Elevator hoisting machine

Abstract

To provide an elevator hoisting machine that maintains a good lubricated state and allows bearings to function smoothly. [Solution] The elevator hoist 100 of the present disclosure is an elevator hoist 100 that winds up a rope 102 attached to a car 104, and is equipped with an electric motor 15, a sheave 1 connected to the electric motor 15 and rotated by the electric motor 15, a bearing 3 that supports the sheave 1, a grease storage section 6 in which grease that lubricates the bearing 3 is stored, a grease evacuation section 10 that has a flow path that allows grease to move between the grease storage section 6 and the grease evacuation section 10 and in which the grease is evacuated, and an evacuation adjustment mechanism section 19 that evacuates the grease in the grease storage section 6 to the grease evacuation section 10 and releases grease from the grease evacuation section 10 to the grease storage section 6.

Description

[Technical Field]

[0001] The present disclosure relates to an elevator hoist. [Background technology]

[0002] An elevator hoist is a device that winds up a rope attached to an elevator car. The hoist is composed of a sheave around which the rope is wound, a rolling bearing that supports the sheave, and other components. The bearings are lubricated with grease, and the rolling elements that make up the bearings push through the grease to support the smooth rotation of the sheave. To prevent damage to the sliding parts inside the bearings and reduce bearing loss, it is necessary to manage the state of grease supply to the bearings. For example, Patent Document 1 discloses a bearing lubrication structure that extends the lubrication life by changing the volume of the grease filling chamber to create a space between the bearing and the old grease for filling with new grease, and then supplies the new grease to the bearing. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 5129969 Summary of the Invention [Problem to be solved by the invention]

[0004] However, with conventional elevator hoist bearings, the condition of the grease that lubricates the bearings changes constantly with use, making it difficult to maintain good lubrication even when new grease is added to or replaced with old grease.

[0005] The present disclosure has been made to solve the above-mentioned problems, and aims to provide an elevator hoist that maintains a good lubricated state and allows bearings to function smoothly. [Means for solving the problem]

[0006] The elevator hoist according to the present disclosure is an elevator hoist that winds up a rope attached to a car, and includes an electric motor, a sheave connected to the electric motor and rotated by the electric motor, a bearing that supports the sheave, a grease storage section in which grease that lubricates the bearing is stored, a grease evacuation section that has a flow path that allows grease to move to and from the grease storage section and in which the grease is evacuated, and an evacuation adjustment mechanism that evacuates the grease in the grease storage section to the grease evacuation section and releases grease from the grease evacuation section to the grease storage section. [Effects of the Invention]

[0007] The elevator hoist of the present disclosure is provided with a grease evacuation section that allows grease to flow between the grease storage section and the grease reservoir section, and by evacuating the grease in the grease storage section to the grease evacuation section and releasing grease from the grease evacuation section to the grease storage section, the volume of the grease storage section can be adjusted, a good lubrication state can be maintained, and the bearings can function smoothly. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a schematic diagram showing the configuration of an elevator according to a first embodiment. [Figure 2] 1 is a schematic cross-sectional view showing an example of the configuration of a portion of an elevator hoisting machine according to a first embodiment. [Figure 3] FIG. 10 is a schematic cross-sectional view showing an example of the positions of a grease retreat section and a temperature sensor according to the second embodiment. [Figure 4] 2 is a schematic cross-sectional view showing an example of the configuration of a grease evacuation section of the elevator hoisting machine according to the first embodiment. FIG. [Figure 5] 5A to 5C are diagrams illustrating the operation of the grease evacuation section according to the first embodiment. [Figure 6] 5A to 5C are diagrams illustrating the operation of the grease evacuation section according to the first embodiment. [Figure 7]10 is a relationship diagram showing the relationship between the stirring resistance of grease and the amount of withdrawal in the bearing of the elevator hoisting machine according to the second embodiment. FIG. [Figure 8] 1 is a schematic cross-sectional view showing an example of the configuration of a portion of an elevator hoisting machine according to a first embodiment. [Figure 9] 1 is a schematic cross-sectional view showing an example of the configuration of a portion of an elevator hoisting machine according to a first embodiment. [Figure 10] FIG. 10 is a block diagram showing the configuration of a grease withdrawal adjustment unit according to a second embodiment. [Figure 11] FIG. 10 is a schematic diagram showing a three-layer neural network model according to the second embodiment. [Figure 12] 10 is a flowchart showing the flow of learning of a grease evacuation adjustment unit according to the second embodiment. [Figure 13] FIG. 10 is a block diagram showing the configuration of a grease retraction adjustment unit and a retraction adjustment mechanism unit of an elevator hoisting machine according to a second embodiment. [Figure 14] 10 is a flowchart showing the flow of grease evacuation adjustment according to the second embodiment. [Figure 15] FIG. 11 is a block diagram showing the configuration of a grease withdrawal adjustment unit according to a third embodiment. [Figure 16] 11 is a flowchart showing the flow of learning of a grease evacuation adjustment unit according to the third embodiment. [Figure 17] FIG. 11 is a block diagram showing the configuration of a grease withdrawal adjustment unit according to a third embodiment. [Figure 18] 11 is a flowchart showing the flow of grease evacuation adjustment according to the third embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. Note that identical or corresponding parts in each drawing are designated by the same reference numerals. In the description of the embodiments, the description of identical or corresponding parts will be omitted or simplified as appropriate.

[0010] Embodiment 1 An elevator hoist 100 according to a first embodiment will be described with reference to Figs. 1 to 9. Fig. 1 is a schematic diagram showing the configuration of an elevator. The elevator is a lifting machine that stops a car 104 in accordance with the position of a floor, and the car 104 is attached to one end of a rope 102 wound around a sheave 1 of the hoist 100, and a counterweight 103 is attached to the other end. A deflector sheave 105 is provided to adjust the hanging center position and winding angle of the counterweight 103. The electric motor 15 of the hoist 100 is connected to a control panel 101, and raises and lowers the car 104 via the rope 102.

[0011] Fig. 2 is a schematic cross-sectional view showing the configuration of the surrounding area of bearing 3 of hoisting machine 100. In Fig. 2, bearing 3 on the electric motor 15 side is not shown. The hoisting machine 100 is a hoisting machine 100 that winds up rope 102 attached to car 104, and includes electric motor 15, sheave 1 connected to and rotated by electric motor 15, bearing 3 that supports sheave 1, grease reservoir 6 in which grease that lubricates bearing 3 is stored, grease retreat section 10 that has a flow path that allows grease to travel to and from grease reservoir 6 and through which grease retreats, and retreat adjustment mechanism 19 that retreats grease from grease reservoir 6 to grease retreat section 10 and releases grease from grease retreat section 10 to grease reservoir 6.

[0012] The hoist 100 winds up a rope 102 attached to a car 104 using a sheave 1 rotated by an electric motor 15. Passengers and others board the car 104 and repeatedly ascend and descend. A grease reservoir 6 is provided in the bearing 3 supporting the sheave 1, and grease for lubricating the bearing 3 is stored in the grease reservoir 6. The grease in the grease reservoir 6 lubricates the bearing 3, but as the car ascends and descends repeatedly, for example, if the temperature of the grease in the grease reservoir 6 rises, the consistency of the grease increases, i.e., it becomes softer, and the stirring resistance of the grease decreases. Therefore, the volume of the grease reservoir 6 is increased, and grease is released from the grease evacuation section 10 into the grease reservoir 6. Conversely, when the grease temperature is low, the consistency of the grease decreases, i.e., it becomes hard, and the resistance to stirring the grease increases. Therefore, the volume of the grease reservoir 6 is reduced, and the hard grease is evacuated to the grease evacuation section 10.

[0013] The grease reservoir 6 is a space provided in the bearing housing 4 that houses the bearing 3, and is closed by, for example, a volume adjustment piston 5 that adjusts the volume, and is filled with grease. The grease reservoir 6 also includes gaps within the bearing 3. The grease reservoir 6 and the grease retreat 10 are connected by a flow path that allows grease to move back and forth, and the grease in the grease reservoir 6 is retreated to the grease retreat 10. To reliably retreat the grease to the grease retreat 10 and to release the grease from the grease retreat 10 to the grease reservoir 6, it is preferable that the space in the grease reservoir 6 be filled with grease, but it is also acceptable for there to be space in the grease reservoir 6. The volume of the grease reservoir 6 can be adjusted, for example, by the volume adjustment piston 5 that moves linearly in the axial direction. The volume adjustment piston 5 has a curved shape, for example, so that its radial outer side covers the shaft portion 2a of the sheave 1 protruding from the bearing 3, and a seal portion 11 is provided on the surface that contacts the shaft portion 2a and the surface that contacts the bearing housing portion 4 to prevent grease from leaking out of the grease storage portion 6. The volume adjustment piston 5 may have any shape that can adjust the volume of the grease storage portion 6. The seal portion 11 is, for example, an O-ring. The shaft portion 2a is inserted from the sheave 1 side, and a seal portion 11 is provided on the outer peripheral surface of the shaft portion 2a to prevent grease from leaking out of the grease storage portion 6. The seal portion 11 is, for example, a lip seal.

[0014] The retraction adjustment mechanism 19 has, for example, a volume adjustment piston 5 that can reciprocate in the axial direction of the bearing 3. The volume adjustment piston 5 is provided facing the grease reservoir 6, and the volume of the grease reservoir 6 can be adjusted by the volume adjustment piston 5 reciprocating in the axial direction of the bearing 3.

[0015] The retraction adjustment mechanism 19 further includes, for example, a piston driver 9a and an adjustment shaft 8 to move the volume adjustment piston 5 in the axial direction of the bearing 3. The piston driver 9a is connected to the volume adjustment piston 5 via the adjustment shaft 8. The piston driver 9a is, for example, a motor that rotates the adjustment shaft 8, or a linear actuator that linearly moves the adjustment shaft 8 in the axial direction of the bearing 3. When the piston driver 9a is a motor that rotates the adjustment shaft 8, a screw is formed on the adjustment shaft 8, and the volume adjustment piston 5 includes an operation mode conversion unit 7 that converts the rotational movement of the adjustment shaft 8 into linear movement. The screw of the adjustment shaft 8 is screwed into the operation mode conversion unit 7, and when the adjustment shaft 8 rotates, the volume adjustment piston 5 moves linearly. The screw is, for example, a trapezoidal screw, a square screw, or a ball screw.

[0016] The bearing receiving portion 4 receives the bearing 3 and is composed of a bearing receiving portion main body 20 , a front cover 17 and an end cover 16 .

[0017] Bearing 3 is housed in bearing housing 4 and comprises outer ring 3a, rolling elements 3b, and inner ring 3c, and is provided on the electric motor 15 side of sheave 1 and the opposite side of electric motor 15 (hereinafter referred to as the anti-motor side) to support the load that sheave 1 receives from rope 102. While Fig. 2 shows a self-aligning roller bearing 3, an open-type bearing such as a tapered roller bearing may also be used as bearing 3. Here, when the term "axial direction" is used in the case of a self-aligning roller bearing, it includes the direction of the axis within the range of the angular difference between the axial direction of inner ring 3c and the axial direction of outer ring 3a.

[0018] The grease retreat section 10 is provided on the outside of the bearing accommodating section 4 so that grease can travel to and from the grease reservoir 6. The grease retreat section 10 is preferably provided in the axial direction of the bearing accommodating section 4, for example, above the AA cross section of FIG. 2 shown in FIG. 3, so as to ensure a flow path from the grease reservoir 6 to the grease retreat section 10 even when the surface of the volume adjustment piston 5 that contacts the grease reservoir 6 is closest to the bearing 3. The grease retreat section 10 is provided, for example, on the outside of the bearing accommodating section 4 in the direction opposite to the cage 104 side. Multiple grease retreat sections 10 may be provided in the circumferential direction of the bearing accommodating section 4.

[0019] In Figure 3, a temperature sensor 12 that measures the temperature of the grease in the grease reservoir 6 is provided, for example, at a position 30° away from the grease retraction section 10, so that the state of the grease can be measured based on the temperature. The grease temperature is preferably measured in the grease in the grease reservoir 6 near the bearing 3. The phase at which the temperature sensor 12 is provided is not limited to 30°, but may be 20°, 40°, etc. Furthermore, multiple temperature sensors 12 may be provided in the circumferential and axial directions of the bearing housing section 4.

[0020] The grease evacuation section 10 is configured, for example, as shown in FIG. 4. FIG. 4 shows a state in which the retraction piston 10d is at bottom dead center, in which case the retraction space 10e is minimized and the amount of grease retracted is minimized. For example, the grease evacuation section 10 has a retraction case 10c, a retraction piston 10d, an adjustment spring 10b, and a cover 10a. The retraction piston 10d, which changes the space in which the grease is evacuated, is provided so as to be able to reciprocate toward the bearing housing section 4. The retraction piston 10d has a protrusion 10f that protrudes from the surface opposite the bearing housing section 4, and the adjustment spring 10b is arranged to cover the protrusion 10f. The adjustment spring 10b is, for example, a compression coil spring, and presses the retraction piston 10d toward the bearing housing section 4 even when the retraction piston 10d is at bottom dead center. Here, instead of positioning the retraction piston 10d so that the protruding portion 10f of the retraction piston 10d faces in the opposite direction from the bearing storage section 4, the protruding portion 10f may face the bearing storage section 4 side, and a tension coil spring may be fixed to the surface of the retraction piston 10d that comes into contact with the retracted grease.

[0021] The grease retreat section 10 is preferably detachable from the bearing housing 4. It is fixed to the bearing housing 4, which has a threaded hole 14, with bolts 13, making it detachable. Because the grease retreat section 10 is detachable, when replacing the grease in the grease reservoir 6, the volume of the grease reservoir 6 can be reduced, the grease retreated to the grease retreat section 10 can be removed, and the used grease can be easily collected. The retreat space 10e is formed by the retreat case 10c and the retreat piston 10d, and stores grease retreated from the grease reservoir 6. To make the grease collection process easier, it is preferable that the total maximum volume of the retreat spaces 10e when all the retreat pistons 10d of one or more grease retreat sections 10 are at top dead center, i.e., when the retreat pistons 10d are at the farthest position from the bearing housing 4 inside the grease retreat section 10, be equal to or greater than the maximum volume of the grease reservoir 6.

[0022] Next, the operation of retracting or releasing grease to the grease retraction section 10 will be described. The volume adjustment piston 5 provided in the bearing housing section 4 moves toward or away from the side of the bearing 3. When the volume adjustment piston 5 moves toward the bearing 3, the grease retraction adjustment section reduces the volume of the grease storage section 6, and as shown in FIG. 5, the grease in the grease storage section 6 is retracted toward the grease retraction section 10. When retracting grease to the grease retraction section 10, the retraction piston 10d moves in the direction opposite the bearing housing section 4 and compresses the adjustment spring 10b. Conversely, when the volume adjustment piston 5 moves away from the bearing 3, the grease retraction adjustment section increases the volume of the grease storage section 6, and as shown in FIG. 6, the grease is released, or returned, from the grease retraction section 10 to the grease storage section 6. Figure 7 is a graph showing the relationship between the agitation resistance of the grease in the bearing 3 and the amount of withdrawal when thin-consistent, i.e., hard, grease is present in the grease reservoir 6. As shown in Figure 7, the smaller the amount of grease withdrawn, the smaller the effect an increase in the amount of withdrawal has on the amount of reduction in agitation resistance, but the greater the amount of grease withdrawn to the grease retreat section 10, the greater the effect an increase in the amount of withdrawal has on the amount of reduction in agitation resistance. Therefore, by increasing the amount of withdrawal, the bearing 3 can rotate and function more smoothly.

[0023] In this way, the system is equipped with an electric motor 15, a sheave 1 connected to the electric motor 15 and rotated by the electric motor 15, a bearing 3 supporting the sheave 1, a grease storage section 6 in which grease that lubricates the bearing 3 is stored, a grease evacuation section 10 which has a flow path that allows grease to move between the grease storage section 6 and the grease storage section 6 and to which the grease is evacuated, and an evacuation adjustment mechanism section 19 which evacuates the grease in the grease storage section 6 to the grease evacuation section 10 and releases grease from the grease evacuation section 10 to the grease storage section 6, so that the volume of the grease storage section 6 can be adjusted, a good lubrication state can be maintained, and the bearing 3 can function smoothly.

[0024] Furthermore, since the retraction adjustment mechanism 19 has a volume adjustment piston 5a that can move back and forth in the axial direction of the bearing 3 and a piston drive unit 9a that drives the adjustment piston, the volume of the grease storage unit 6 can be easily adjusted even in a hoisting machine 100 that has a large bearing 3 with a large amount of grease in the grease storage unit 6.

[0025] Furthermore, by configuring the grease retreat section 10 to be detachable from the bearing storage section 4, when the grease is retreated to the grease retreat section 10 and then the grease that was in the grease storage section 6 is removed during inspection, etc., the grease can be easily recovered.

[0026] In Figure 2, an example has been described in which the grease reservoir 6, the retraction adjustment mechanism 19, and the grease retraction section 10 are provided in the bearing housing 4 on the opposite side to the motor. However, as shown in Figure 8, the shafts 2b and 2c of the sheave 1 may also be inserted into the bearing 3 on the motor 15 side via the motor 15, and the grease reservoir 6, the retraction adjustment mechanism 19, and the grease retraction section 10 may also be provided in the bearing housing 4 that houses the bearing 3 on the motor 15 side.

[0027] 2 shows an example of a bearing housing 4 in which the front cover 17 and the end cover 16 are integrated, but as shown in FIG. 9, the bearing housing 4 may be configured separately from the bearing housing main body 20, the front cover 17, and the end cover 16. The bearing housing 4 only needs to house the bearing 3 and have the retraction adjustment mechanism 19 attached to it. The outer periphery of the volume adjustment piston 5a only needs to be inscribed in the bearing housing 4. It may be inscribed in the bearing housing main body 20 or in the end cover 16. When the end cover 16 is provided, a through hole into which the adjustment shaft 8 of the retraction adjustment mechanism 19 is inserted is formed in the end cover 16, and an adjustment support bearing 18 that supports the rotation of the adjustment shaft 8 is provided in the through hole. This makes it easy to manufacture the bearing housing 4 and assemble and disassemble it.

[0028] Embodiment 2 A hoist 100 according to a second embodiment will be described with reference to Fig. 10 to Fig. 14. In the first embodiment, an example was described in which the volume of the grease storage section 6 was adjusted by adjusting the amount of grease retracted into the grease retraction section 10 based on the state of the grease, such as the temperature of the grease, but the second embodiment differs in that the amount of grease retracted into the grease retraction section 10 is calculated based on at least one of hoist measurement data indicating the measured operational state of winding up the rope 102, preset specification data, and operation data indicating past operating conditions. The following description will focus on the differences from the first embodiment, and descriptions of the same or corresponding parts will be omitted as appropriate.

[0029] The generation of a learned model for calculating the amount of grease evacuated to the grease evacuation section 10 (hereinafter referred to as the evacuated amount) in this embodiment will be described. Fig. 10 is a functional block diagram showing the configuration of a grease evacuation adjustment section 200 related to the hoisting machine 100. The grease evacuation adjustment section 200 includes a evacuated amount learning device 201 having a data acquisition section 202 that acquires hoisting machine measurement data, specification data, operation data, etc., and a model generation section 203 that generates a learning model, and a learned model storage section 204 that stores a calculation model for calculating the evacuated amount.

[0030] The data acquisition unit 202 acquires at least one of hoisting machine measurement data indicating the operating state of winding the measured rope 102, preset specification data, and operation data, as well as teacher data on the amount of retraction (hereinafter referred to as retraction amount teacher data) as learning data.

[0031] The measured hoist measurement data indicating the operating state of winding the rope 102 is a feature that affects the state of the grease, and indicates, for example, the grease temperature and the temperature of the bearing 3 described above. The operating state also includes a stationary state in which the power is turned on and preparations for winding operation are complete. The grease temperature is a feature that affects the consistency of the grease; the lower the temperature, the lower the consistency and the harder the grease becomes. As for the temperature of the bearing 3, the bearing 3 generates heat through its rotation, which increases the grease temperature through heat transfer, affecting the increase in grease consistency, i.e., the softening of the grease.

[0032] The pre-set specification data are characteristic quantities that affect the initial amount of grease filled in the hoisting machine 100, and indicate the load capacity of the car 104, the size of the grease reservoir 6 including its maximum and minimum volumes, characteristic values related to fluidity such as the consistency of the grease, etc. For example, in the case of an elevator with a specification data specification in which the load capacity of the car 104 is large, the hoisting machine will also be large and the size of the grease reservoir 6 will also be large, which will increase the amount of grease in the grease reservoir 6 and will tend to result in greater resistance to stirring the grease during use compared to a small hoisting machine 100.

[0033] The operational data are characteristic quantities that affect the deterioration of the grease, such as the number of times the car 104 has been raised and lowered, the operating time, the time elapsed since the grease was refilled, and the heat cycle of the grease. The heat cycle is the number of times the grease has been cooled from a high temperature to a low temperature in the temperature history of the grease. For example, the influence of the heat cycle, which is one of the operational data, on the consistency of the grease will be explained. When the temperature load is removed from a high temperature to a low temperature, condensation may occur in the grease reservoir 6. When moisture is contained in the grease due to condensation, the consistency of the grease increases, i.e., it softens, and the stirring resistance of the grease decreases.

[0034] The model generation unit 203 learns the target value of the retraction amount based on learning data created based on a combination of hoist measurement data, specification data, operation data, etc., and retraction amount teacher data. That is, a learned model is generated that calculates an optimal target value of the retraction amount from the hoist measurement data, specification data, operation data, etc., of the hoist 100 and the retraction amount teacher data. Here, the learning data is data that associates the hoist measurement data, specification data, operation data, etc., with the retraction amount.

[0035] The learning algorithm used by the model generation unit 203 may be a known algorithm such as supervised learning, unsupervised learning, reinforcement learning, etc. As an example, a case where a neural network is applied will be described.

[0036] The model generation unit 203 learns the target value of the evacuation amount by so-called supervised learning, for example, according to a neural network model. Here, supervised learning refers to a method in which pairs of input and result (label) data are provided to the evacuation amount learning device 201, and the device learns the features of the learning data and infers the result from the input.

[0037] A neural network consists of an input layer consisting of multiple neurons, an intermediate layer (hidden layer) consisting of multiple neurons, and an output layer consisting of multiple neurons. The intermediate layer may be one layer, or two or more layers.

[0038] For example, in a three-layer neural network as shown in Figure 11, when multiple inputs are input to the input layer (X1-X3), the values are multiplied by weight W1 (w11-w16) and input to the middle layer (Y1-Y2), and the result is further multiplied by weight W2 (w21-w26) and output from the output layer (Z1-Z3). This output result changes depending on the values of weights W1 and W2.

[0039] The neural network learns the retraction amount by so-called supervised learning in accordance with learning data created based on a combination of the retraction amount and hoist measurement data, specification data, operation data, etc.

[0040] In other words, the neural network learns by inputting hoist measurement data, specification data, operational data, etc. into the input layer and adjusting the weights W1 and W2 so that the results output from the output layer approach the retraction amount training data.

[0041] The model generation unit 203 generates and outputs a trained model by performing the above-described learning.

[0042] The trained model storage unit 204 stores the trained model output from the model generation unit 203.

[0043] Next, the learning process of the grease evacuation adjustment unit 200 will be described with reference to Fig. 12. Fig. 12 is a flowchart showing the flow of learning by the grease evacuation adjustment unit 200.

[0044] In step S101, the data acquisition unit 202 acquires hoisting machine measurement data, specification data, operation data, etc., and retraction amount teacher data. Although the hoisting machine measurement data, specification data, operation data, etc., and the retraction amount teacher data are acquired simultaneously, it is sufficient if the hoisting machine measurement data, specification data, operation data, etc., and the retraction amount teacher data are input in association with each other, and the hoisting machine measurement data, specification data, operation data, etc., and the retraction amount teacher data may be acquired at different times.

[0045] In step S102, the model generation unit 203 learns the target value of the retraction amount by so-called supervised learning in accordance with learning data created based on a combination of the hoist measurement data, specification data, operating data, etc. acquired by the data acquisition unit 202 and the retraction amount, and generates a learned model.

[0046] In step S103, the trained model storage unit 204 stores the trained model generated by the model generation unit 203.

[0047] Next, the use of a trained model for calculating the target value of the retraction amount in this embodiment will be described. FIG. 13 is a block diagram showing the configurations of the grease retraction adjustment unit 200a and the retraction adjustment mechanism unit 19. As shown in FIG. 13, the grease retraction adjustment unit 200a includes a trained model storage unit 204a that stores a trained model for calculating the target value of the retraction amount, a data acquisition unit 202a that acquires data from at least one of the measurement unit 205 and the specification data storage unit 206, a numerical calculation unit 208 that calculates the target value of the retraction amount based on the data acquired by the data acquisition unit 202a, and an operation control unit 209 that controls the operation of the retraction adjustment mechanism unit 19. The operation control unit 209 sends a control signal via wire or wireless to the piston drive unit 9b of the retraction adjustment mechanism unit 19, causing the volume adjustment piston 5b, which is directly or indirectly connected to the piston drive unit 9b, to move linearly in the axial direction of the bearing 3.

[0048] The measurement unit 205 measures at least one of hoisting machine measurement data indicating the operational state of winding up the rope 102, preset specification data, and operation data indicating past operating conditions.

[0049] The specification data storage unit 206 stores preset elevator specification data and transmits the specification data to the data acquisition unit 202 .

[0050] The data acquisition unit 202a receives data from at least one of the measurement unit 205 and the specification data storage unit 206, and integrates the data. The data acquisition unit 202a transmits the integrated data to the numerical calculation unit 208.

[0051] The numerical calculation unit 208 calculates a target value of the retraction amount based on the data received from the data acquisition unit 202a and the learned model stored in the learned model storage unit 204a, and outputs the target value of the retraction amount to the operation control unit 209. Here, the numerical calculation unit 208 may calculate a set position in the axial direction of the volume adjustment piston 5b from the target value of the retraction amount. That is, the numerical calculation unit 208 calculates the target value of the retraction amount obtained using the learned model. By inputting at least one of the hoisting machine measurement data indicating the measured operating state of winding up the rope 102, which is acquired by the data acquisition unit 202a, preset specification data, and operation data indicating past operating conditions, into this learned model, it is possible to output a retraction amount inferred from at least one of the hoisting machine measurement data indicating the measured operating state of winding up the rope 102, preset specification data, and operation data indicating past operating conditions.

[0052] The operation control unit 209 receives a control signal related to the target value calculated by the numerical calculation unit 208 and sends a signal to the piston driver 9b to control the amount of movement of the adjustment piston so that at least one of the volume of the evacuation space 10e of the grease evacuation unit 10, the amount of grease evacuation, and the volume of the grease reservoir 6 reaches the target value, thereby operating the piston driver 9b. Based on the signal received from the operation control unit 209, the piston driver 9b controls the position of the volume adjustment piston 5b so that it matches the set position of the volume adjustment piston 5 output from the numerical calculation unit 208. Here, if the piston driver 9b is a stepping motor, the operation control unit 209 can control the rotation using the rotation angle. Therefore, it is possible to calculate the amount of movement of the volume adjustment piston 5 from the rotation angle and confirm whether the volume adjustment piston 5b has moved the desired distance. Even if a stepping motor is not used, it is possible to confirm whether the volume adjustment piston 5b has moved the desired distance by, for example, providing a distance sensor in the bearing housing 4 that measures the axial movement of the volume adjustment piston 5b.

[0053] The grease evacuation adjustment unit 200a may be provided in the hoist or in another location. For example, the grease evacuation adjustment unit 200a may be located on a server, a cloud, or the like, and the data acquisition unit 202a may acquire data via a wired or wireless connection.

[0054] Next, a process for obtaining a target value of the amount of evacuation using the amount of evacuation calculation device 207 will be described with reference to FIG.

[0055] In step S201, the data acquisition unit 202a acquires at least one of hoisting machine measurement data indicating the measured operating state of winding up the rope 102, preset specification data, and operation data indicating past operating conditions.

[0056] In step S202, the grease evacuation adjustment unit 200a acquires a trained model from the trained model storage unit 204a, inputs the data acquired by the data acquisition unit 202a into the acquired trained model, and obtains a target value for the evacuation amount.

[0057] In step S203, the grease evacuation adjustment unit 200a outputs the target value of the evacuation amount obtained by the learned model to the operation control unit 209.

[0058] In step S204, the operation control unit 209 uses the output target value of the retraction amount to control the piston drive unit and adjust the retraction amount, thereby making it possible to automatically maintain a good lubrication state with grease based on the state or specifications of the hoisting machine.

[0059] In this way, in the hoist 100 according to embodiment 2, the target value of the retraction amount is determined based on at least one of the hoist measurement data indicating the measured operating state of winding up the rope 102, the preset specification data, and the operating data indicating the past operating conditions, thereby making it possible to adjust the retraction amount according to the state or specifications of the hoist, and automatically maintain a good lubrication state of the bearing 3.

[0060] The retraction amount learning device 201 and the retraction amount calculation device 207 are used to learn the retraction amount of the hoist 100, but may be devices connected to the hoist 100 via a network and separate from the hoist 100, for example. The retraction amount learning device 201 and the retraction amount calculation device 207 may be built into the hoist 100. Furthermore, the retraction amount learning device 201 and the retraction amount calculation device 207 may exist on a cloud server. The learned model and specification data may be stored in advance and called up according to set conditions, or may be input by an operator or the like.

[0061] In addition, in this embodiment, the target value of the evacuation amount is described as being output using a learned model stored in the learned model memory unit 204a provided in the hoisting machine 100, but it is also possible to obtain a learned model from an external source, such as another hoisting machine 100, and output the evacuation amount to the grease evacuation section 10 based on this learned model.

[0062] Furthermore, although an example has been shown in which the operation control unit 209 controls the position of the volume adjustment piston 5b to a desired position, the operation control unit 209 may also control the volume adjustment piston 5b so that the retraction piston 10d of the grease retraction unit 10 is at a desired position. By the operation control unit 209 controlling the volume adjustment piston 5b so that the retraction piston 10d of the grease retraction unit 10 is at a desired position, the retraction amount can be adjusted more accurately to reduce the stirring resistance in the bearing 3.

[0063] In addition, by using at least one of the grease temperature and the temperature of the bearing 3 as the hoisting machine measurement data, it is possible to adjust at least one of the volume and withdrawal amount of the grease storage section 6 taking into account factors that affect the grease stirring resistance in the bearing 3, thereby reducing the grease stirring resistance in the bearing 3.

[0064] Furthermore, by defining the specification data as at least one of the loading capacity of the cage 104, the maximum volume of the grease storage section 6, and the characteristic values of the grease, it is possible to adjust at least one of the volume and evacuation amount of the grease storage section 6 not only for a specific model but also for models with different specifications.

[0065] In addition, by using as the operating data at least one of the number of times the cage 104 is raised and lowered, the operating time, the time elapsed since the grease was supplied, and the heat cycle of the grease, the amount of retraction can be adjusted based on the operating history of the hoisting machine 100.

[0066] Furthermore, the numerical calculation unit 208 calculates the set position of the adjustment piston based on the target value of the retraction amount, and the operation control unit 209 controls the adjustment piston to the set position via the piston drive unit 9b, thereby enabling the volume adjustment piston 5b to operate accurately, thereby improving the accuracy of the retraction amount adjustment.

[0067] Embodiment 3 A hoist 100 according to a third embodiment will be described with reference to Figures 15 to 18. In the second embodiment, the adjustment of the amount of grease evacuated based on hoist measurement data and the like was described, but the third embodiment differs in that reinforcement learning is used to improve the accuracy of the target value for the amount of grease evacuated. The following description will focus on the differences from the second embodiment, and descriptions of the same or corresponding parts will be omitted as appropriate.

[0068] Generation of a trained model for inferring a target value for the withdrawal amount in this embodiment will be described. Fig. 15 is a configuration diagram of a grease withdrawal adjustment unit 300 according to embodiment 3. The grease withdrawal adjustment unit 300 includes a withdrawal amount reinforcement learning device 301 having a learning data acquisition unit 302 that acquires learning data including an actual withdrawal amount value and rotational resistance data of the electric motor 15 that are necessary for reinforcement learning of the target value for the withdrawal amount, an agitation resistance value setting unit 303 that sets the rotational resistance data of the electric motor as the agitation resistance value of grease in the bearing 3, and a model generation unit 304 that generates a trained model for inferring the target value for the withdrawal amount, and a trained model storage unit 307.

[0069] The learning data acquisition unit 302 acquires, as learning data, the actual measured value of the amount of grease evacuated to the grease evacuation section 10 and rotational resistance data of the electric motor 15. The rotational resistance data of the electric motor 15 is, for example, the current of the electric motor 15, the torque measured by a torque meter, etc. Here, the learning data acquisition unit 302 may acquire the volume of the grease reservoir 6, the position of the retraction piston 10d of the grease retraction unit 10, the position of the volume adjustment piston 5c, etc., and convert them into actual retraction amount measurements.

[0070] The stirring resistance value setting unit 303 sets the rotation resistance data of the electric motor 15 acquired by the learning data acquisition unit 302 as the stirring resistance value of the grease in the bearing 3. If the learning data acquisition unit 302 can acquire the stirring resistance value of the grease in the bearing 3, the stirring resistance value setting unit 303 may be omitted.

[0071] The model generation unit 304 learns the target value of the retraction amount based on learning data including the actual retraction amount value and the stirring resistance value. That is, it generates a learned model that infers the target value of the retraction amount from the stirring resistance value of the hoisting machine 100.

[0072] As an example of a learning algorithm used by the model generation unit 304, a case where reinforcement learning is applied will be described. In reinforcement learning, an agent (acting subject) in a certain environment observes the current state (environmental parameters) and decides on an action to take. The environment changes dynamically depending on the agent's actions, and the agent is given a reward according to the environmental changes. The agent repeats this process and learns the course of action that will obtain the most reward through a series of actions. Known representative methods of reinforcement learning are Q-learning and TD-learning. For example, in the case of Q-learning, the update formula for the action value function Q(s, a) is expressed as the following formula (1):

[0073]

number

[0074] In equation (1), s t represents the state of the environment at time t, and a t represents the action at time t. Action a t Therefore, the state is s t+1 Changes to r t+1 represents the reward obtained by the change in state, γ represents the discount rate, and α represents the learning coefficient. Note that γ is in the range of 0<γ≦1, and α is in the range of 0<α≦1. When the actual value of the evacuation amount is t The stirring resistance value is in state s t and the state s at time t t Best Practices in a t Learn.

[0075] The update formula expressed by equation (1) increases the action value Q if the action value Q of the action a with the highest Q value at time t+1 is greater than the action value Q of the action a executed at time t, and decreases the action value Q in the opposite case. In other words, the action value function Q(s, a) is updated so that the action value Q of the action a at time t approaches the best action value at time t+1. As a result, the best action value in a certain environment is propagated sequentially to the action value in the previous environment.

[0076] As described above, when generating a trained model by reinforcement learning, the model generation unit 304 includes a reward calculation unit 305 and a function update unit 306.

[0077] The reward calculation unit 305 calculates a reward based on the measured retraction amount and the agitation resistance value. The reward calculation unit 305 calculates a reward r based on the agitation resistance value. For example, if the agitation resistance value decreases, the reward r is increased (for example, a reward of "1" is given), and on the other hand, if the agitation resistance value increases, the reward r is decreased (for example, a reward of "-1" is given).

[0078] The function update unit 306 updates the function for determining the target value of the evacuation amount in accordance with the reward calculated by the reward calculation unit 305, and outputs the updated function to the learned model storage unit 307. For example, in the case of Q-learning, the action value function Q(s t ,a t ) is used as a function to calculate the target value of the evacuation amount.

[0079] The learning process is repeated as described above. The learned model storage unit 307 stores the action-value function Q(s t ,a t ), i.e., stores the trained model.

[0080] Next, the learning process of the grease evacuation adjustment unit 300 will be described with reference to Fig. 16. Fig. 16 is a flowchart showing the learning process of the grease evacuation adjustment unit 300.

[0081] In step S301, the learning data acquisition unit 302 acquires the measured retraction amount value and the rotational resistance data of the electric motor 15 as learning data.

[0082] In step S302 , the stirring resistance value setting unit 303 sets the rotation resistance data of the electric motor 15 acquired by the learning data acquisition unit 302 as the stirring resistance value of the grease in the bearing 3 .

[0083] In step S303, the model generation unit 304 calculates a reward based on the measured withdrawal amount and the stirring resistance value. Specifically, the reward calculation unit 305 acquires the measured withdrawal amount and the stirring resistance value, and determines whether to increase the reward (step S304) or decrease the reward (step S305) based on a predetermined reward standard.

[0084] If the reward calculation unit 305 determines that the reward should be increased, it increases the reward in step S304. On the other hand, if the reward calculation unit 305 determines that the reward should be decreased, it decreases the reward in step S305.

[0085] In step S306, the function update unit 306 updates the action value function Q(s t ,a t ) to update the

[0086] The retreat amount reinforcement learning device 301 repeatedly executes the above steps S301 to S306, and generates the action value function Q(s t ,a t ) is stored as a trained model.

[0087] The evacuation amount reinforcement learning device 301 according to this embodiment stores the learned model in a learned model memory unit 307 provided inside the grease evacuation adjustment unit 300, but the learned model memory unit 307 may also be provided outside the grease evacuation adjustment unit 300.

[0088] Next, the use of a trained model for inferring a target value for the withdrawal amount in this embodiment will be described. Fig. 17 is a configuration diagram of a grease withdrawal adjustment unit 300a according to embodiment 3. The grease withdrawal adjustment unit 300a includes a withdrawal amount inference device 308 having an environmental data acquisition unit 309 that acquires rotational resistance data of the electric motor 15, an agitation resistance value setting unit 303a that sets the rotational resistance data of the electric motor 15 as an agitation resistance value, and an inference unit 310 that infers a target value for the withdrawal amount, a trained model storage unit 307a, and an operation control unit 311.

[0089] The environmental data acquisition unit 309 acquires rotational resistance data of the electric motor 15. The rotational resistance data of the electric motor 15 is, for example, the current of the electric motor 15, torque data measured by a torque meter, and the like.

[0090] The stirring resistance value setting unit 303a sets the rotational resistance data of the electric motor 15 acquired by the environmental data acquisition unit 309 as the stirring resistance value of the grease in the bearing 3. Note that if the learning data acquisition unit 302 can acquire the stirring resistance of the grease in the bearing 3, the stirring resistance value setting unit 303a may be omitted.

[0091] The inference unit 310 infers the target value of the retraction amount using the trained model trained by the model generation unit 304 of the hoisting machine 100. That is, by inputting the rotational resistance data of the electric motor 15 acquired by the environmental data acquisition unit 309 into this trained model, it is possible to infer the target value of the retraction amount that is suitable for the rotational resistance data of the electric motor 15.

[0092] Here, in this embodiment, it has been described that the target value of the retraction amount is output using a learned model learned by the model generation unit 304 of the hoisting machine 100, but it is also possible to obtain a learned model from another hoisting machine 100 and output the target value of the retraction amount based on this learned model.

[0093] Next, the process for obtaining the saved amount using the saved amount inference device 308 will be described with reference to FIG.

[0094] In step S401 , the environmental data acquisition unit 309 acquires the rotational resistance data of the electric motor 15 .

[0095] In step S402, the stirring resistance value setting unit 303a sets the rotation resistance data of the electric motor 15 acquired by the environmental data acquisition unit 309 as the stirring resistance value of the grease in the bearing 3.

[0096] In step S403, the grease evacuation adjustment unit 300a acquires a learned model from the learned model storage unit 307a, inputs the stirring resistance value into the acquired learned model, and obtains a target value for the evacuation amount.

[0097] In step S404, the inference unit 310 outputs the target value of the evacuation amount obtained from the learned model to the operation control unit 311.

[0098] In step S405, the operation control unit 311 of the hoisting machine 100 uses the output target value of the retraction amount to drive the piston driving unit 9c and adjust the retraction amount via the volume adjustment piston 5c. This makes it possible to reduce the stirring resistance of the grease in the bearing 3.

[0099] In this way, by generating a trained model that infers the target value of the retraction amount in the hoisting machine 100, it can be applied to hoisting machines 100 that have a retraction adjustment mechanism unit 19 located in a different location, thereby reducing the stirring resistance of grease in the bearings 3 of multiple hoisting machines 100.

[0100] In addition, by optimizing the amount of retraction using a learned model that infers a target value for the amount of retraction in the grease retraction adjustment unit 300, a hoist 100 can be provided in which the rotational load of the electric motor on the bearing 3 is low.

[0101] In this embodiment, a case where reinforcement learning is applied to the learning algorithm used by the inference unit 310 has been described, but the present invention is not limited to this. As for the learning algorithm, it is also possible to apply supervised learning shown in the second embodiment in addition to reinforcement learning.

[0102] Furthermore, the learning algorithm used in the model generation unit 304 may be deep learning, which learns to extract the features themselves, or machine learning may be performed according to other known methods, such as neural networks or genetic programming.

[0103] Furthermore, the retraction amount reinforcement learning device 301 and the retraction amount reasoning device 308 may be connected to the hoisting machine 100 via a network, for example, and may be separate devices from the hoisting machine 100. Furthermore, the retraction amount reinforcement learning device 301 and the retraction amount reasoning device 308 may be built into the hoisting machine 100. Furthermore, the retraction amount reinforcement learning device 301 and the retraction amount reasoning device 308 may exist on a cloud server.

[0104] The model generation unit 304 may also learn the retraction amount using learning data acquired from multiple hoists 100. The model generation unit 304 may acquire learning data from multiple hoists 100 used in the same area, or may learn the target value of the retraction amount using learning data collected from multiple hoists 100 operating independently in different areas. It is also possible to add or remove a hoist 100 from which learning data is collected as a target during the process. Furthermore, the retraction amount reinforcement learning device 301 that has learned the target value of the retraction amount for a certain hoist 100 may be applied to another hoist 100, and the retraction amount for the other hoist 100 may be re-learned and updated.

[0105] Furthermore, the evacuation amount inference device 308 according to this embodiment refers to a learned model from the learned model memory unit 307a provided inside the grease evacuation adjustment unit 300a, but the learned model memory unit 307a may also be provided outside the grease evacuation adjustment unit 300a.

[0106] Although various exemplary embodiments are described in this disclosure, the various features, aspects, and functions described in one or more embodiments are not limited to the application of a particular embodiment, but may be applied to the embodiments alone or in various combinations. Therefore, countless variations not illustrated are contemplated within the scope of the technology disclosed herein. For example, this includes cases where at least one component is modified, added, or omitted, and even cases where at least one component is extracted and combined with components of another embodiment.

[0107] Various aspects of the present disclosure are summarized below as appendices.

[0108] (Appendix 1) An elevator hoist that hoists a rope attached to a car, An electric motor, a sheave connected to the electric motor and rotated by the electric motor; a bearing supporting the sheave; a grease reservoir in which grease for lubricating the bearing is stored; a grease evacuation section having a flow path through which the grease can travel between the grease storage section and the grease evacuation section, and through which the grease is evacuated; a retraction adjustment mechanism that retracts the grease in the grease storage portion to the grease retraction portion and releases the grease from the grease retraction portion to the grease storage portion; An elevator hoisting machine comprising: (Appendix 2) The elevator hoist described in Appendix 1, wherein the retraction adjustment mechanism includes a volume adjustment piston that adjusts the volume of the grease storage section, and a piston drive unit that reciprocates the volume adjustment piston in the axial direction of the bearing. (Appendix 3) a bearing housing portion in which the bearing is housed, 3. The elevator hoist according to claim 1, wherein the grease evacuation section is detachably provided in the bearing housing section. (Appendix 4) a grease evacuation adjustment unit that adjusts the evacuation of the grease to the grease evacuation unit, The grease evacuation adjustment unit is a data acquisition unit that acquires at least one of hoist measurement data indicating the measured operating state of the rope being hoisted, preset specification data, and operation data indicating past operating conditions; a numerical calculation unit that calculates the target value of the amount of grease to be evacuated from the grease reservoir to the grease evacuation unit based on the data acquired by the data acquisition unit; and an operation control unit that controls the operation of the retraction adjustment mechanism unit to control the retraction amount of the grease to the calculated target value; 4. An elevator hoist according to claim 1, further comprising: (Appendix 5) 5. The elevator hoist according to claim 4, wherein the hoist measurement data is at least one of the temperature of the grease and the temperature of the bearing. (Appendix 6) 6. The elevator hoist according to claim 4, wherein the specification data is at least one of a load capacity of the car, a maximum volume of the grease reservoir, and a characteristic value of the grease. (Appendix 7) 7. The elevator hoist according to claim 4, wherein the operation data is at least one of the number of times the car has ascended and descended, the operation time, the elapsed time since the grease was supplied, and the heat cycle of the grease. (Appendix 8) a grease evacuation adjustment unit that adjusts the evacuation of the grease to the grease evacuation unit, The grease evacuation adjustment unit is a learning data acquisition unit that acquires learning data including rotational resistance data of the electric motor and an actual measurement value of the amount of grease withdrawn from the rotational resistance data; an agitation resistance value setting unit that sets the rotation resistance data as an agitation resistance value of the grease; a model generation unit that generates a trained model for inferring a target value of the amount of grease retracted into the grease retraction unit based on the stirring resistance value, using the training data; 8. An elevator hoisting machine according to any one of claims 1 to 7, comprising: (Appendix 9) a grease evacuation adjustment unit that adjusts the evacuation of the grease to the grease evacuation unit, The grease evacuation adjustment unit is an environmental data acquisition unit that acquires rotational resistance data of the electric motor; an agitation resistance value setting unit that sets the rotation resistance data as an agitation resistance value of the grease; an inference unit that infers a target value of the amount of grease withdrawn based on the stirring resistance value using a trained model for inferring a target value of the amount of grease withdrawn from the stirring resistance value; an operation control unit that controls the operation of the retraction adjustment mechanism unit to control the retraction amount of the grease to the estimated target value; 9. An elevator hoist according to any one of claims 1 to 8, comprising: [Explanation of symbols]

[0109] 1 sheave, 2a, 2b, 2c shaft section, 3 bearing, 4 bearing storage section, 5a, 5b, 5c volume adjustment piston, 6 grease storage section, 7 operation form conversion section, 8 adjustment shaft, 9a, 9b, 9c piston drive section, 10 grease evacuation section, 11 seal section, 12 temperature sensor, 13 bolt, 14 screw hole, 15 electric motor, 16 end cover, 17 front cover, 18 adjustment support bearing, 19 evacuation adjustment mechanism section, 20 bearing storage section body, 100 hoisting machine, 101 control panel, 102 rope, 103 counterweight, 104 cage, 105 deflector sheave, 200, 200a, 300, 300a grease evacuation adjustment section, 202, 202a Data acquisition unit, 204, 204a, 307, 307a learned model storage unit, 209, 311 operation control unit, 302 learned data acquisition unit, 303, 303a stirring resistance value setting unit, 203, 304 model generation unit, 309 environmental data acquisition unit, 310 inference unit

Claims

1. An elevator hoist that hoists a rope attached to a car, An electric motor, a sheave connected to the electric motor and rotated by the electric motor; a bearing supporting the sheave; a grease reservoir in which grease for lubricating the bearing is stored; a grease evacuation section having a flow path through which the grease can travel between the grease storage section and the grease evacuation section, and through which the grease is evacuated; a retraction adjustment mechanism that retracts the grease in the grease storage portion to the grease retraction portion and releases the grease from the grease retraction portion to the grease storage portion; An elevator hoisting machine comprising:

2. 2. The elevator hoist according to claim 1, wherein the retraction adjustment mechanism includes a volume adjustment piston that adjusts the volume of the grease reservoir, and a piston drive unit that reciprocates the volume adjustment piston in the axial direction of the bearing.

3. a bearing housing portion in which the bearing is housed, The elevator hoist according to claim 1, wherein the grease evacuation section is detachably provided in the bearing housing section.

4. a grease evacuation adjustment unit that adjusts the evacuation of the grease to the grease evacuation unit, The grease evacuation adjustment unit is a data acquisition unit that acquires at least one of hoist measurement data indicating the measured operating state of the rope being hoisted, preset specification data, and operation data indicating past operating conditions; a numerical calculation unit that calculates a target value of the amount of grease to be evacuated from the grease reservoir to the grease evacuation unit based on the data acquired by the data acquisition unit; and an operation control unit that controls the operation of the retraction adjustment mechanism unit to control the retraction amount of the grease to the calculated target value; 2. The elevator hoisting machine according to claim 1, further comprising:

5. The elevator hoist according to claim 4, wherein the hoist measurement data is at least one of the temperature of the grease and the temperature of the bearing.

6. 5. The elevator hoist according to claim 4, wherein the specification data is at least one of a load capacity of the car, a maximum volume of the grease reservoir, and a characteristic value of the grease.

7. 5. The elevator hoist according to claim 4, wherein the operation data is at least one of the number of times the car has ascended and descended, the operation time, the time elapsed since the grease was supplied, and the heat cycle of the grease.

8. a grease evacuation adjustment unit that adjusts the evacuation of the grease to the grease evacuation unit, The grease evacuation adjustment unit is a learning data acquisition unit that acquires learning data including rotational resistance data of the electric motor and an actual measurement value of the amount of grease withdrawn from the rotational resistance data; an agitation resistance value setting unit that sets the rotation resistance data as an agitation resistance value of the grease; a model generation unit that generates a trained model for inferring a target value of the amount of grease retracted into the grease retraction unit based on the stirring resistance value, using the training data; 2. The elevator hoisting machine according to claim 1, comprising:

9. a grease evacuation adjustment unit that adjusts the evacuation of the grease to the grease evacuation unit, The grease evacuation adjustment unit is an environmental data acquisition unit that acquires rotational resistance data of the electric motor; an agitation resistance value setting unit that sets the rotation resistance data as an agitation resistance value of the grease; an inference unit that infers a target value of the amount of grease withdrawn based on the stirring resistance value using a trained model for inferring a target value of the amount of grease withdrawn from the stirring resistance value; an operation control unit that controls the operation of the retraction adjustment mechanism unit to control the retraction amount of the grease to the estimated target value; 2. The elevator hoisting machine according to claim 1, further comprising:

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