination R 2 _1(L) Small.

[0212] Fig.13 is a diagram showing another example of a linear regression equation generated by the regression equation generation unit 23, which is equivalent to Figure 7 . Fig.13 Shown with Figure 7 Compared with the example shown in FIG. 1 , a larger vibration is suddenly generated when the car 11 is traveling at a constant speed. Fig.13 The example shown calculates the coefficient of determination R 2 _1(L) ratio according to Figure 7 The example shown calculates the coefficient of determination R 2 _1(L) is small. On the other hand, according to Fig.13 The example shown calculates the coefficient of determination R 2 _1(L) ratio according to Fig.12 The example shown calculates the coefficient of determination R 2 _1(L) Large.

[0213] Thus, when the vertical vibration of the car 11 increases, the determination coefficient R 2 _1(L) becomes smaller. Therefore, the determination coefficient R 2 _1(L) is stored in the storage unit 32 as monitored information, and the determination coefficient R 2 _1(L) is one of the monitoring objects of the monitoring unit 35. Fig.14 It shows the coefficient of determination R 2 Figure _1(L) example. Fig.14 Examples of the values ​​when the car 11 is vibrating normally, that is, when no abnormal vibration is generated, the values ​​when the vibration increases, and the values ​​when sudden vibration is superimposed are shown. The monitoring unit 35 may also determine the determination coefficient R 2_1(L) is lower than the preset lower limit. In addition, the determination coefficient R can also be monitored by the monitoring unit 35. 2 _1(L) changes over time, and the monitoring result is reflected in the maintenance plan of the elevator device 10.

[0214] In addition, the coefficient of determination R 2 , the characteristic quantity calculation unit 27 may also calculate a value related to the residual based on the ascending linear regression equation generated by the regression equation generation unit 26 as a characteristic quantity associated with the torque of the hoisting machine 15. That is, the characteristic quantity calculation unit 27 calculates a value related to the residual between the intermediate position torque estimation value (observation value) calculated by the torque calculation unit 24 and the value (prediction value) obtained by the ascending linear regression equation as the characteristic quantity. In addition, the value related to the residual based on the ascending linear regression equation can be calculated even if the descending linear regression equation is not generated by the regression equation generation unit 26.

[0215] As an example, the value associated with the residual is the coefficient of determination R 2 Similar to equation (23), the coefficient of determination R of the linear regression equation generated by the regression equation generation unit 26 during the rising period is 2 _2_up is expressed by formula (24).

[0216] R 2 _2_up = 1-residual sum of squares / total sum of squares…(24)

[0217] As another example, the feature amount calculation unit 27 may calculate RMSE, MSE, MAE, or the like as a value related to the residual.

[0218] An example of a regression when rising is Fig. 9 shown. Fig. 9 The horizontal axis of represents the load factor in the car 11, and the vertical axis represents the intermediate position torque estimation value calculated by the torque calculation unit 24. 2 When the value of _2_up decreases, the corresponding relationship between the load factor in the car 11 and the intermediate position torque estimation value may change. Fig. 9 The horizontal axis of represents the value based on the actual measurement value of the weighing device 11d, for example. 2 When the value of _2_up becomes smaller, the intermediate position torque estimation value calculated using the linear regression equation becomes unstable, and it is reasonable to judge that the output of the weighing device 11d becomes unstable based on this.

[0219] About the coefficient of determination R 2 _2_up, can also be stored in the storage unit 32 as monitored information, and the determination coefficient R 2_2_up is one of the monitoring objects of the monitoring unit 35. For example, the monitoring unit 35 may also determine the determination coefficient R 2 _2_up is lower than the preset lower limit. In addition, the monitoring unit 35 may monitor the determination coefficient R 2 _2_up changes over time, and the monitoring result is reflected in the maintenance plan of the elevator device 10. In addition, in this example, it is not impossible to determine that the determination coefficient R 2 When the value of _2_up becomes smaller, the intermediate position torque estimation value becomes unstable.

[0220] The characteristic quantity calculation unit 27 may calculate, as a characteristic quantity associated with the torque of the hoisting machine 15, a value related to the residual based on the descending linear regression equation generated by the regression equation generation unit 26 instead of the value related to the residual based on the ascending linear regression equation. The characteristic quantity calculation unit 27 may calculate the value related to the residual based on the descending linear regression equation together with the value related to the residual based on the ascending linear regression equation. Fig.15 The coefficient of determination R based on the linear regression equation for the rising 2 _2_up and the coefficient of determination R based on the linear regression equation when the decline occurs 2 _2_dn is an example of a figure. In this example, the feature quantity calculation unit 27 calculates a value related to the residual between the intermediate position torque estimation value (observation value) calculated by the torque calculation unit 24 and the value (prediction value) obtained by the linear regression formula when descending as the feature quantity. In addition, the value related to the residual based on the linear regression formula when descending can be calculated even if the linear regression formula generation unit 26 does not generate the linear regression formula when ascending.

[0221] As an example, the value associated with the residual is the coefficient of determination R 2 Similar to equation (24), the coefficient of determination R of the linear regression equation generated by the regression equation generation unit 26 is 2 _2_dn is expressed by formula (25).

[0222] R 2 _2_dn = 1-residual sum of squares / total sum of squares…(25)

[0223] As another example, the feature amount calculation unit 27 may calculate RMSE, MSE, MAE, or the like as a value related to the residual.

[0224] Fig.16 2 is a diagram showing an example of hardware resources of the monitoring device 20. As hardware resources, the monitoring device 20 includes a processing circuit 60 including a processor 61 and a memory 62. The processing circuit 60 may include a plurality of processors 61. The processing circuit 60 may include a plurality of memories 62.

[0225] In this embodiment, the components indicated by reference numerals 21 to 28 represent functions of the monitoring device 20. The function of the storage unit 21 is implemented by the memory 62. The functions of the components indicated by reference numerals 22 to 28 can be implemented by software, firmware, or a combination of software and firmware described as a program. The program is stored in the memory 62. The monitoring device 20 implements the functions of the components indicated by reference numerals 22 to 28 by executing the program stored in the memory 62 by the processor 61 (computer).

[0226] The processor 61 is also called a CPU (Central Processing Unit), a central processing device, a processing device, an arithmetic device, a microprocessor, a microcomputer or a DSP. As the memory 62, a semiconductor memory, a magnetic disk, a floppy disk, an optical disk, a CD (compact disk), a mini disc or a DVD (Digital Versatile Disk) can also be used. The semiconductor memory that can be used includes RAM (Random Access Memory), ROM (Read Only Memory), flash memory, EPROM (Erasable Programmable Read Only Memory) and EEPROM (Electrically Erasable Programmable Read Only Memory).

[0227] Fig.17 FIG. 2 is a diagram showing another example of hardware resources of the monitoring device 20. Fig.17 In the example shown, the monitoring device 20 has a processing circuit 60 including a processor 61 , a memory 62 and dedicated hardware 63 . Fig.17 The example in which a part of the functions of the monitoring device 20 is implemented by the dedicated hardware 63 is shown. All the functions of the monitoring device 20 may be implemented by the dedicated hardware 63. As the dedicated hardware 63, a single circuit, a complex circuit, a programmed processor, a parallel programmed processor, an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or a combination thereof can be used.

[0228] The hardware resources of the analysis device 30 and Fig.16 or Fig.17The example shown is the same. As a hardware resource, the analysis device 30 has a processing circuit including a processor and a memory. The processing circuit may also include multiple processors. The processing circuit may include multiple memories. The function of the storage unit 32 is implemented by the memory. The analysis device 30 implements the functions of the various units shown by labels 33 to 36 by executing the program stored in the memory by the processor (computer). As a hardware resource, the analysis device 30 may also have a processing circuit including a processor, a memory and dedicated hardware. Regarding the analysis device 30, part or all of the functions of the device may also be implemented by dedicated hardware.

[0229] Examples of the embodiments that can be included in the present invention are explicitly described below as supplementary notes.

[0230] [Note 1]

[0231] An information system, wherein the information system has:

[0232] an information acquisition unit that acquires first information indicating a position of an elevator car and second information indicating a torque value of a traction machine that drives the car; and

[0233] A first regression equation generating unit generates a linear regression equation of the position of the car and the torque value of the hoisting machine when the car is performing a first travel at a constant speed in a hoistway based on the first information and the second information acquired by the information acquiring unit.

[0234] [Note 2]

[0235] The information system according to Appendix 1, wherein:

[0236] The information system further includes a torque calculation unit that calculates a torque value of the hoisting machine when the car is located at a middle position of the hoistway during the first travel, based on the linear regression equation generated by the first regression equation generation unit.

[0237] [Note 3]

[0238] The information system according to Appendix 1, wherein:

[0239] The information system further includes a slope determination unit that determines a slope of the linear regression equation generated by the first regression equation generation unit.

[0240] [Note 4]

[0241] The information system according to Appendix 2, wherein:

[0242] The information system further comprises a first storage unit,

[0243] The information acquisition unit acquires third information indicating a travel direction of the car when the first travel is being performed and fourth information indicating a load in the car when the first travel is being performed,

[0244] The third information and the fourth information acquired by the information acquisition unit and the fifth information indicating the torque value calculated by the torque calculation unit are stored in the first storage unit in association with each other as a set of state information.

[0245] [Note 5]

[0246] The information system according to Appendix 4, wherein:

[0247] By performing the travel of the car at a constant speed a plurality of times, a plurality of sets of status information are stored in the first storage unit.

[0248] [Note 6]

[0249] An information system, wherein the information system has:

[0250] an information acquisition unit that acquires third information and fourth information, wherein the third information indicates a travel direction of the elevator car when the elevator car is performing a first travel at a constant speed in the hoistway, and the fourth information indicates a load in the elevator car when the elevator car is performing the first travel at a constant speed in the hoistway; and

[0251] a first storage unit for storing the third information and the fourth information acquired by the information acquisition unit in association with fifth information indicating a torque value of a traction machine driving the car as a set of status information;

[0252] The fifth information is information indicating a torque value when the car is located at a middle position of the hoistway during the first travel.

[0253] By performing the travel of the car at a constant speed a plurality of times, state information of a plurality of groups is stored in the first storage unit.

[0254] [Note 7]

[0255] The information system according to Appendix 5 or 6, wherein:

[0256] The information system also has a second regression equation generating unit, which generates a linear regression equation for the increase of the load in the car when the car ascends at a constant speed and the torque value of the traction machine when the car is located in the middle position of the shaft, based on the status information of the multiple groups stored in the first storage unit, and generates a linear regression equation for the decrease of the load in the car when the car descends at a constant speed and the torque value of the traction machine when the car is located in the middle position of the shaft.

[0257] [Note 8]

[0258] The information system according to Appendix 7, wherein:

[0259] The information system further includes a feature quantity calculation unit that calculates a feature quantity associated with the torque of the hoisting machine based on the ascending linear regression equation and the descending linear regression equation generated by the second regression equation generation unit.

[0260] [Note 9]

[0261] The information system according to Appendix 8, wherein:

[0262] The feature amount calculation unit calculates a value of loss torque as the feature amount using information indicating an intercept of the linear regression equation during the rising period and information indicating an intercept of the linear regression equation during the falling period.

[0263] [Note 10]

[0264] The information system according to Appendix 9, wherein:

[0265] The feature quantity calculation unit calculates the feature quantity without using information indicating the slope of the linear regression equation when the rise occurs and information indicating the slope of the linear regression equation when the fall occurs.

[0266] [Note 11]

[0267] The information system according to any one of Supplementary Notes 8 to 10, wherein:

[0268] The feature amount calculation unit calculates the value of the unbalanced torque as the feature amount using information indicating the intercept of the linear regression equation when the linear regression equation is increased and information indicating the intercept of the linear regression equation when the linear regression equation is decreased.

[0269] [Note 12]

[0270] The information system according to any one of Supplementary Notes 8 to 11, wherein:

[0271] The feature quantity calculation unit calculates an estimated value of the balanced load in the car as the feature quantity using information indicating the slope and the intercept of the linear regression equation during the ascending state and information indicating the slope and the intercept of the linear regression equation during the descending state.

[0272] [Note 13]

[0273] The information system according to any one of Supplementary Notes 8 to 12, wherein:

[0274] The information system further includes a reproduction unit that reproduces the traveling phenomenon of the car in a cyberspace using the feature amount calculated by the feature amount calculation unit.

[0275] [Note 14]

[0276] The information system according to any one of Supplementary Notes 8 to 13, wherein:

[0277] The information system further includes a first communication unit that transmits information indicating the feature amount calculated by the feature amount calculation unit to a control device that controls the hoisting machine to use the information as a control parameter in the control device.

[0278] [Note 15]

[0279] The information system according to any one of Supplementary Notes 8 to 14, wherein:

[0280] The information system further includes a second storage unit that stores information indicating the feature amount calculated by the feature amount calculation unit in time series.

[0281] [Note 16]

[0282] The information system according to Appendix 15, wherein:

[0283] The information system further includes a monitoring unit that monitors changes in the feature amount stored in the second storage unit.

[0284] [Note 17]

[0285] The information system according to Appendix 16, wherein:

[0286] The information system further includes a second communication unit that transmits information for reflecting the monitoring result of the monitoring unit in a maintenance plan of the elevator device including the car.

[0287] [Note 18]

[0288] The information system according to Appendix 1, wherein:

[0289] The information system further includes a feature quantity calculation unit that calculates a value associated with a residual based on the linear regression equation generated by the first regression equation generation unit as a feature quantity associated with the torque of the hoisting machine.

[0290] [Note 19]

[0291] The information system according to Supplement 5 or 6, wherein the information system further comprises:

[0292] a second regression equation generating unit for generating a linear regression equation for the load in the car when the car is ascending at a constant speed and the torque value of the traction machine when the car is located at a middle position of the hoistway based on the state information of the plurality of groups stored in the first storage unit; and

[0293] A feature quantity calculation unit calculates a value related to a residual based on the ascending linear regression equation generated by the second regression equation generation unit as a feature quantity associated with the torque of the hoisting machine.

[0294] [Note 20]

[0295] The information system according to Supplement 5, 6 or 19, wherein the information system further comprises:

[0296] a second regression equation generating unit for generating a linear regression equation for the load in the car when the car is descending at a constant speed and the torque value of the traction machine when the car is located at a middle position of the hoistway based on the state information of the plurality of groups stored in the first storage unit; and

[0297] A feature quantity calculation unit calculates a value related to a residual based on the descending linear regression equation generated by the second regression equation generation unit as a feature quantity associated with the torque of the hoisting machine.

[0298] [Note 21]

[0299] A method for obtaining information, wherein the method for obtaining information comprises:

[0300] an information acquisition step of acquiring first information indicating a position of an elevator car and second information indicating a torque value of a traction machine driving the car; and

[0301] The first regression equation generating step generates a linear regression equation of the position of the car and the torque value of the hoisting machine during the first travel of the car at a constant speed in the hoistway based on the first information and the second information acquired in the information acquiring step.

[0302] [Note 22]

[0303] A method for obtaining information, wherein the method for obtaining information comprises:

[0304] an information acquisition step of acquiring third information and fourth information, wherein the third information indicates the travel direction of the elevator car when the elevator car is in a first travel at a constant speed in the hoistway, and the fourth information indicates the load in the elevator car when the elevator car is in a first travel at a constant speed in the hoistway; and

[0305] a first storage step of storing the third information and the fourth information obtained in the information acquisition step in association with the fifth information indicating the torque value of the traction machine driving the car as a set of state information in a first storage unit;

[0306] The fifth information is information indicating a torque value when the car is located at a middle position of the hoistway during the first travel.

[0307] By performing the travel of the car at a constant speed a plurality of times, state information of a plurality of groups is stored in the first storage unit.

[0308] The matters described in Supplementary Notes 2-5 and 7-20 may be directly or indirectly added to Supplementary Note 21, similarly to the matters described in Supplementary Notes 1 and 7-17, and 19-20 may be directly or indirectly added to Supplementary Note 6, similarly to the matters described in Supplementary Notes 2-5 and 7-20.

Claims

1. An information system, wherein: The information system has: an information acquisition unit that acquires first information indicating a position of a car of an elevator and second information indicating a torque value of a traction machine that drives the car; as well as A first regression equation generating unit generates a linear regression equation of the position of the car and the torque value of the hoisting machine when the car is performing a first travel at a constant speed in a hoistway based on the first information and the second information acquired by the information acquiring unit.

2. The information system according to claim 1, wherein: The information system further includes a torque calculation unit that calculates a torque value of the hoisting machine when the car is located at a middle position of the hoistway during the first travel, based on the linear regression equation generated by the first regression equation generation unit.

3. The information system according to claim 1, wherein: The information system further includes a slope determination unit that determines a slope of the linear regression equation generated by the first regression equation generation unit.

4. The information system according to claim 2, wherein: The information system further comprises a first storage unit, The information acquisition unit acquires third information indicating a travel direction of the car when the first travel is being performed and fourth information indicating a load in the car when the first travel is being performed, The third information and the fourth information acquired by the information acquisition unit and the fifth information indicating the torque value calculated by the torque calculation unit are stored in the first storage unit in association with each other as a set of state information.

5. The information system according to claim 4, wherein: By performing the travel of the car at a constant speed a plurality of times, a plurality of sets of status information are stored in the first storage unit.

6. An information system, wherein: The information system has: an information acquisition unit that acquires third information and fourth information, wherein the third information indicates a travel direction of the elevator car when the elevator car is performing a first travel at a constant speed in the hoistway, and the fourth information indicates a load in the elevator car when the elevator car is performing the first travel at a constant speed in the hoistway; and a first storage unit for storing the third information and the fourth information acquired by the information acquisition unit in association with fifth information indicating a torque value of a traction machine driving the car as a set of status information; The fifth information is information indicating a torque value when the car is located at a middle position of the hoistway during the first travel. By performing the travel of the car at a constant speed a plurality of times, a plurality of sets of status information are stored in the first storage unit.

7. The information system according to claim 5 or 6, wherein: The information system also has a second regression equation generating unit, which generates a linear regression equation for the increase of the load in the car when the car rises at a constant speed and the torque value of the hoisting machine when the car is located in the middle position of the shaft, based on the multiple groups of status information stored in the first storage unit, and generates a linear regression equation for the decrease of the load in the car when the car descends at a constant speed and the torque value of the hoisting machine when the car is located in the middle position of the shaft.

8. The information system according to claim 7, wherein: The information system further includes a feature quantity calculation unit that calculates a feature quantity associated with the torque of the hoisting machine based on the ascending linear regression equation and the descending linear regression equation generated by the second regression equation generation unit.

9. The information system according to claim 8, wherein: The feature amount calculation unit calculates a loss torque value as the feature amount using information indicating an intercept of the linear regression equation during the rising period and information indicating an intercept of the linear regression equation during the falling period.

10. The information system according to claim 9, wherein: The feature quantity calculation unit calculates the feature quantity without using information indicating the slope of the linear regression equation when the rise occurs and information indicating the slope of the linear regression equation when the fall occurs.

11. The information system according to claim 8, wherein: The feature amount calculation unit calculates an unbalanced torque value as the feature amount using information indicating an intercept of the linear regression equation when the linear regression equation is increased and information indicating an intercept of the linear regression equation when the linear regression equation is decreased.

12. The information system according to claim 8, wherein: The feature quantity calculation unit calculates an estimated value of the balanced load in the car as the feature quantity using information indicating the slope and the intercept of the linear regression equation during the ascending state and information indicating the slope and the intercept of the linear regression equation during the descending state.

13. The information system according to claim 8, wherein: The information system further includes a reproduction unit that reproduces the traveling phenomenon of the car in a cyberspace using the feature amount calculated by the feature amount calculation unit.

14. The information system according to claim 8, wherein: The information system further includes a first communication unit that transmits information indicating the feature amount calculated by the feature amount calculation unit to a control device that controls the hoisting machine to use the information as a control parameter in the control device.

15. The information system according to claim 8, wherein: The information system further includes a second storage unit that stores information indicating the feature amount calculated by the feature amount calculation unit in time series.

16. The information system according to claim 15, wherein: The information system further includes a monitoring unit that monitors changes in the feature amount stored in the second storage unit.

17. The information system according to claim 16, wherein: The information system further includes a second communication unit that transmits information for reflecting the monitoring result of the monitoring unit in a maintenance plan of the elevator device including the car.

18. The information system according to claim 1, wherein: The information system further includes a feature quantity calculation unit that calculates a value associated with a residual based on the linear regression equation generated by the first regression equation generation unit as a feature quantity associated with the torque of the hoisting machine.

19. The information system according to claim 5 or 6, wherein: The information system also has: a second regression equation generating unit for generating a linear regression equation for the load in the car when the car is ascending at a constant speed and the torque value of the traction machine when the car is located at a middle position of the hoistway based on the plurality of sets of status information stored in the first storage unit; as well as A feature quantity calculation unit calculates a value related to a residual based on the ascending linear regression equation generated by the second regression equation generation unit as a feature quantity associated with the torque of the hoisting machine.

20. The information system according to claim 5 or 6, wherein: The information system also has: a second regression equation generating unit for generating a linear regression equation for the load in the car when the car is descending at a constant speed and the torque value of the traction machine when the car is located at a middle position of the hoistway based on the plurality of sets of status information stored in the first storage unit; as well as A feature quantity calculation unit calculates a value related to a residual based on the descending linear regression equation generated by the second regression equation generation unit as a feature quantity associated with the torque of the hoisting machine.

21. A method for obtaining information, wherein: The information acquisition method comprises: an information acquisition step of acquiring first information indicating a position of an elevator car and second information indicating a torque value of a traction machine driving the car; and The first regression equation generating step generates a linear regression equation of the position of the car and the torque value of the hoisting machine during the first travel of the car at a constant speed in the hoistway based on the first information and the second information acquired in the information acquiring step.

22. A method for obtaining information, wherein: The information acquisition method comprises: an information acquisition step of acquiring third information and fourth information, wherein the third information indicates the travel direction of the elevator car when the elevator car is in a first travel at a constant speed in the hoistway, and the fourth information indicates the load in the elevator car when the elevator car is in a first travel at a constant speed in the hoistway; and a first storage step of storing the third information and the fourth information obtained in the information acquisition step in association with the fifth information indicating the torque value of the traction machine driving the car as a set of state information in a first storage unit; The fifth information is information indicating a torque value when the car is located at a middle position of the hoistway during the first travel. By performing the travel of the car at a constant speed a plurality of times, a plurality of sets of status information are stored in the first storage unit.

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