Elevator hoisting machine

Abstract

To provide an elevator hoisting machine that maintains good lubrication and ensures smooth bearing operation. [Solution] The elevator hoisting machine 100 according to the present disclosure is an elevator hoisting machine 100 for winding up a rope 102 attached to a car 104, and comprises 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 for lubricating the bearing 3 is stored, a grease retraction section 10 having a passage that allows grease to move between the grease storage section 6 and the grease retraction section 10, and a retraction adjustment mechanism section 19 that retracts grease in the grease storage section 6 to the grease retraction section 10 or releases grease from the grease retraction section 10 to the grease storage section 6.

Description

[Technical Field]

[0001] This disclosure relates to a hoisting machine for elevators. [Background technology]

[0002] An elevator hoist is a device that winds up a rope attached to an elevator car. The hoist consists of a sheave around which the rope is wound, rolling bearings that support the sheave, etc. The bearing is lubricated with grease, and the rolling elements that make up the bearing support the smooth rotation of the sheave by pushing aside the grease. To prevent damage to the sliding parts within the bearing and to reduce bearing losses, it is necessary to manage the supply state of grease to the bearing. 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 for filling new grease between the bearing and the old grease, thereby supplying new grease to the bearing. [Prior art documents] [Patent Documents]

[0003] [Patent Document 1] Patent No. 5129969 [Overview of the project] [Problems that the invention aims to solve]

[0004] However, conventional elevator hoisting machines have a problem in that the condition of the grease that lubricates the bearings changes moment by moment with use, making it difficult to maintain a consistently good lubrication state even if new grease is added to old grease or replaced.

[0005] This disclosure was made to solve the problems described above, and aims to provide an elevator hoisting machine that maintains good lubrication and allows the bearings to function smoothly. [Means for solving the problem]

[0006] The elevator hoisting machine according to this disclosure is an elevator hoisting machine for winding up a rope attached to a car, and comprises an electric motor, a sheave connected to the electric motor and rotated by the electric motor, a bearing supporting the sheave, a grease reservoir for storing grease that lubricates the bearing, a grease retraction section having a passage that allows grease to move between the grease reservoir and the grease retraction section, and a retraction adjustment mechanism for retracting grease from the grease reservoir to the grease retraction section or releasing grease from the grease retraction section to the grease reservoir. [Effects of the Invention]

[0007] The elevator hoisting machine of this disclosure is provided with a grease receptacle that allows grease to move between it and the grease reservoir, and by relocating the grease in the grease reservoir to the grease receptacle or releasing grease from the grease receptacle to the grease reservoir, the volume of the grease reservoir can be adjusted, a good lubrication state can be maintained and the bearing can function smoothly. [Brief explanation of the drawing]

[0008] [Figure 1] This is a schematic diagram showing the configuration of an elevator according to Embodiment 1. [Figure 2] This is a schematic cross-sectional view showing an example of a part of the configuration of an elevator hoisting machine according to Embodiment 1. [Figure 3] This is a schematic cross-sectional view showing an example of the position of the grease retraction section and temperature sensor according to Embodiment 2. [Figure 4] This is a schematic cross-sectional view showing an example of the configuration of the grease receptacle section of an elevator hoisting machine according to Embodiment 1. [Figure 5] This diagram illustrates the operation of the grease retraction section according to Embodiment 1. [Figure 6] This diagram illustrates the operation of the grease retraction section according to Embodiment 1. [Figure 7]It is a relational diagram showing the relationship between the stirring resistance of grease and the retreat amount in the bearing of the elevator hoisting machine according to Embodiment 2. [Figure 8] It is a schematic cross-sectional view showing an example of a part of the configuration of the elevator hoisting machine according to Embodiment 1. [Figure 9] It is a schematic cross-sectional view showing an example of a part of the configuration of the elevator hoisting machine according to Embodiment 1. [Figure 10] It is a block diagram showing the configuration of the grease retreat adjustment unit according to Embodiment 2. [Figure 11] It is a schematic diagram showing the three-layer neural network model according to Embodiment 2. [Figure 12] It is a flowchart showing the learning process of the grease retreat adjustment unit according to Embodiment 2. [Figure 13] It is a block diagram showing the configuration of the grease retreat adjustment unit and the retreat adjustment mechanism unit of the elevator hoisting machine according to Embodiment 2. [Figure 14] It is a flowchart showing the process of grease retreat adjustment according to Embodiment 2. [Figure 15] ]>It is a block diagram showing the configuration of the grease retreat adjustment unit according to Embodiment 3. [Figure 16] It is a flowchart showing the learning process of the grease retreat adjustment unit according to Embodiment 3. [Figure 17] It is a block diagram showing the configuration of the grease retreat adjustment unit according to Embodiment 3. [Figure 18] It is a flowchart showing the process of grease retreat adjustment according to Embodiment 3.

Embodiments for Carrying Out the Invention

[0009] Hereinafter, embodiments according to the present disclosure will be described with reference to the drawings. The same or corresponding parts in each figure are denoted by the same reference numerals. In the description of the embodiments, the description of the same or corresponding parts will be omitted or simplified as appropriate.

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

[0011] Figure 2 is a schematic cross-sectional view showing the configuration of the area surrounding the bearing 3 of the hoisting machine 100. In Figure 2, the bearing 3 on the electric motor 15 side is not shown. The hoisting machine 100 is a hoisting machine 100 that winds up a rope 102 attached to a cage 104, and comprises 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 reservoir 6 in which grease for lubricating the bearing 3 is stored, a grease retraction section 10 having a passage that allows grease to move between the grease reservoir 6 and the grease retraction section 6 and to retract the grease, and a retraction adjustment mechanism 19 that retracts the grease in the grease reservoir 6 to the grease retraction section 10 or releases grease from the grease retraction section 10 to the grease reservoir 6.

[0012] The hoisting machine 100 uses a sheave 1, which is rotated by an electric motor 15, to wind up a rope 102 attached to a car 104. Passengers and others ride in the car 104, and it is repeatedly raised and lowered. The bearing 3 that supports the sheave 1 is provided with a grease reservoir 6, which stores grease to lubricate the bearing 3. The grease in the grease reservoir 6 lubricates the bearing 3, but as the car is repeatedly raised and lowered, for example, the temperature of the grease in the grease reservoir 6 rises, the consistency of the grease increases, that is, it becomes softer, and the agitation resistance of the grease decreases. Therefore, the volume of the grease reservoir 6 is increased, and grease is released from the grease receptacle 10 into the grease reservoir 6. Conversely, when the grease temperature is low, the consistency of the grease decreases, meaning it hardens, and the resistance to stirring the grease increases. Therefore, the volume of the grease reservoir 6 is reduced, and the hardened grease is moved to the grease receptacle 10.

[0013] The grease reservoir 6 is a space enclosed within 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 the gaps within the bearing 3. The grease reservoir 6 and the grease receptacle 10 are connected by a passage through which grease can move, and the grease from the grease reservoir 6 is recepted to the grease receptacle 10. In order to reliably recept the grease to the grease receptacle 10 and to release grease from the grease receptacle 10 to the grease reservoir 6, it is preferable that the space of the grease reservoir 6 is filled with grease, but space may exist in the grease reservoir 6. The volume of the grease reservoir 6 can be adjusted by, for example, a volume adjustment piston 5 that moves linearly in the axial direction. The volume adjustment piston 5 has a curved shape, for example, such that its radially outer side covers the shaft portion 2a of the sheave 1 which protrudes from the bearing 3. Seal portions 11 are provided on the surface that contacts the shaft portion 2a and the surface that contacts the bearing housing 4 to prevent grease from leaking to the outside of the grease reservoir 6. The volume adjustment piston 5 only needs to have a shape that can adjust the volume of the grease reservoir 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 circumferential surface of the shaft portion 2a to prevent grease from the grease reservoir 6 from leaking to the outside. The seal portion 11 is, for example, a lip seal.

[0014] The retraction adjustment mechanism 19 includes, 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 reciprocating motion of the volume adjustment piston 5 in the axial direction of the bearing 3.

[0015] The retraction adjustment mechanism 19 further includes, for example, a piston drive unit 9a and an adjustment shaft 8 to move the volume adjustment piston 5 in the axial direction of the bearing 3. The piston drive unit 9a is connected to the volume adjustment piston 5 via the adjustment shaft 8. The piston drive unit 9a is, for example, a motor that rotates the adjustment shaft 8, or a linear actuator that moves the adjustment shaft 8 linearly in the axial direction of the bearing 3. If the piston drive unit 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 has an operation mode conversion unit 7 that converts the rotational movement of the adjustment shaft 8 into linear movement. The operation mode conversion unit 7 is used to screw in the screw on the adjustment shaft 8, 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 housing section 4 houses the bearing 3 and consists of the bearing housing section body 20, a front cover 17, and an end cover 16.

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

[0018] The grease receptacle 10 is provided on the outside of the bearing housing 4 so that grease can move between it and the grease reservoir 6. It is preferable that the grease receptacle 10 is provided in the axial direction of the bearing housing 4, for example, at the top of the AA cross-section in Figure 2 shown in Figure 3, so as to ensure a flow path from the grease reservoir 6 to the grease receptacle 10 even when the surface of the volume adjustment piston 5 in contact with the grease reservoir 6 is closest to the bearing 3. The grease receptacle 10 is provided, for example, on the outside of the bearing housing 4 in the direction opposite to the cage 104 side. Multiple grease receptacles 10 may be provided in the circumferential direction of the bearing housing 4.

[0019] In Figure 3, for example, a temperature sensor 12 is provided at a position 30° away from the grease receptacle 10 to measure the temperature of the grease in the grease reservoir 6, allowing the condition of the grease to be measured by temperature. It is preferable to measure the temperature of 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 4.

[0020] The grease retraction section 10 is configured as shown in Figure 4, for example. Figure 4 shows the state where the retraction piston 10d is at its bottom dead center. In this case, the retraction space 10e is minimized, and the amount of grease retracted is minimized. For example, the grease retraction section 10 includes a retraction case 10c, a retraction piston 10d, an adjustment spring 10b, and a cover 10a. The retraction piston 10d, which changes the space for retracting grease, is provided to be reciprocable toward the bearing housing 4. The retraction piston 10d is provided with a projection 10f that protrudes from the side opposite to the bearing housing 4, and the adjustment spring 10b is positioned to cover the projection 10f. The adjustment spring 10b is, for example, a compression coil spring, which pushes the retraction piston 10d toward the bearing housing 4 even when the retraction piston 10d is at its bottom dead center. Here, instead of positioning the retracted piston 10d with its protruding portion 10f facing away from the bearing housing 4, the protruding portion 10f may face the bearing housing 4, and a tension coil spring may be fixed to the surface of the retracted piston 10d that comes into contact with the retracted grease.

[0021] It is preferable that the grease receptacle 10 is detachable from the bearing housing 4. It is fixed to the bearing housing 4, which has screw holes 14, with bolts 13 to make it removable. By making the grease receptacle 10 detachable, when replacing the grease in the grease reservoir 6, the volume of the grease reservoir 6 can be reduced, and the grease can be relocated to the grease receptacle 10 before the grease receptacle 10 is removed, making it easier to recover the used grease. The receptacle space 10e is formed by the receptacle case 10c and the receptacle piston 10d, and the grease relocated from the grease reservoir 6 is stored there. To further facilitate the grease recovery work, it is preferable that the total maximum volume of the receptacle space 10e when all the receptacle pistons 10d of one or more grease receptacle 10s are at top dead center, i.e., when the receptacle pistons 10d are at the position furthest from the bearing housing 4 inside the grease receptacle 10 is greater than or equal to the maximum volume of the grease reservoir 6.

[0022] Next, the operation of retracting or releasing grease into the grease retraction section 10 will be described. The volume adjustment piston 5, located within the bearing housing 4, moves closer to and away from the side of the bearing 3. When the volume adjustment piston 5 moves closer to the bearing 3, the grease retraction adjustment section reduces the volume of the grease reservoir 6, and as shown in Figure 5, retracts the grease from the grease reservoir 6 to the grease retraction section 10. When retracting grease into the grease retraction section 10, the retraction piston 10d moves in the direction opposite to the bearing housing 4, compressing 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 reservoir 6, and as shown in Figure 6, releases, or returns, grease from the grease retraction section 10 to the grease reservoir 6. Figure 7 is a diagram showing the relationship between the stirring resistance of the grease in the bearing 3 and the amount of grease retraction when the grease in the grease reservoir 6 is of low consistency, i.e., hard. As shown in Figure 7, the smaller the amount of grease retraction, the smaller the effect of increasing the amount of retraction on the decrease in stirring resistance. However, the larger the amount of grease retraction into the grease retraction section 10, the greater the effect of increasing the amount of retraction on the decrease in stirring resistance. Therefore, by increasing the amount of retraction, the bearing 3 can rotate and function smoothly.

[0023] Thus, the system includes 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 reservoir 6 where grease for lubricating the bearing 3 is stored, a grease receptacle 10 having a passage that allows grease to move between the grease reservoir 6 and the reservoir 6, and where grease is retracted, and a retraction adjustment mechanism 19 that retracts grease in the grease reservoir 6 to the grease receptacle 10 or releases grease from the grease receptacle 10 to the grease reservoir 6. As a result, the volume of the grease reservoir 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 includes a volume adjustment piston 5a that can reciprocate in the axial direction of the bearing 3 and a piston drive unit 9a that drives the adjustment piston, the volume of the grease reservoir 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 reservoir 6.

[0025] Furthermore, by making the grease receptacle 10 detachable from the bearing housing 4, when inspecting the bearing housing 4, the grease can be easily recovered from the grease reservoir 6 by removing the grease receptacle 10 while the grease has been recepted into it.

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

[0027] Furthermore, although Figure 2 shows an example of a bearing housing 4 in which the front cover 17 and end cover 16 are integrated, as shown in Figure 9, the bearing housing 4 may also be configured by separating it into a bearing housing body 20, a front cover 17, and an end cover 16. The bearing housing 4 only needs to house the bearing 3 and to have the retraction adjustment mechanism 19 attached. The outer circumference of the volume adjustment piston 5a only needs to be in contact with the bearing housing 4. It may be in contact with the bearing housing body 20, or it may be in contact with the end cover 16. If an end cover 16 is provided, a through hole can be formed in the end cover 16 into which the adjustment shaft 8 of the retraction adjustment mechanism 19 is inserted, and an adjustment support bearing 18 that supports the rotation of the adjustment shaft 8 can be provided in the through hole, making it easier to manufacture the bearing housing 4 and to assemble and disassemble it.

[0028] Embodiment 2. The hoisting machine 100 according to Embodiment 2 will be described with reference to Figures 10 to 14. In Embodiment 1, an example was described in which the amount of grease retracted into the grease retraction section 10 is adjusted based on the state of the grease, such as the temperature of the grease, to increase the volume of the grease storage section 6. However, Embodiment 2 differs in that the amount of grease retracted into the grease retraction section 10 is calculated based on at least one of the following: hoisting machine measurement data showing the operation state of winding up the measured rope 102, preset specification data, and operation data showing past operating conditions. The following will focus on the differences from Embodiment 1, and descriptions of identical or corresponding parts will be omitted as appropriate.

[0029] This embodiment describes the generation of a trained model for calculating the amount of grease to be relocated to the grease relocation section 10 (hereinafter referred to as the relocation amount). Figure 10 is a functional block diagram showing the configuration of the grease relocation adjustment section 200 related to the hoisting machine 100. The grease relocation adjustment section 200 includes a relocation amount learning device 201 having a data acquisition unit 202 that acquires hoisting machine measurement data, specification data, operation data, etc., and a model generation unit 203 that generates a trained model, and a trained model storage unit 204 that stores a calculation model for calculating the relocation amount.

[0030] The data acquisition unit 202 acquires training data including at least one of the following: hoisting machine measurement data indicating the operation status of winding up the measured rope 102, preset specification data, and operational data, as well as training data for the amount of evacuation (hereinafter referred to as evacuation amount training data).

[0031] The hoisting machine measurement data, which shows the operating state of the measured rope 102 being hoisted, includes characteristic quantities that affect the state of the grease, such as the grease temperature and the temperature of the bearing 3. The operating state also includes the stationary state when the power is turned on and preparations for hoisting operation are complete. The temperature of the grease is a characteristic quantity that affects the consistency of the grease; the lower the temperature, the lower the consistency and the harder it becomes. Regarding the temperature of the bearing 3, the bearing 3 generates heat due to its rotational operation, and the heat transfer action increases the temperature of the grease, which affects the increase in the consistency of the grease, i.e., the softening of the grease.

[0032] The pre-set specification data refers to characteristic quantities that affect the initial amount of grease sealed in the hoisting machine 100, and includes the loading capacity of the cage 104, the size including the maximum and minimum volumes of the grease storage section 6, and characteristic values ​​related to fluidity such as the consistency of the grease. For example, in the case of an elevator with a specification that includes a large loading capacity for the cage 104, the hoisting machine will also be larger, and the size of the grease storage section 6 will also increase. As a result, the amount of grease in the grease storage section 6 will increase, and the resistance to stirring the grease will tend to increase with use compared to a smaller hoisting machine 100.

[0033] Operating data refers to characteristic quantities that affect the degradation of the grease, such as 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 heat cycle is the number of times the grease has been cooled from a high temperature to a low temperature in the temperature history applied to it. For example, the effect of the heat cycle, which is one of the operating 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 added to the grease due to condensation, the consistency of the grease increases, that is, it softens and the stirring resistance of the grease decreases.

[0034] The model generation unit 203 learns the target value of the evacuation amount based on training data created from combinations of hoisting machine measurement data, specification data, operation data, etc., and evacuation amount training data. In other words, it generates a trained model that calculates the optimal target value of the evacuation amount from the hoisting machine measurement data, specification data, operation data, etc., of the hoisting machine 100 and the evacuation amount training data. Here, the training data is data that associates the hoisting machine measurement data, specification data, operation data, etc., with the evacuation amount.

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

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

[0037] A neural network consists of an input layer made up of multiple neurons, an intermediate layer (hidden layer) made up of multiple neurons, and an output layer made up of multiple neurons. The intermediate layer can be one 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), these values ​​are multiplied by weights W1 (w11-w16) and input to the hidden layer (Y1-Y2). The result is then multiplied again by weights W2 (w21-w26) and output from the output layer (Z1-Z3). This output varies depending on the values ​​of weights W1 and W2.

[0039] The neural network learns the evacuation amount through so-called supervised learning, according to training data created based on combinations of hoisting machine measurement data, specification data, operation data, etc., and the evacuation amount.

[0040] In other words, the neural network learns by inputting hoisting machine measurement data, specification data, operation data, etc. into the input layer and adjusting the weights W1 and W2 so that the result output from the output layer approaches the evacuation amount training data.

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

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

[0043] Next, using Figure 12, we will explain the learning process of the grease retraction adjustment unit 200. Figure 12 is a flowchart showing the learning flow of the grease retraction adjustment unit 200.

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

[0045] In step S102, the model generation unit 203 learns a target value for the evacuation amount and generates a trained model by supervised learning, based on training data created from combinations of hoisting machine measurement data, specification data, operation data, etc., acquired by the data acquisition unit 202 and the evacuation amount.

[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 learned model for calculating the target value of the retraction amount in this embodiment will be described. Figure 13 is a block diagram showing the configuration of the grease retraction adjustment unit 200a and the retraction adjustment mechanism unit 19. As shown in Figure 13, the grease retraction adjustment unit 200a includes a learned model storage unit 204a that stores a learned 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 to the piston drive unit 9b of the retraction adjustment mechanism unit 19 by wire or wireless, 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 measuring unit 205 measures at least one of the following: hoisting machine measurement data indicating the operation status of winding up the rope 102, preset specification data, and operation data indicating past operating conditions.

[0049] The specification data storage unit 206 stores pre-set 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 202 transmits the integrated data to the numerical calculation unit 208.

[0051] The numerical calculation unit 208 calculates a target value for 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 for the retraction amount to the operation control unit 209. Here, the numerical calculation unit 208 may also calculate the axial setting position of the volume adjustment piston 5b from the target value for the retraction amount. That is, the numerical calculation unit 208 calculates a target value for the retraction amount obtained using the learned model. By inputting at least one of the hoisting machine measurement data indicating the operation state of winding up the rope 102, preset specification data, and operation data indicating past operating conditions, the numerical calculation unit 208 can output a retraction amount inferred from at least one of the hoisting machine measurement data indicating the operation state of winding up the rope 102, preset specification data, and operation data indicating past operating conditions, which were acquired by the data acquisition unit 202a.

[0052] The motion control unit 209 receives a control signal related to the target value calculated by the numerical calculation unit 208 and transmits a signal to the piston drive unit 9b to control the amount of movement of the adjustment piston so that at least one of the volume of the retraction space 10e of the grease retraction unit 10, the amount of grease retracted, and the volume of the grease storage unit 6 reaches the target value, thereby operating the piston drive unit 9b. Based on the signal received from the motion control unit 209, the piston drive unit 9b controls the position of the volume adjustment piston 5b to the set position of the volume adjustment piston 5 output from the numerical calculation unit 208. If the piston drive unit 9b is a stepping motor, the motion control unit 209 can control the rotation using the rotation angle when it rotates, so it may 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. Alternatively, even if a stepping motor is not used, for example, a distance sensor that measures the amount of axial movement of the volume adjustment piston 5b may be provided in the bearing housing 4 to confirm whether the volume adjustment piston 5b has moved the desired distance.

[0053] The grease retraction adjustment unit 200a may be located inside the hoisting machine or in a different location. For example, the grease retraction adjustment unit 200a may reside on a server, cloud, etc., and data may be acquired by the data acquisition unit 202a via wired or wireless connection.

[0054] Next, using Figure 14, we will explain the process for obtaining the target value of the evacuation amount using the evacuation amount calculation device 207.

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

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

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

[0058] In step S204, the operation control unit 209 controls the piston drive unit using the outputted target value for the retraction amount to adjust the retraction amount. This allows the lubrication state by grease to be automatically maintained in good condition based on the state or specifications of the hoisting machine.

[0059] Thus, in the hoisting machine 100 according to Embodiment 2, the target value of the retraction amount is determined based on at least one of the hoisting machine measurement data indicating the operating state of the measured rope 102 being hoisted, preset specification data, and operation data indicating past operating conditions. This makes it possible to adjust the retraction amount according to the state or specifications of the hoisting machine, and to automatically maintain good lubrication of the bearing 3.

[0060] The evacuation amount learning device 201 and the evacuation amount calculation device 207 are used to learn the evacuation amount of the hoisting machine 100, but they may be connected to the hoisting machine 100 via a network and be separate devices from the hoisting machine 100. Furthermore, the evacuation amount learning device 201 and the evacuation amount calculation device 207 may be built into the hoisting machine 100. Additionally, the evacuation amount learning device 201 and the evacuation amount calculation device 207 may reside on a cloud server. The learned model and specification data may be stored in advance and recalled according to set conditions, or they may be input by operators or other personnel.

[0061] Furthermore, although this embodiment describes the output of a target value for the amount of grease to be retracted using a learned model stored in a learned model storage unit 204a provided in the hoisting machine 100, it is also possible to obtain a learned model from an external source such as another hoisting machine 100 and output the amount of grease to be retracted to the grease retracting unit 10 based on this learned model.

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

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

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

[0065] Furthermore, by using at least one of the following as the operating data: the number of times the cage 104 is raised and lowered, the operating time, the time elapsed since grease was applied, and the grease heat cycle, 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 setting 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 setting position via the piston drive unit 9b, thereby enabling the volume adjustment piston 5b to operate accurately, and improving the accuracy of the adjustment of the retraction amount.

[0067] Embodiment 3. The hoisting machine 100 according to Embodiment 3 will be described with reference to Figures 15 to 18. In Embodiment 2, the amount of grease to be retracted was adjusted based on hoisting machine measurement data, etc., but Embodiment 3 differs in that it uses reinforcement learning to improve the accuracy of the target value of the amount of grease to be retracted. The following will mainly describe the differences from Embodiment 2, and descriptions of the same or corresponding parts will be omitted as appropriate.

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

[0069] The learning data acquisition unit 302 acquires the measured amount of grease retracted into the grease retraction unit 10 and the rotational resistance data of the electric motor 15 as learning data. The rotational resistance data of the electric motor 15 includes, for example, the current of the electric motor 15 and the torque measured by the torque meter. Here, the learning data acquisition unit 302 may acquire the volume of the grease storage unit 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 an actual measured value of the retraction amount.

[0070] The stirring resistance value setting unit 303 sets the rotational 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 the learning data including the measured retraction amount and the stirring resistance value. That is, it generates a learned model for inferring the target value of the retraction amount from the stirring resistance value of the hoist 100.

[0072] As an example of the learning algorithm used by the model generation unit 304, the case where reinforcement learning is applied will be described. In reinforcement learning, an agent (acting entity) in a certain environment observes the current state (parameters of the environment) and determines the action to be taken. The action of the agent dynamically changes the environment, and the agent is given a reward according to the change in the environment. The agent repeats this and learns the action policy that can obtain the most rewards through a series of actions. As typical methods of reinforcement learning, Q-learning and TD-learning are known. For example, in the case of Q-learning, the update formula of the action value function Q(s,a) is represented by the following formula (1).

[0073]

Equation

[0074] In Equation (1), s t represents the state of the environment at time t, and a t represents the action at time t. Due to the action a t , the state changes to s t+1 . r t+1 represents the reward obtained due to the change in that 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. The measured retraction amount becomes the action a t , the stirring resistance value becomes the state s t , and the best action a t at time t in the state s t is learned.

[0075] The update formula, represented by equation (1), increases the action value Q of action a if the action value Q of action a with the highest Q value at time t+1 is greater than the action value Q of action a performed at time t, and decreases the action value Q if the opposite is true. In other words, it updates the action value function Q(s,a) so that the action value Q of action a at time t approaches the best action value at time t+1. As a result, the best action value in a given environment is sequentially propagated to the action values ​​in previous environments.

[0076] As described above, when a trained model is generated 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 the reward based on the measured value of the retraction amount and the stirring resistance value. The reward calculation unit 305 calculates the reward r based on the stirring resistance value. For example, if the stirring resistance value decreases, the reward r is increased (for example, a reward of "1" is given), and on the other hand, if the stirring 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 escape amount according to the reward calculated by the reward calculation unit 305, and outputs it to the trained model storage unit 307. For example, in the case of Q-learning, the action value function Q(s) represented by equation 1 is used. t ,a t ) is used as a function to calculate the target value of the evacuation amount.

[0079] The learning process described above is repeated. The trained model memory unit 307 stores the action-value function Q(s) updated by the function update unit 306. t ,a t ), in other words, it memorizes the trained model.

[0080] Next, the learning process of the grease retraction adjustment unit 300 will be explained using Figure 16. Figure 16 is a flowchart relating to the learning process of the grease retraction adjustment unit 300.

[0081] In step S301, the learning data acquisition unit 302 acquires the measured value of the retraction amount 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 rotational 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 the reward based on the measured retraction amount and the stirring resistance value. Specifically, the reward calculation unit 305 obtains the measured retraction amount and the stirring resistance value and decides 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 it is appropriate to increase the reward, it increases the reward in step S304. On the other hand, if the reward calculation unit 305 determines that it is appropriate to decrease the reward, it decreases the reward in step S305.

[0085] In step S306, the function update unit 306 updates the action value function Q(s) represented by the number 1 stored in the trained model memory unit 307, based on the reward calculated by the reward calculation unit 305. t ,a t ) Update.

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

[0087] In this embodiment, the retraction amount reinforcement learning device 301 stores the learned model in a learned model storage unit 307 located inside the grease retraction adjustment unit 300. However, the learned model storage unit 307 may be located outside the grease retraction adjustment unit 300.

[0088] Next, the use of a trained model for inferring a target value for the amount of grease retraction in this embodiment will be described. Figure 17 is a diagram of the configuration of the grease retraction adjustment unit 300a according to Embodiment 3. The grease retraction adjustment unit 300a includes an environmental data acquisition unit 309 for acquiring rotational resistance data of the electric motor 15, a stirring resistance value setting unit 303a for setting the rotational resistance data of the electric motor 15 as a stirring resistance value, and a retraction amount inference device 308 having an inference unit 310 for inferring a target value for the amount of grease retraction, 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 includes, for example, the current of the electric motor 15 and torque data measured by a torque meter.

[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. However, 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 uses the trained model learned by the model generation unit 304 of the hoisting machine 100 to infer a target value for the retraction amount. 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 a target value for the retraction amount that is suitable for the rotational resistance data of the electric motor 15.

[0092] In this embodiment, the target value of the evacuation amount was described as being output using a trained model learned by the model generation unit 304 of the hoisting machine 100. However, it is also possible to obtain a trained model from another hoisting machine 100 and output the target value of the evacuation amount based on this trained model.

[0093] Next, using Figure 18, we will explain the process for obtaining the evacuation amount using the evacuation amount inference device 308.

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

[0095] In step S402, 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.

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

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

[0098] In step S405, the operation control unit 311 of the hoisting machine 100 uses the outputted target value for the retraction amount to drive the piston drive unit 9c and adjust the retraction amount via the volume adjustment piston 5c. This reduces 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 is possible to apply this to hoisting machines 100 that have a retraction adjustment mechanism 19 located in a different place, thereby reducing the grease stirring resistance in the bearings 3 of multiple hoisting machines 100.

[0100] Furthermore, by optimizing the amount of retraction using a trained model that infers a target value for the amount of retraction in the grease retraction adjustment unit 300, a hoisting machine 100 with a low rotational load on the motor at the bearing 3 can be provided.

[0101] In this embodiment, we have described the case where reinforcement learning is applied to the learning algorithm used by the inference unit 310, but this is not the only possible case. In addition to reinforcement learning, supervised learning as shown in Embodiment 2 can also be applied to the learning algorithm.

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

[0103] Furthermore, the evacuation amount reinforcement learning device 301 and the evacuation amount inference 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. Alternatively, the evacuation amount reinforcement learning device 301 and the evacuation amount inference device 308 may be built into the hoisting machine 100. Moreover, the evacuation amount reinforcement learning device 301 and the evacuation amount inference device 308 may reside on a cloud server.

[0104] Furthermore, the model generation unit 304 may learn the evacuation amount using training data acquired from multiple hoisting machines 100. The model generation unit 304 may acquire training data from multiple hoisting machines 100 used in the same area, or it may learn a target value for the evacuation amount using training data collected from multiple hoisting machines 100 operating independently in different areas. It is also possible to add or remove hoisting machines 100 from the target midway through the process. Moreover, the evacuation amount reinforcement learning device 301, which has learned a target value for the evacuation amount for one hoisting machine 100, may be applied to another hoisting machine 100, and the evacuation amount for that other hoisting machine 100 may be relearned and updated.

[0105] Furthermore, although the retraction amount inference device 308 according to this embodiment references a learned model from a learned model storage unit 307a provided inside the grease retraction adjustment unit 300a, the learned model storage unit 307a may be provided outside the grease retraction adjustment unit 300a.

[0106] While this disclosure describes various exemplary embodiments, the various features, aspects, and functions described in one or more embodiments are not limited to the application of a particular embodiment, but are applicable individually or in various combinations to the embodiments. Therefore, countless variations not illustrated are conceivable within the scope of the art disclosed herein. These include, for example, modifying, adding, or omitting at least one component, or even extracting at least one component and combining it with components from other embodiments.

[0107] The various aspects of this disclosure are summarized below as an appendix.

[0108] (Note 1) An elevator hoisting machine that winds up a rope attached to the car, Electric motor and, A sheave connected to the aforementioned electric motor and rotated by the aforementioned electric motor, A bearing that supports the aforementioned sheave, A grease reservoir is provided in which grease for lubricating the bearing is stored, A grease receptacle has a flow path that allows the grease to move back and forth between it and the grease storage section, and a grease receptacle that retracts the grease, A retraction adjustment mechanism that retracts the grease in the grease reservoir into the grease retraction section, or releases the grease from the grease retraction section into the grease reservoir; A hoisting machine for elevators equipped with [a specific feature / feature]. (Note 2) The elevator hoisting machine according to Appendix 1, wherein the retraction adjustment mechanism includes a volume adjustment piston for adjusting the volume of the grease reservoir, and a piston drive unit for causing the volume adjustment piston to reciprocate in the axial direction of the bearing. (Note 3) The bearing housing section, which houses the aforementioned bearing, The grease receptacle is detachably provided in the bearing housing and is part of the elevator hoisting machine described in Appendix 1 or Appendix 2. (Note 4) The system includes a grease retraction adjustment unit that adjusts the retraction of the grease to the grease retraction unit, The grease retraction adjustment unit is, A data acquisition unit that acquires at least one of the following data: hoisting machine measurement data indicating the operation state of winding up the measured rope, preset specification data, and operation data indicating past operating status. A numerical calculation unit calculates the target value of the amount of grease to be moved from the grease storage unit to the grease retraction unit based on the data acquired by the data acquisition unit, An operation control unit controls the operation of the retraction adjustment mechanism to control the amount of grease retracted to the calculated target value. An elevator hoisting machine as described in any one of the appendices 1 to 3, having the following: (Note 5) The elevator hoisting machine according to Appendix 4, wherein the hoisting machine measurement data is at least one of the grease temperature and the bearing temperature. (Note 6) The elevator hoisting machine described in Appendix 4 or Appendix 5, wherein the specification data is at least one of the loading capacity of the cage, the maximum volume of the grease reservoir, and the characteristic value of the grease. (Note 7) The elevator hoisting machine described in any one of the following appendices, 4 to 6, wherein the operating data is at least one of the number of times the car is raised and lowered, the operating time, the time elapsed since the grease was supplied, and the heat cycle of the grease. (Note 8) The system includes a grease retraction adjustment unit that adjusts the retraction of the grease to the grease retraction unit, The grease retraction adjustment unit is, A learning data acquisition unit acquires learning data including rotational resistance data of the electric motor and measured values ​​of the amount of grease retracted in the rotational resistance data. A stirring resistance value setting unit sets the rotational resistance data as the stirring resistance value of the grease, A model generation unit generates a trained model for inferring a target value for the amount of grease to be retracted into the grease retraction section based on the stirring resistance value, using the aforementioned training data. An elevator hoisting machine possessing any one of the items described in Appendix 1 to Appendix 7. (Note 9) The system includes a grease retraction adjustment unit that adjusts the retraction of the grease to the grease retraction unit, The grease retraction adjustment unit is, An environmental data acquisition unit that acquires rotational resistance data of the aforementioned electric motor, A stirring resistance value setting unit sets the rotational resistance data as the stirring resistance value of the grease, An inference unit that infers a target value for the amount of grease to be retracted based on the stirring resistance value, using a trained model for inferring a target value for the amount of grease to be retracted from the stirring resistance value, An operation control unit controls the operation of the retraction adjustment mechanism to control the amount of grease retracted to the inferred target value. An elevator hoisting machine having one of the features described in any one of the appendices 1 to 8. [Explanation of Symbols]

[0109] 1 Sheave, 2a, 2b, 2c Shaft section, 3 Bearing, 4 Bearing housing section, 5a, 5b, 5c Volume adjustment piston, 6 Grease reservoir section, 7 Operating mode conversion section, 8 Adjustment shaft, 9a, 9b, 9c Piston drive section, 10 Grease retraction section, 11 Seal section, 12 Temperature sensor, 13 Bolt, 14 Screw hole, 15 Electric motor, 16 End cover, 17 Front cover, 18 Adjustable support bearing, 19 Retraction adjustment mechanism section, 20 Bearing housing section body, 100 Hoisting machine, 101 Control panel, 102 Rope, 103 Counterweight, 104 Cage, 105 Deflection wheel, 200, 200a, 300, 300a Grease retraction adjustment section, 202, 202a Data acquisition unit, 204, 204a, 307, 307a; Trained model storage unit, 209, 311; Operation control unit, 302; Training 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 hoisting machine that winds up a rope attached to the car, Electric motor and, A sheave connected to the aforementioned electric motor and rotated by the aforementioned electric motor, A bearing that supports the aforementioned sheave, A grease reservoir is provided in which grease for lubricating the bearing is stored, A grease receptacle has a flow path that allows the grease to move back and forth between it and the grease storage section, and a grease receptacle that retracts the grease, A retraction adjustment mechanism that retracts the grease in the grease reservoir into the grease retraction section, or releases the grease from the grease retraction section into the grease reservoir; A hoisting machine for elevators equipped with [a specific feature / feature].

2. The elevator hoisting machine according to claim 1, wherein the retraction adjustment mechanism comprises a volume adjustment piston for adjusting the volume of the grease reservoir, and a piston drive unit for causing the volume adjustment piston to reciprocate in the axial direction of the bearing.

3. The bearing housing section, which houses the aforementioned bearing, The elevator hoisting machine according to claim 1, wherein the grease receptacle is detachably provided in the bearing housing.

4. The system includes a grease retraction adjustment unit that adjusts the retraction of the grease to the grease retraction unit, The grease retraction adjustment unit is, A data acquisition unit that acquires at least one of the following data: hoisting machine measurement data indicating the operation state of winding up the measured rope, preset specification data, and operation data indicating past operating status. A numerical calculation unit calculates a target value for the amount of grease to be moved from the grease storage unit to the grease retraction unit based on the data acquired by the data acquisition unit, An operation control unit controls the operation of the retraction adjustment mechanism to control the amount of grease retracted to the calculated target value. A hoisting machine for elevators according to claim 1, having the following:

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

6. The elevator hoisting machine according to claim 4, wherein the specification data is at least one of the loading capacity of the cage, the maximum volume of the grease reservoir, and the characteristic value of the grease.

7. The elevator hoisting machine according to claim 4, wherein the operating data is at least one of the number of times the car is raised and lowered, the operating time, the time elapsed since the grease was supplied, and the heat cycle of the grease.

8. The system includes a grease retraction adjustment unit that adjusts the retraction of the grease to the grease retraction unit, The grease retraction adjustment unit is, A learning data acquisition unit acquires learning data including rotational resistance data of the electric motor and measured values ​​of the amount of grease retracted in the rotational resistance data. A stirring resistance value setting unit sets the rotational resistance data as the stirring resistance value of the grease, A model generation unit generates a trained model for inferring a target value for the amount of grease to be retracted into the grease retraction section based on the stirring resistance value, using the aforementioned training data. A hoisting machine for elevators according to claim 1.

9. The system includes a grease retraction adjustment unit that adjusts the retraction of the grease to the grease retraction unit, The grease retraction adjustment unit is, An environmental data acquisition unit that acquires rotational resistance data of the aforementioned electric motor, A stirring resistance value setting unit sets the rotational resistance data as the stirring resistance value of the grease, An inference unit that infers a target value for the amount of grease to be retracted based on the stirring resistance value, using a trained model for inferring a target value for the amount of grease to be retracted from the stirring resistance value, An operation control unit controls the operation of the retraction adjustment mechanism to control the amount of grease retracted to the inferred target value. A hoisting machine for elevators according to claim 1, having the following:

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