Information processing method and bearing life prediction device

Abstract

To meticulously predict the life of a bearing even when a rotary electric machine used as an electric motor is reused as a power generator.SOLUTION: A prediction device according to the present embodiment calculates, by using output power from a rotary electric machine, the average load of the equivalent loads applied to a bearing in a second period during which the rotary electric machine is used as a power generator, and predicts the life of the bearing in the second period, from the calculated average load in the second period and the state of deterioration of the bearing in a first period during which the rotary electric machine is used as an electric motor.SELECTED DRAWING: Figure 4

Description

Technical Field

[0001] The present invention relates to an information processing method and a bearing life prediction device.

Background Art

[0002] Conventionally, a technique for predicting the life of a bearing that supports the rotating shaft of a rotating electrical machine has been known. For example, in Patent Document 1 listed below, an average load of a bearing is obtained from the torque of a motor, a life rotation speed is calculated from the average load and the rated load of the bearing, and a consumption rate until the bearing life is obtained from the life rotation speed and the integrated rotation speed of the motor. A technique is described.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, the above-described conventional technology can only assume a situation where the rotating electrical machine is used as a motor, and cannot accurately predict the bearing life when the rotating electrical machine is reused as a generator.

[0005] The present invention has been made in view of such circumstances on one side, and an object thereof is to provide an information processing method and a bearing life prediction device capable of accurately predicting the bearing life even when a rotating electrical machine used as a motor is reused as a generator.

Means for Solving the Problems

[0006] In order to solve the above-described problems, an information processing method according to an aspect of the present invention is an information processing method for causing a processor to execute a process of predicting the life of a bearing that supports a rotating shaft of a rotating electrical machine. The processor calculates an average load of a dynamic equivalent load applied to the bearing in a first period, which is a period during which the rotating electrical machine is used as an electric motor, from an operating state of the rotating electrical machine in the first period, estimates a first deterioration state, which is a deterioration state of the bearing in the first period, from the average load in the first period, calculates an average load of a dynamic equivalent load applied to the bearing in a second period, which is a period during which the rotating electrical machine is used as a generator, using the output power of the rotating electrical machine in the second period, and predicts the life of the bearing in the second period from the first deterioration state in the first period and the average load in the second period.

Effect of the Invention

[0007] According to the present invention, it is possible to provide an information processing method and a bearing life prediction device capable of accurately predicting the life of a bearing even when a rotating electrical machine used as an electric motor is reused as a generator.

Brief Description of the Drawings

[0008]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Mode for Carrying Out the Invention

[0009] Hereinafter, embodiments according to one aspect of the present invention (hereinafter also referred to as "the present embodiment") will be described with reference to the drawings. However, the present embodiment described below is merely an exemplification of the present invention in every aspect. Needless to say, various improvements and modifications can be made without departing from the scope of the present invention. That is, in carrying out the present invention, a specific configuration according to the embodiment may be appropriately adopted. In the present embodiment, the data that appears is described in natural language, but more specifically, it is specified by a pseudo-language, command, parameter, machine language, etc. that can be recognized by a computer. Further, the description "x / y" may be used in the meaning of "at least one of x and y".

[0010] §1 Application Example FIG. 1 shows an overall overview of a bearing life prediction system (bearing life prediction system Sys) according to the present embodiment. The bearing life prediction system Sys includes a prediction device 1 and a rotating electric machine 2. The prediction device 1 is a bearing life prediction device according to the present embodiment, and predicts the life of a bearing 22 (bearing) that supports the rotating shaft 21 of the rotating electric machine 2. The rotating electric machine 2 operates as an electric motor (motor) or a generator, and in the present embodiment, it is a rotating electric machine that can be (re)used as a generator after being used as an electric motor of a vehicle. The rotating electric machine 2 can be used, for example, as a generator of renewable energy such as wind power generation, hydropower generation, wave power generation, and geothermal power generation. In the present embodiment, the "period during which the rotating electric machine 2 is used as an electric motor" may be referred to as the "first period FP". Similarly, the "period during which the rotating electric machine 2 is used as a generator (including at least one of the period during which the rotating electric machine 2 is to be used as a generator and the period during which the rotating electric machine 2 is being used as a generator)" may be referred to as the "second period SP".

[0011] In the illustrated example, the prediction device 1 acquires the operating state AS of the rotating electrical machine 2 from the rotating electrical machine 2, and also receives the output power OP of the rotating electrical machine 2 when the rotating electrical machine 2 is used as a generator. For example, the prediction device 1 uses the acquired (input) operating state AS and output power OP of the rotating electrical machine 2 to predict the life of the bearing 22, particularly the life of the bearing 22 when the rotating electrical machine 2 is reused as a generator after being used as a motor (i.e., in the second period SP).

[0012] The operating state AS includes, for example, at least one of the (total) operating time, (total / average) rotational speed, (average) rotation speed, and (average) torque of the rotating electrical machine 2. The operating state AS may further include information related to the setting and design of the rotating electrical machine 2 (for example, quantitative information such as the gear ratio of the rotating electrical machine 2). Note that the "operating state AS in the first period FP" may be particularly referred to as the "first operating state FAS". The first operating state FAS includes, for example, the (total) driving distance of a vehicle using the rotating electrical machine 2 as a motor. The first operating state FAS may further include information related to the setting and design of such a vehicle (for example, quantitative information such as the tire circumference (L) of such a vehicle). That is, the first operating state FAS may include the state of the vehicle using the rotating electrical machine 2 as a motor in addition to the operating state AS. The output power OP may be an expected value, anticipated value, set value, etc. of the output power of the rotating electrical machine 2 during the period when the rotating electrical machine 2 is to be used as a generator, or may be the measured value (average value) of the output power of the rotating electrical machine 2 during the period when the rotating electrical machine 2 is being used as a generator.

[0013] FIG. 1 shows an example in which the prediction device 1 and the rotating electrical machine 2 are configured as separate devices. When a vehicle that uses the rotating electrical machine 2 as an electric motor includes the separately configured prediction device 1 and the rotating electrical machine 2, the prediction device 1 and the rotating electrical machine 2 may be connected to each other via a CAN (Controller Area Network) or other in-vehicle LAN and may transmit and receive information to and from each other. However, for the bearing life prediction system Sys, it is not essential to configure the prediction device 1 and the rotating electrical machine 2 as separate devices, and they may be integrally configured. For example, the processor (CPU) that executes the process of detecting and controlling the operating state AS of the rotating electrical machine 2 included in the rotating electrical machine 2 and the processor that executes the process of predicting the life of the bearing 22 included in the prediction device 1 may be the same processor.

[0014] The processor that executes the process of predicting the life of the bearing 22 may be included in a vehicle that uses the rotating electrical machine 2 as an electric motor. By including a vehicle that uses the rotating electrical machine 2 as an electric motor with a processor that executes the process of predicting the life of the bearing 22, the prediction device 1 (the information processing method PM that causes such a processor to execute the process of predicting the life of the bearing 22) has, for example, the following effects. That is, the prediction device 1 (the information processing method PM) can predict the life of the bearing 22 when the rotating electrical machine 2 is reused as a generator while the rotating electrical machine 2 is still installed in the vehicle. Therefore, the prediction device 1 can provide information for the user to appropriately determine, for example, the suitability of subsequent reuse as a generator, the method of use as a generator, and the usage form (including the installation position, power generation method, etc.) for the rotating electrical machine 2 that is still installed in the vehicle. The users include the user of the rotating electrical machine 2 (for example, the owner of a vehicle that uses the rotating electrical machine 2 as an electric motor) and the person planning to use the rotating electrical machine 2 (for example, a business operator considering reusing the rotating electrical machine 2 as a generator). However, for the bearing life prediction system Sys, it is not essential for the vehicle to include the prediction device 1, and the prediction device 1 may be configured independently of the vehicle.

[0015] §2 Configuration Example [Hardware Configuration] Figure 2 schematically illustrates an example of the hardware configuration of the prediction device 1 according to the present embodiment. As shown in Figure 2, the prediction device 1 according to the present embodiment is a computer in which a control unit 11, a storage unit 12, a communication interface 13, an external interface 14, an input device 15, an output device 16, and a drive 17 are electrically connected. In Figure 2, the communication interface and the external interface are described as "communication I / F" and "external I / F".

[0016] The control unit 11 includes a CPU (Central Processing Unit), which is a hardware processor, a RAM (Random Access Memory), a ROM (Read Only Memory), etc., and is configured to execute information processing based on programs and various data. The CPU is an example of a processor resource. The storage unit 12 is an example of a memory resource and is composed of, for example, a hard disk drive, a solid state drive, etc. In the present embodiment, the storage unit 12 stores various information such as a prediction program 120, a fatigue life-load correlation formula 122, and torque-rotation characteristic information 124.

[0017] The prediction program 120 is a program for causing the prediction device 1 to execute information processing (Figure 4) described later for predicting the life of the bearing 22 that supports the rotating shaft 21 of the rotating electrical machine 2. The prediction program 120 includes a series of instructions for the information processing.

[0018] The fatigue life-load correlation formula 122 is a formula that defines the relationship (correlation relationship) between the fatigue life (rated fatigue rotation speed) and the load (average load) for the bearing 22, and may be a curve (correlation curve) plotting the relationship between the two. The fatigue life-load correlation formula 122 (fatigue life-load correlation curve) illustrated in Figures 5 and 6 shows the correlation relationship between the fatigue life (L0.5 rated fatigue rotation speed / × 10 9 ) and the load (average load / N) for the bearing 22. In the present embodiment, the fatigue life-load correlation formula 122 may be abbreviated as "correlation formula 122".

[0019] Torque-rotation characteristic information 124 is information (e.g., an equation) indicating the relationship (correlation) between torque and rotational characteristics (rotational speed, rotational velocity, etc.) for the rotating electrical machine 2, and may be a curve (correlation curve) plotting the relationship between the two. In the present embodiment, the torque-rotation characteristic information 124 may be abbreviated as "characteristic information 124".

[0020] The communication interface 13 is, for example, a wired LAN (Local Area Network) module, a wireless LAN module, etc., and is an interface for performing wired or wireless communication via a network. As described above, the communication interface 13 may be an interface for performing communication via CAN or other in-vehicle LANs. The prediction device 1 may execute data communication via the network with other information processing devices using this communication interface 13. The external interface 14 is, for example, a USB (Universal Serial Bus) port, a dedicated port, etc., and is an interface for connecting to an external device. The type and number of the external interfaces 14 may be appropriately selected according to the type and number of the external devices to be connected. For example, the prediction device 1 is connected to the rotating electrical machine 2 via at least one of the communication interface 13 and the external interface 14, and acquires the operating state AS of the rotating electrical machine 2 from the rotating electrical machine 2.

[0021] The input device 15 is, for example, a device for performing inputs such as a mouse and a keyboard. Also, the output device 16 is, for example, a device for performing outputs such as a display and a speaker. An operator such as a user can operate the prediction device 1 by using the input device 15 and the output device 16.

[0022] The drive 17 is, for example, a CD drive, a DVD drive, etc., and is a drive device for reading various information such as programs stored in the storage medium 91. The storage medium 91 is a medium that accumulates information such as programs by means of electrical, magnetic, optical, mechanical, or chemical actions so that computers and other devices and machines can read the stored various information. At least one of the above-mentioned prediction program 120, correlation formula 122, and characteristic information 124 may be stored in the storage medium 91. The prediction device 1 may acquire at least one of the prediction program 120, correlation formula 122, and characteristic information 124 from this storage medium 91. In FIG. 2, as an example of the storage medium 91, a disk-type storage medium such as a CD or a DVD is illustrated. However, the type of the storage medium 91 may not be limited to the disk type and may be other than the disk type. Examples of storage media other than the disk type include semiconductor memories such as flash memories. The type of the drive 17 may be arbitrarily selected according to the type of the storage medium 91.

[0023] Regarding the specific hardware configuration of the prediction device 1, components can be appropriately omitted, replaced, and added according to the embodiment. For example, the processor resources may include a plurality of hardware processors. The hardware processor may be composed of a microprocessor, an FPGA (field-programmable gate array), a DSP (digital signal processor), etc. The storage unit 12 may be composed of a RAM and a ROM included in the control unit 11. At least one of the communication interface 13, the external interface 14, the input device 15, the output device 16, and the drive 17 may be omitted. The prediction device 1 may be composed of a plurality of computers. In this case, the hardware configurations of the respective computers may or may not match. Further, the prediction device 1 may be a general-purpose server device, a PC (Personal Computer), etc., in addition to an information processing device designed specifically for the provided service.

[0024] [Software Configuration] FIG. 3 schematically illustrates an example of the software configuration of the prediction device 1 according to the present embodiment. The control unit 11 of the prediction device 1 expands the prediction program 120 stored in the storage unit 12 into the RAM. Then, the control unit 11 interprets and executes the instructions included in the prediction program 120 expanded in the RAM by the CPU to control each component. As a result, as shown in FIG. 3, the prediction device 1 according to the present embodiment operates as a computer including a first average load calculation unit 110, a deterioration state estimation unit 112, a determination unit 114, a second average load calculation unit 116, and a prediction unit 118 as software modules. That is, in the present embodiment, each software module of the prediction device 1 is realized by the control unit 11 (CPU).

[0025] The first average load calculation unit 110 acquires the operating state AS of the rotating electric machine 2 (that is, the first operating state FAS) in the first period FP, and calculates the average load AL of the dynamic equivalent load applied to the bearing 22 in the first period FP from the acquired first operating state FAS. The dynamic equivalent load is the sum of the radial load and the axial load. In the present embodiment, the "average load AL of the dynamic equivalent load applied to the bearing 22 in the first period FP" is also referred to as the "first average load FAL". The first average load calculation unit 110 may acquire the first operating state FAS from the rotating electric machine 2, or may acquire it from a device that detects the first operating state FAS of the rotating electric machine 2, a server that stores the first operating state FAS, etc. The acquisition source of the first operating state FAS is not particularly limited.

[0026] The deterioration state estimation unit 112 estimates a first deterioration state FDS, which is the deterioration state of the bearing 22 in the first period FP, from the first average load FAL. For example, the deterioration state estimation unit 112 refers to the storage unit 12 to obtain a correlation formula 122 (fatigue life - load correlation formula of the bearing 22), and calculates the basic rating life (number of rotations) of the bearing 22 in the first period FP from the obtained correlation formula 122 and the first average load FAL. Then, the deterioration state estimation unit 112 may estimate, as the first deterioration state FDS, the consumption rate (or remaining rate) [%] of the (integrated) number of rotations of the bearing 22 in the first period FP with respect to the "basic rating life (number of rotations) of the bearing 22 in the first period FP" calculated. The above-mentioned "remaining rate" is also referred to as the "remaining life rate" in this embodiment. The "remaining rate (remaining life rate) of the 'number of rotations of the bearing 22 in the first period FP' with respect to the 'basic rating life (number of rotations) of the bearing 22 in the first period FP'" is an example of information indicating the first deterioration state FDS. However, the method by which the deterioration state estimation unit 112 estimates the first deterioration state FDS from the first average load FAL is not limited to the above method.

[0027] The determination unit 114 determines whether the use of the rotating electrical machine 2 has been changed from a motor to a generator based on the operating state AS of the rotating electrical machine 2. For example, the determination unit 114 may determine the change in the use of the rotating electrical machine 2 using the load (at least one of the radial load and the axial load) applied to the rotating electrical machine 2 (bearing 22). For example, when the average load becomes smaller than a predetermined value, it may be determined that there has been a change. The determination unit 114 may also determine the change in the use of the rotating electrical machine 2 using the load (average load) with respect to the number of rotations. For example, when the average load with respect to the number of rotations becomes smaller than a predetermined value, it may be determined that there has been a change. However, the method of determining the change in the use of the rotating electrical machine 2 from the operating state AS is not limited to the one described above.

[0028] As will be described in detail later, when the determination unit 114 determines that the use of the rotating electrical machine 2 has been changed (will be changed) from a motor to a generator, the prediction device 1 predicts the deterioration (such as wear) of the bearing 22 during the second period SP (that is, the period during which the rotating electrical machine 2 is used as a generator). Then, the prediction device 1 predicts the life of the bearing 22 during the second period SP based on the deterioration of the bearing 22 during the first period FP (that is, the period during which the rotating electrical machine 2 is used as a generator) (the first deterioration state FDS) and the deterioration of the bearing 22 during the second period SP. Therefore, by determining whether the use of the rotating electrical machine 2 has been changed from a motor to a generator based on the operating state AS of the rotating electrical machine 2, the prediction device 1 has the following effects. That is, the prediction device 1 can accurately determine whether the use of the rotating electrical machine 2 has been changed from a motor to a generator based on the operating state AS of the rotating electrical machine 2. When it is determined that the use has been changed, the prediction device 1 can automatically and precisely predict the life of the bearing 22 in the generator application.

[0029] Note that it is not essential for the prediction device 1 to include the determination unit 114. That is, it is not essential for the prediction device 1 to determine whether the use of the rotating electrical machine 2 has been changed (will be changed) from a motor to a generator. For example, the prediction device 1 may obtain information indicating that the use of the rotating electrical machine 2 has been changed (will be changed) from a motor to a generator from an external device, or may receive a user operation indicating such a change in use. When such information, user operation is obtained or received, the prediction device 1 may execute a process of predicting the life of the bearing 22 during the second period SP.

[0030] When the second average load calculation unit 116 is notified by the determination unit 114 of the determination result that "the use of the rotating electrical machine 2 has been changed from a motor to a generator", the second average load calculation unit 116 calculates the average load AL of the dynamic equivalent load applied to the bearing 22 in the second period SP using the output power OP of the rotating electrical machine 2. In the present embodiment, the "average load AL of the dynamic equivalent load applied to the bearing 22 in the second period SP" is also referred to as the "second average load SAL". The second average load calculation unit 116 may acquire the output power OP from the rotating electrical machine 2, or may acquire it from an external device that uses the rotating electrical machine 2 as a generator, or may acquire it as an input from a person who (re)uses the rotating electrical machine 2 as a generator (intends to (re)use it). The acquisition source of the output power OP is not particularly limited, similar to the acquisition source of the first operating state FAS.

[0031] For the prediction device 1, it is not essential for the second average load calculation unit 116 to calculate the second average load SAL using the output power OP of the rotating electrical machine 2 triggered by the determination result of "the use of the rotating electrical machine 2 has been changed from a motor to a generator" by the determination unit 114. When the second average load calculation unit 116 acquires the output power OP (or various parameters capable of determining the output power OP described later) or receives a user operation for inputting the output power OP, the second average load calculation unit 116 may calculate the second average load SAL using the output power OP.

[0032] The second average load calculation unit 116 may obtain the characteristic information 124 (torque-rotation characteristic of the rotating electrical machine 2) by referring to the storage unit 12, and calculate the second average load SAL from the obtained characteristic information 124 and the output power OP. For example, from the characteristic information 124 and the "relation formula of torque, rotation speed, and output: H = T×(2πN / 60) [H: output, N: rotation speed, T: torque]", the torque of the rotating electrical machine 2 in the second period SP can be calculated based on the output power OP (H: output). Then, from the calculated torque of the rotating electrical machine 2 in the second period SP, the average load AL of the bearing 22 in the second period SP, that is, the second average load SAL, can be obtained. By calculating the second average load SAL in such a manner, the second average load calculation unit 116 can accurately calculate the second average load SAL from the output power OP and the characteristic information 124. However, the method by which the second average load calculation unit 116 calculates the second average load SAL using the output power OP is not limited to the above-described one.

[0033] When the rotating electrical machine 2 is used as a generator in wind power generation, the output power OP may be determined using at least one of the power generation efficiency wind speed of the windmill, the windward area of the blade, the air density, and the power coefficient of the windmill in such wind power generation. For example, from the power generation efficiency wind speed [V] of the windmill, the windward area [A] of the blade, the air density [ρ], and the power coefficient [Cp] of the windmill, the output power OP may be calculated as "output power OP = Cp×(1 / 2)×ρ×A×V 3 ". The prediction device 1 may include an output power calculation unit (not shown), and the output power calculation unit may calculate the output power OP from the above-described various variables, and the calculated output power OP may be used by the second average load calculation unit 116. By determining (calculating) the output power OP using at least one of the power generation efficiency wind speed of the windmill, the windward area of the blade, the air density, and the power coefficient of the windmill in wind power generation where the rotating electrical machine 2 is used as a generator, the prediction device 1 has the following effects. That is, the prediction device 1 can accurately determine the output power OP from the above-described variables (various parameters capable of determining the output power OP) even when, for example, the output power OP is unknown.

[0034] When the rotating electrical machine 2 is used as a generator in hydropower generation, the output power OP may be determined using at least one of the water volume applied to the waterwheel, the gravitational acceleration, the water head, and the efficiency in such hydropower generation. For example, from the water volume [Q] applied to the waterwheel, the gravitational acceleration [g], the water head [h], and the efficiency [η h , the output power OP may be calculated as "output power OP = Q × g × h × η h ". Similar to the case of wind power generation, an output power calculation unit (not shown) may calculate the output power OP of the hydropower generation that uses the rotating electrical machine 2 as a generator from the above-described various variables, and the second average load calculation unit 116 may use the calculated output power OP.

[0035] When the rotating electrical machine 2 is used as a generator in wave power generation, the output power OP may be determined using at least one of the wave power, the wave width, and the efficiency in such wave power generation. For example, from the wave power [P], the wave width [B], and the efficiency [η w , the output power OP may be calculated as "output power OP = P × B × η w ". Similar to the case of wind power generation, an output power calculation unit (not shown) may calculate the output power OP of the wave power generation that uses the rotating electrical machine 2 as a generator from the above-described various variables, and the second average load calculation unit 116 may use the calculated output power OP.

[0036] When the rotating electrical machine 2 is used as a generator in geothermal power generation, the output power OP may be determined using at least one of the steam flow rate, the inlet and outlet temperature, the latent heat of steam, and the efficiency in such geothermal power generation. For example, from the steam flow rate [Q], the inlet and outlet temperature [T], the latent heat of steam [h], and the efficiency [η b , the output power OP may be calculated as "output power OP = Q × T × h × η b ". Similar to the case of wind power generation, an output power calculation unit (not shown) may calculate the output power OP of the geothermal power generation that uses the rotating electrical machine 2 as a generator from the above-described various variables, and the second average load calculation unit 116 may use the calculated output power OP.

[0037] The prediction unit 118 predicts the life of the bearing 22 in the second period SP from the first deterioration state FDS and the second average load SAL. In the present embodiment, the remaining life of the bearing 22 in the second period SP is predicted. The remaining life of the bearing 22 in the second period SP may be, for example, the time (number of years) from the start point of (re)use of the rotating electrical machine 2 as a generator to the basic rated life (number of rotations) of the bearing 22. The prediction unit 118 outputs the predicted "remaining life of the bearing 22 in the second period SP" and displays it on, for example, an output device 16 (display device).

[0038] For example, the prediction unit 118 estimates the second deterioration state SDS, which is the deterioration state of the bearing 22 in the second period SP, from the second average load SAL. For example, the prediction unit 118 refers to the storage unit 12 to obtain the correlation formula 122, and calculates the basic rated life (number of rotations) of the bearing 22 in the second period SP as the second deterioration state SDS from the obtained correlation formula 122 and the second average load SAL. Then, the prediction unit 118 may predict the life of the bearing 22 in the second period SP, for example, the remaining life of the bearing 22 in the second period SP, from the calculated second deterioration state SDS and the first deterioration state FDS. As described above, the first deterioration state FDS is, for example, the residual rate (remaining life rate) [%] of the "number of rotations of the bearing 22 in the first period FP" with respect to the "basic rated life (number of rotations) of the bearing 22 in the first period FP". Therefore, the prediction unit 118 may multiply such a residual rate by the second deterioration state SDS (for example, the basic rated life (number of rotations) of the bearing 22 in the second period SP) to predict the remaining life (number of rotations) of the bearing 22 in the second period SP as the "life of the bearing 22 in the second period SP". The prediction unit 118 may convert the predicted "remaining life (number of rotations) of the bearing 22 in the second period SP" into the remaining life (years). In the present embodiment, the "remaining life of the bearing 22 in the second period SP" is also referred to as the "second remaining life SRL", and the second remaining life SRL is, for example, the second remaining life SRL (number of rotations) or the second remaining life SRL (years), and is an example of the information indicating the "life of the bearing 22 in the second period SP".

[0039] §3 Operation Example FIG. 4 is a flowchart showing an example of the processing procedure of the prediction device 1 according to the present embodiment. The processing procedure described below is an example of the processing procedure of the information processing method PM for "predicting the life of the bearing 22 that supports the rotating shaft 21 of the rotating electrical machine 2". However, the processing procedure described below is only an example, and each step may be changed as much as possible. Further, with respect to the processing procedure described below, steps may be omitted, replaced, and added as appropriate according to the embodiment.

[0040] (Step S110) In step S110, the control unit 11 operates as the first average load calculation unit 110 and calculates the first average load FAL from the first operating state FAS. For example, the control unit 11 acquires the operating state AS (first operating state FAS) of the rotating electrical machine 2 in the first period FP from the rotating electrical machine 2, and calculates the first average load FAL from the acquired first operating state FAS.

[0041] (Step S120) In step S120, the control unit 11 operates as the deterioration state estimation unit 112 and estimates the first deterioration state FDS from the first average load FAL calculated in step S110. For example, the control unit 11 estimates the first deterioration state FDS from the first average load FAL and the correlation formula 122 (fatigue life - load correlation formula of the bearing 22). For example, the control unit 11 estimates, as the first deterioration state FDS, the remaining rate (remaining life rate) [%] of the "accumulated rotation number" of the bearing 22 in the first period FP with respect to the "basic rated life (rotation number) of the bearing 22 in the first period FP" calculated from the correlation formula 122 and the first average load FAL.

[0042] (Step S130) In step S130, the control unit 11 operates as a determination unit 114 and determines whether the use of the rotating electrical machine 2 has been changed from a motor to a generator based on the operating state AS of the rotating electrical machine 2. The control unit 11 may determine whether it has acquired information indicating that the use of the rotating electrical machine 2 has been (will be) changed from a motor to a generator from an external device. Further, the control unit 11 may determine whether it has received a user operation indicating a change in the use of the rotating electrical machine 2 from a motor to a generator. The user operation indicating a change in the use of the rotating electrical machine 2 from a motor to a generator may be an input of the output power OP, or may be an input of a variable or parameter that can determine the output power OP. When it is determined that the use of the rotating electrical machine 2 has been changed from a motor to a generator (the above information has been acquired, the above user operation has been received) (Yes in S130), the control unit 11 proceeds to step S140. When it is determined that the use of the rotating electrical machine 2 has not been changed from a motor to a generator (the above information has not been acquired, the above user operation has not been received) (No in S130), the control unit 11 proceeds to step S160.

[0043] (Step S140) In step S140, the control unit 11 operates as a second average load calculation unit 116 and calculates a second average load SAL using the output power OP. The control unit 11 may calculate the second average load SAL from the output power OP and the characteristic information 124 (torque-rotation characteristic of the rotating electrical machine 2).

[0044] (Step S150) In step S150, the control unit 11 operates as a prediction unit 118, and predicts the life of the bearing 22 in the second period SP from the first deterioration state FDS estimated in step S120 and the second average load SAL calculated in step S140. The control unit 11 predicts, for example, the remaining life of the bearing 22 in the second period SP (the second remaining life SRL (number of rotations) or the second remaining life SRL (years)). For example, the control unit 11 first calculates the basic rated life (number of rotations) of the bearing 22 in the second period SP from the correlation formula 122 and the second average load SAL. Then, the control unit 11 may multiply the calculated "basic rated life (number of rotations) of the bearing 22 in the second period SP" by the first deterioration state FDS (for example, the remaining ratio [%]) estimated in step S120 to predict the second remaining life SRL (number of rotations). The control unit 11 may convert the predicted second remaining life SRL (number of rotations) into the second remaining life SRL (years).

[0045] (Step S160) In step S160, the control unit 11 operates as a deterioration state estimation unit 112, predicts the life of the bearing 22 from the first deterioration state FDS estimated in step S120, and particularly predicts the life of the bearing 22 in the first period FP. For example, the control unit 11 may predict the remaining life (number of rotations) of the bearing 22 in the first period FP from the "basic rated life (number of rotations) of the bearing 22 in the first period FP" calculated from the first average load FAL and the correlation formula 122 and the "number of rotations of the bearing 22 in the first period FP". The control unit 11 may convert the predicted "remaining life (number of rotations) of the bearing 22 in the first period FP" into the "remaining life (years) of the bearing 22 in the first period FP".

[0046] <Specific Example of Information Processing for Predicting the Life of a Bearing Supporting the Rotating Shaft of a Rotating Electrical Machine> Using FIGS. 5 and 6, a specific example of applying the information processing method PM to the following assumed cases will be described. The assumed case is an example in which the rotating electrical machine 2 that has been used as an electric motor of a vehicle with a (total) travel distance of "150,000 [km]" is to be reused as a generator for hydroelectric power generation (small-scale hydroelectric power generation) with an output power OP of "7.5 [kW]".

[0047] (Estimation of Bearing Deterioration State during the First Period) First, the prediction device 1 acquires the operating state AS of the rotating electrical machine 2, and particularly acquires the operating state AS (i.e., the first operating state FAS) during the first period FP. For example, the prediction device 1 acquires the "gear ratio: 8.193" of the rotating electrical machine 2 as the operating state AS, and also acquires at least the "tire circumference (L): 1.99 [m]" and "travel distance: 150,000 [km]" of the above-mentioned vehicle as the first operating state FAS.

[0048] The prediction device 1 (the first average load calculation unit 110) calculates the first average load FAL from the acquired operating state AS (particularly, the first operating state FAS). In the assumed case, the above-mentioned vehicle has been used as a company car, for example, and the prediction device 1 calculates the "first average load FAL: 620 [N]" from the first operating state FAS.

[0049] Next, the prediction device 1 (the deterioration state estimation unit 112) obtains the "fatigue life (L0.5 life) during the first period FP" from the "first average load FAL: 620 [N]". For example, the prediction device 1 obtains, as shown in FIG. 5, the "fatigue life during the first period FP: 3×10 9 [revolutions]" from the "first average load FAL: 620 [N]" and the correlation formula 122 (the fatigue life - load correlation formula of the bearing 22).

[0050] In addition, the prediction device 1 calculates the rotation speed of the bearing 22 for the rotating electrical machine 2 that has been used as the electric motor of the vehicle with a "travel distance: 150,000 [km]", that is, calculates the (integrated) rotation speed (the first rotation speed) of the bearing 22 during the first period FP. In the assumed case, since the "gear ratio: 8.193" of the rotating electrical machine 2 and the "tire circumference: 1.99 [m]" of the vehicle, the prediction device 1 calculates "150,000×10 3 [m] / 1.99 [m / revolution]×8.193 = 0.6×10 9 [revolutions]" as the first rotation speed.

[0051] The prediction device 1 has the "fatigue life during the first period FP: 3×10 9"[Revolutions]" and "First Rotation Speed: 0.6×10 9 From "[Revolutions]" and "First Rotation Speed: 0.6×10 9 [Revolutions] / 3×10 9 [Revolutions]×100 = 80 [%]" is taken as the first remaining life rate. The first remaining life rate is a rephrasing of "the remaining rate (remaining life rate) of the 'rotation speed of bearing 22 in the first period FP' with respect to the 'basic rated life (rotation speed) of bearing 22 in the first period FP'".

[0052] (Prediction of Bearing Life in the Second Period) The prediction device 1 acquires the output power OP (as described above, "7.5 kW") of the rotating electrical machine 2 that is reused as a generator for small hydropower generation. The prediction device 1 (second average load calculation unit 116) calculates the second average load SAL using the output power OP, and in the assumed case, calculates "Second Average Load SAL: 152 [N]". The prediction device 1 may calculate "Second Average Load SAL: 152 [N]" from the output power OP and the characteristic information 124. The prediction device 1 may also acquire (or calculate) "208 [rpm]" as the rotation speed (average rotation speed) of the rotating electrical machine 2 in the second period SP.

[0053] The prediction device 1 (prediction unit 118) obtains the "fatigue life (L0.5 life) in the second period SP" from the "Second Average Load SAL: 152 [N]". For example, the prediction device 1 obtains "Fatigue Life in the Second Period SP: 19×10 9 [Revolutions]" as illustrated in FIG. 6 from the "Second Average Load SAL: 152 [N]" and the correlation formula 122.

[0054] The prediction device 1 multiplies the fatigue life in the second period SP by the first remaining life rate to estimate (calculate) the remaining life of the bearing 22 in the second period SP, that is, the second remaining life SRL (rotation speed). As described above, "Fatigue Life in the Second Period SP: 19×10 9 [Revolutions]" and "First Remaining Life Rate: 80 [%]", so the prediction device 1 calculates "19×109 [Number of revolutions] × 80 [%] = 15 × 10 9 Let "[Number of revolutions]" be the second remaining life SRL (number of revolutions).

[0055] The prediction device 1 may convert the second remaining life SRL (number of revolutions) into years (RUL, remaining useful life), that is, it may convert it into the second remaining life SRL (years). In the assumed case, "Average number of revolutions of the rotating electrical machine 2 in the second period SP: 208 [rpm]" and "Gear ratio: 8.193". Therefore, the prediction device 1 calculates "[15 × 10 9 [Number of revolutions] / (208 [rpm] × 8.193) / 60 [min] / 24 [h] / 365 [day] = 17 [years]" as the second remaining life [years].

[0056] [Features] As described above, the prediction device 1 (bearing life prediction device) according to the present embodiment is a bearing life prediction device that predicts the life of the bearing that supports the rotating shaft 21 of the rotating electrical machine 2. The prediction device 1 includes a first average load calculation unit 110, a deterioration state estimation unit 112, a second average load calculation unit 116, and a prediction unit 118. The first average load calculation unit 110 calculates the average load AL (that is, the first average load FAL) of the dynamic equivalent load applied to the bearing 22 in the first period FP from the operating state AS of the rotating electrical machine 2 in the first period FP (that is, the first operating state FAS). The first period FP is the period during which the rotating electrical machine 2 is used as a motor. The deterioration state estimation unit 112 estimates the first deterioration state FDS, which is the deterioration state of the bearing 22 in the first period FP, from the first average load FAL. The second average load calculation unit 116 calculates the average load AL (that is, the second average load SAL) of the dynamic equivalent load applied to the bearing 22 in the second period SP using the output power OP of the rotating electrical machine 2 in the second period SP. The second period SP is the period during which the rotating electrical machine 2 is used as a generator. The prediction unit 118 predicts the life of the bearing 22 in the second period SP from the first deterioration state FDS and the second average load SAL, and for example, predicts the remaining life of the bearing 22 in the second period SP (the second remaining life SRL (number of revolutions) or the second remaining life SRL (years)).

[0057] The information processing method PM (information processing method) according to this embodiment is an information processing method for causing a processor (for example, the CPU of the prediction device 1) to execute a process of predicting the life of the bearing 22 that supports the rotating shaft 21 of the rotating electrical machine 2. Such a processor executes the above-described steps S110, S120, S140, and S150. That is, in step S110, the processor calculates the first average load FAL from the first operating state FAS. In step S120, the processor estimates the first deterioration state FDS from the first average load FAL. In step S140, the processor calculates the second average load SAL using the output power OP of the rotating electrical machine 2 used as a generator. In step S150, the processor predicts the life of the bearing 22 in the second period SP from the first deterioration state FDS and the second average load SAL.

[0058] According to this configuration, the prediction device 1 (information processing method PM) predicts the life of the bearing 22 in the second period SP from the deterioration of the bearing 22 (first deterioration state FDS) in the first period FP and the subsequent deterioration of the bearing 22 in the second period SP. In particular, for the deterioration of the bearing 22 in the first period FP (first deterioration state FDS), the prediction device 1 estimates it from the operating state AS (first operating state FAS) of the rotating electrical machine 2 in the first period FP, and in particular, from the first average load FAL corresponding to the first operating state FAS. Further, for the deterioration of the bearing 22 in the second period SP, the prediction device 1 predicts it from the output power OP of the rotating electrical machine 2 in the second period SP, and in particular, from the second average load SAL corresponding to the output power OP. Therefore, the prediction device 1 can accurately estimate the deterioration of the bearing 22 in the first period FP using the first operating state FAS, and can also accurately predict the deterioration of the bearing 22 in the second period SP using the output power OP. And the prediction device 1 can accurately predict (estimate) the life of the bearing 22 in the second period SP from the "deterioration of the bearing 22 in the first period FP" and the "deterioration of the bearing 22 in the second period SP" respectively estimated (predicted) with high accuracy. Therefore, the prediction device 1 can accurately predict the life of the bearing 22 even when the rotating electrical machine 2 used as an electric motor is reused as a generator. The prediction device 1 predicts the life of the bearing 22 in the second period SP, for example, as the remaining life of the bearing 22 (second remaining life SRL (number of rotations) or second remaining life SRL (years)) in the second period SP. Therefore, the prediction device 1 can provide information related to the remaining life of the bearing 22 (rotating electrical machine 2) when the rotating electrical machine 2 is reused as a generator to the user ((re)user (planned (re)user) etc.) of the rotating electrical machine 2. The user can use the above-mentioned information provided by the prediction device 1 to judge the suitability of reusing the rotating electrical machine 2 as a generator, make an appropriate evaluation (such as price) as a generator, and select a suitable reuse application (for example, installation location, power generation method, etc.).

[0059] FIG. 7 shows an example in which the prediction device 1 (information processing method PM) estimates the first degradation state FDS from the first average load FAL in the first period FP and estimates the second degradation state SDS from the second average load SAL in the second period SP. In the example shown in FIG. 7, the use of the rotating electrical machine 2 is changed at time t = T. That is, in the first period FP, the rotating electrical machine 2 is used as "an electric motor for driving an electric vehicle", and in the second period SP, the use of the rotating electrical machine 2 is changed at time t = T so that the rotating electrical machine 2 is used as "a generator for renewable energy power generation". The prediction device 1 (information processing method PM) may determine such a change in use from the operating state AS of the rotating electrical machine 2. In other words, the change in the use of the rotating electrical machine 2 may be detected based on the operating state AS of the rotating electrical machine 2. Then, the prediction device 1 (information processing method PM) estimates the first degradation state FDS from the first average load FAL in the first period FP and estimates the second degradation state SDS from the second average load SAL in the second period SP. For example, for the first period FP, the prediction device 1 estimates the first degradation state FDS from the first average load FAL corresponding to the operating state AS (first operating state FAS) of the rotating electrical machine 2 in the first period FP. Also, for the second period SP, the prediction device 1 estimates (predicts) the second degradation state SDS from the second average load SAL. In the present embodiment, an example in which the second average load SAL is calculated from the output power OP of the rotating electrical machine 2 in the second period SP has been described. However, the prediction device 1 (information processing method PM) may calculate the second average load SAL from the operating state AS of the rotating electrical machine 2 in the second period SP. For example, when the rotating electrical machine 2 is already used as a generator, the bearing life prediction system Sys may detect and acquire the operating state AS of the rotating electrical machine 2 during use as such a generator. Then, the prediction device 1 (information processing method PM) may calculate the second average load SAL from the operating state AS (for example, a detected value, a measured value) of the rotating electrical machine 2 during use as a generator, that is, from the operating state AS of the rotating electrical machine 2 in the second period SP.

[0060] §4 Modification Example Although the embodiments of the present invention have been described in detail above, the description up to the foregoing is merely illustrative of the present invention in every aspect. Needless to say, various improvements or modifications can be made without departing from the scope of the present invention. For example, the following changes are possible. In the following, the same reference numerals are used for the same components as in the above embodiment, and the description of the same points as in the above embodiment is omitted as appropriate. The following modification examples can be combined as appropriate.

Explanation of Reference Numerals

[0061] 1... Prediction device (bearing life prediction device), 2... Rotating electrical machine, 21... Rotating shaft, 22... Bearing 110... First average load calculation unit, 112... Deterioration state estimation unit, 116... Second average load calculation unit 118... Prediction unit, AL... Average load, AS... Operating state, FDS... First deterioration state FP... First period, OP... Output power, SP... Second period

Claims

1. An information processing method for causing a processor to execute a process of predicting the life of a bearing that supports a rotating shaft of a rotating electrical machine, comprising: the processor: calculating an average load of an equivalent dynamic load applied to the bearing in a first period, which is a period during which the rotating electrical machine is used as an electric motor, from an operating state of the rotating electrical machine in the first period; estimating a first deterioration state, which is a deterioration state of the bearing in the first period, from the average load in the first period; calculating an average load of an equivalent dynamic load applied to the bearing in a second period, which is a period during which the rotating electrical machine is used as a generator, using output power of the rotating electrical machine in the second period; predicting the life of the bearing in the second period from the first deterioration state in the first period and the average load in the second period; Information processing method.

2. The processor further determines whether the use of the rotating electrical machine has been changed from an electric motor to a generator from the operating state of the rotating electrical machine, and when it is determined that the use of the rotating electrical machine has been changed from an electric motor to a generator, the processor: calculates the average load in the second period; predicts the life of the bearing in the second period from the first deterioration state and the average load in the second period; The information processing method according to claim 1.

3. The average load in the second period is: the output power of the rotating electrical machine in the second period; the torque-rotation characteristics of the rotating electrical machine; calculated from: The information processing method according to claim 1 or 2.

4. The processor is provided in a vehicle that uses the rotating electrical machine as an electric motor; The information processing method according to claim 1 or 2.

5. The output power of the rotating electrical machine is determined using at least one of: the wind speed for power generation efficiency of a windmill in wind power generation in which the rotating electrical machine is used as a generator; the windward area of the blades; the air density; and the power coefficient of the windmill in wind power generation in which the rotating electrical machine is used as a generator; The information processing method according to claim 1 or 2.

6. The output power of the rotating electrical machine is determined using at least one of: the amount of water applied to a waterwheel in hydropower generation in which the rotating electrical machine is used as a generator; the acceleration due to gravity; the water head; and the efficiency in hydropower generation in which the rotating electrical machine is used as a generator; The information processing method according to claim 1 or 2.

7. The output power of the rotating electrical machine is determined using at least one of: the wave power; the wave width; and the efficiency Determined using at least one of The information processing method according to claim 1 or 2.

8. The output power of the rotating electrical machine is in geothermal power generation where the rotating electrical machine is used as a generator, Steam flow rate, Inlet and outlet temperature, Latent heat of steam, and Efficiency Determined using at least one of The information processing method according to claim 1 or 2.

9. A bearing life prediction device for predicting the life of a bearing that supports the rotating shaft of a rotating electrical machine, A first average load calculation unit that calculates an average load of the dynamic equivalent load applied to the bearing in the first period, which is a period in which the rotating electrical machine is used as a motor, from the operating state of the rotating electrical machine in the first period; A deterioration state estimation unit that estimates a first deterioration state, which is the deterioration state of the bearing in the first period, from the average load in the first period calculated by the first average load calculation unit; A second average load calculation unit that calculates an average load of the dynamic equivalent load applied to the bearing in the second period using the output power of the rotating electrical machine in the second period, which is a period in which the rotating electrical machine is used as a generator; A prediction unit that predicts the life of the bearing in the second period from the first deterioration state in the first period estimated by the deterioration state estimation unit and the average load in the second period calculated by the second average load calculation unit; Comprising Bearing life prediction device.

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