Management method, database generation method, database, management system, operating condition determination device, and output device

Abstract

This invention provides a management method, a management system, an operating condition determination device, and an output device that enable the operation of managed objects in a manner that prevents fatal damage to the objects until scheduled maintenance, as well as a database generation method and database useful for management. [Solution] The management method includes the steps of: predicting the remaining lifespan of the managed object 30 based on an abnormality of the managed object 30; calculating an assumed value for the remaining lifespan of the managed object 30 when at least one assumed condition is applied to the operating conditions of the managed object 30; and outputting the assumed condition applied among at least one assumed condition as a candidate condition if the assumed value for the remaining lifespan of the managed object 30 is equal to or greater than a predetermined period.

Description

Technical Field

[0004] , , , ,

[0001] The present disclosure relates to a management method, a database generation method, a database, a management system, an operation condition determination device, and an output device.

Background Art

[0002] In conventional facility maintenance, preventive maintenance is performed, such as a method of determining the maintenance priority of equipment as described in Patent Document 1 and formulating a maintenance plan based thereon, or a method of outputting an alarm or determining a maintenance plan based on a remaining life estimation result as described in Patent Document 2. Further, as described in Patent Document 3, a method of performing maintenance management based on a remaining life evaluation result for an object in which a crack has occurred is performed. As in these examples, it is generally performed to predict a failure and formulate a maintenance plan based on the prediction result.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Patent Document 2

Patent Document 3

Summary of the Invention

Problems to be Solved by the Invention

[0004] According to the method described in Patent Document 1, since a maintenance plan is formulated in advance, it is not possible to respond to irregular cases where the maintenance plan needs to be changed, such as when a crack occurs during operation. According to the method described in Patent Document 2, maintenance requests can be output according to the evaluated failure risk, but in large-scale factory lines, there is a problem that unplanned equipment shutdowns are difficult to implement because the damage caused by unplanned equipment shutdowns is significant. According to the method described in Patent Document 3, maintenance management is performed according to the remaining life prediction results, but there is a problem that it is not possible to respond to cases where it is difficult to perform unplanned maintenance.

[0005] Therefore, this disclosure aims to provide a management method, a management system, an operating condition determination device, and an output device that enable the operation of managed objects so that fatal damage does not occur to the managed objects until scheduled maintenance. Furthermore, this disclosure aims to provide a database generation method and a database useful for operating managed objects so that fatal damage does not occur to the managed objects until scheduled maintenance. [Means for solving the problem]

[0006] A management method according to one embodiment of the present disclosure includes the steps of: (1) a management device predicting the remaining lifespan of a managed object based on an abnormality detected from the managed object; when the predicted remaining lifespan of the managed object is less than a predetermined period, the management device calculates an assumed value of the remaining lifespan of the managed object based on information identifying an influencing parameter among the operational parameters included in the operational conditions of the managed object that affects the remaining lifespan of the managed object, and information identifying the correlation between the remaining lifespan of the managed object and the influencing parameter, by applying at least one assumed condition to the operational conditions of the managed object; and the management device outputting an assumed condition as a candidate condition when the assumed value of the remaining lifespan of the managed object is greater than or equal to the predetermined period.

[0007] (2) In the management method described in (1) above, the information for identifying the influence parameters may include information for identifying influencing factors that affect the remaining lifespan of the managed object, and information for identifying the operational parameters that affect the influencing factors as the influence parameters. The information for identifying the correlation between the remaining lifespan of the managed object and the influence parameters may include information for identifying the correlation between the remaining lifespan of the managed object and the influencing factors, and information for identifying the correlation between the influencing factors and the influence parameters. In the step of calculating the assumed value of the remaining lifespan of the managed object, the management device may calculate the assumed value of the factors when the assumed conditions are applied to the operational conditions of the managed object, and calculate the assumed value of the remaining lifespan of the managed object based on the assumed value of the factors.

[0008] (3) The management method described in (1) or (2) above may further include the step of determining from the candidate conditions to be newly applied to the operating conditions of the managed object.

[0009] A database generation method according to one embodiment of the present disclosure includes the step of generating a database of information that identifies the correlation between the remaining lifespan of a managed object and an influencing parameter among the operational parameters included in the operating conditions of the managed object that affects the remaining lifespan of the managed object.

[0010] (5) In the database generation method described in (4) above, the information that identifies the correlation between the remaining lifespan of the managed object and the influence parameters may include information that identifies the correlation between the remaining lifespan of the managed object and the influence factors that affect the remaining lifespan of the managed object, and information that identifies the correlation between the influence factors and the influence parameters that affect the influence factors.

[0011] A database (6) according to one embodiment of the present disclosure stores information that identifies the correlation between the remaining lifespan of the managed object and an influencing parameter among the operational parameters included in the operating conditions of the managed object that affects the remaining lifespan of the managed object.

[0012] (7) In the database described in (6) above, the information that identifies the correlation between the remaining lifespan of the managed object and the influence parameters may include information that identifies the correlation between the remaining lifespan of the managed object and the influence factors that affect the remaining lifespan of the managed object, and information that identifies the correlation between the influence factors and the influence parameters that affect the influence factors.

[0013] (8) A management system according to one embodiment of the present disclosure includes a detection device for detecting abnormalities in a managed object, a database for storing information that identifies the correlation between the remaining lifespan of the managed object and an influencing parameter among the operational parameters included in the operating conditions of the managed object that affects the remaining lifespan of the managed object, and a management device. The management device includes a remaining lifespan prediction unit that predicts the remaining lifespan of the managed object based on the detection result of an abnormality of the managed object obtained from the detection device, an operational condition determination unit that, when the predicted value of the remaining lifespan of the managed object is less than a predetermined period, calculates an assumed value of the remaining lifespan of the managed object when at least one assumed condition is applied to the operational conditions of the managed object based on information that identifies an influencing parameter among the operational parameters included in the operating conditions of the managed object that affects the remaining lifespan of the managed object, and information that identifies the correlation between the remaining lifespan of the managed object and the influencing parameter, and determines the assumed condition applied among the at least one assumed condition when the assumed value of the remaining lifespan of the managed object is greater than or equal to the predetermined period as a candidate condition, and an output unit that outputs the candidate condition.

[0014] An operating condition determination device according to one embodiment of the present disclosure (9) comprises an assumption unit that calculates an assumed value of the remaining lifespan of a managed object when at least one assumption condition is applied to the operating conditions of the managed object, based on information that identifies an influencing parameter among the operating parameters included in the operating conditions of the managed object that affects the remaining lifespan of the managed object, and information that identifies the correlation between the remaining lifespan of the managed object and the influencing parameter, and an extraction unit that extracts an assumption condition as a candidate condition among the at least one assumption condition that is applied when the assumed value of the remaining lifespan of the managed object is greater than or equal to a predetermined period.

[0015] The output device (10) according to an embodiment of the present disclosure outputs the candidate conditions extracted by the operation condition determination device described in (9) above.

Advantages of the Invention

[0016] The present disclosure can provide a management method, a management system, an operation condition determination device, and an output device that can operate a management target so that no fatal damage occurs to the management target until planned maintenance. Further, the present disclosure can provide a database generation method and a database useful for operating a management target so that no fatal damage occurs to the management target until planned maintenance.

Brief Description of the Drawings

[0017] [Figure 1] It is a block diagram showing a configuration example of a management system according to the present disclosure. [Figure 2] It is a block diagram showing another configuration example of a management system according to the present disclosure. [Figure 3] It is an example of a database that specifies the relationship between an abnormality of a management target, an influencing factor, and an influence parameter. [Figure 4A] It is an example of a graph showing the correlation between an influence parameter and a remaining life. [Figure 4B] It is an example of a table showing the correlation between an influence parameter and a remaining life. [Figure 5] It is an example of a graph that separately represents the correlation of FIG. 4A into the correlation between an influence parameter and an influencing factor and the correlation between an influencing factor and a remaining life. [Figure 6] It is an example of a table showing the correlation between two influence parameters and a remaining life. [Figure 7] It is a flowchart showing an example of the procedure of a management method according to the present disclosure. [Figure 8] It is a graph showing an example of the correlation between the remaining life of a bearing of a sizing press and the reduction width, which is an influence parameter included in the operating conditions of the sizing press. [Figure 9]It is a flowchart showing an example procedure for determining the allowable range of influence parameters from the correlation between influence parameters and remaining life. [Figure 10] It is a flowchart showing an example procedure for determining the allowable range of influence parameters from the correlation between influence factors and remaining life. [Figure 11] It is a flowchart showing an example procedure of a database generation method.

Mode for Carrying Out the Invention

[0018] Hereinafter, embodiments of the management method, database generation method, database, management system 1 (see FIG. 1), operation condition determination device 60 (see FIG. 2), and output device 70 (see FIG. 2) according to the present disclosure will be described based on the drawings. Each drawing is schematic and may be different from the actual one. Further, the following embodiments illustrate an apparatus or method for embodying the technical idea of the present disclosure, and do not specify the configuration to the following. That is, various changes can be made to the technical idea of the present disclosure within the technical scope described in the claims.

[0019] In a large-scale line, it is difficult to stop individual equipment, and the influence range caused by stopping the line is large. Therefore, it is preferable to perform maintenance management in accordance with a predetermined maintenance cycle. On the other hand, since the number of facilities is large, the optimal maintenance times of each facility do not always coincide. In this case, the number of maintenance may increase unnecessarily due to maintenance being carried out earlier than necessary.

[0020] Also, when the equipment, particularly the equipment constituting a part of the above large-scale line, is the management object, and there is a possibility that the management object cannot operate until the next scheduled maintenance time, that is, there is a possibility that the management object cannot last until the next maintenance time, a change in the maintenance time such as an emergency stop occurs. However, such a change in the maintenance time causes problems such as securing personnel, adjusting the construction, or influencing the previous and subsequent processes.

[0021] Furthermore, similar problems arise when the managed object is a building, particularly a structure such as bridge piers, bridges, or tunnels that support infrastructure, also known as social infrastructure or social capital.

[0022] Therefore, this disclosure provides a management method that, when the object to be managed is, for example, equipment subjected to some kind of load, and when crack propagation in the equipment is considered an abnormality of the equipment, allows the equipment to last until a predetermined maintenance timing by adjusting the operating conditions of the equipment to slow down crack propagation.

[0023] For example, delaying crack propagation can be achieved by reducing the load on the object being managed. Reducing the load on the object being managed is achieved by adjusting the operating conditions of the equipment. Therefore, the management method described herein allows the equipment to last until a predetermined maintenance timing by delaying crack propagation through adjustment of the operating conditions of the equipment. By allowing the equipment to last until a predetermined maintenance timing, downtime due to unexpected equipment failure or production reduction due to emergency shutdowns can be prevented. In addition, work such as securing personnel or adjusting processes due to changes in the maintenance plan can be reduced.

[0024] The management method described in this disclosure clearly indicates how to adjust operating conditions by analyzing and narrowing down the candidate parameters that affect crack propagation. This clear indication of how to adjust operating conditions prevents confusion in the operation of the managed object and enables optimal treatment of the managed object.

[0025] (Example configuration of Management System 1) As shown in Figure 1, a management system 1 according to one embodiment comprises a management device 10, a detection device 40, and a management database 50. In this disclosure, the database is also abbreviated as DB. The management device 10 comprises a remaining life prediction unit 11, an operation condition determination unit 12, an output unit 13, a remaining life database 14, a factor database 15, and a correlation database 16. The remaining life database 14, the factor database 15, and the correlation database 16 may be configured as an integrated database, or they may be configured as a database with some parts common to each other. The management device 10, the detection device 40, and the management database 50 are connected to each other via a network 80 so that they can communicate with each other by wire or wireless. The management device 10, the detection device 40, and the management database 50 may be connected to each other so that they can communicate directly without going through the network 80.

[0026] Management system 1 detects abnormalities in the managed object 30 using detection device 40. The managed object 30 may include devices or equipment that are subjected to loads. The managed object 30 may be equipment within a factory. If the managed object 30 is a device or equipment, the load may include vibrations or shocks caused by the operation of the device or equipment. The managed object 30 may also include infrastructure such as bridge piers, bridges, or tunnels on expressways. If the managed object 30 is a road, the load may include vibrations caused by vehicle traffic.

[0027] The detection device 40 may detect damage such as cracks occurring in the managed object 30 as an abnormality of the managed object 30. The detection device 40 may detect an abnormality of the managed object 30 by measuring and analyzing the physical quantities of the managed object 30. The physical quantities of the managed object 30 may include, for example, vibration data of the managed object 30. The detection device 40 may detect an abnormality of the managed object 30 by photographing the managed object 30 and analyzing the image of the managed object 30.

[0028] The management system 1 predicts the remaining lifespan of the managed object 30 using the remaining lifespan prediction unit 11 of the management device 10. If the remaining lifespan of the managed object 30 is shorter than the time until the next maintenance of the managed object 30, the management system 1 uses the operation condition determination unit 12 of the management device 10 to determine the operation conditions of the managed object 30 in order to extend the remaining lifespan of the managed object 30. By extending the remaining lifespan of the managed object 30 as needed, the management system 1 can maintain the maintenance plan for the managed object 30. In other words, the management system 1 can keep the managed object 30 in use until a predetermined maintenance timing.

[0029] As shown in Figure 2, the management system 1 may include a remaining life prediction device 20, an operating condition determination device 60, and an output device 70 instead of the management device 10 in Figure 1. The remaining life prediction device 20 corresponds to the remaining life prediction unit 11 of the management device 10 in Figure 1. The operating condition determination device 60 corresponds to the operating condition determination unit 12 of the management device 10 in Figure 1. The operating condition determination device 60 includes an assumption unit 61, an extraction unit 62, and a determination unit 63. The output device 70 corresponds to the output unit 13 of the management device 10 in Figure 1. Furthermore, the management system 1 may include a remaining life database 14, a factor database 15, and a correlation database 16. In other words, the functions of the management device 10 in Figure 1 may be realized as separate devices. The management device 10 in Figure 1 may have all its functions realized as independent devices, as illustrated in Figure 2, but only some of its functions may be realized as independent devices. A device that provides independent functionality from the management device 10 may be connected to other devices via the network 80 to enable communication, or it may be connected to other devices directly to enable communication without going through the network 80.

[0030] The components of Management System 1 are described below.

[0031] <Remaining life prediction unit 11 or remaining life prediction device 20> The remaining life prediction unit 11 or remaining life prediction device 20 predicts the remaining life of the managed object 30 based on the detection result of the state of the managed object 30 by the detection device 40. The remaining life prediction unit 11 or remaining life prediction device 20 may include a CPU (Central Processing Unit) or GPU (Graphics Processing Unit) for executing various controls. The remaining life prediction unit 11 or remaining life prediction device 20 may include a storage unit for storing various information or data. The storage unit may function as work memory for the CPU or GPU. The storage unit may include, but is not limited to, semiconductor memory. The storage unit may be configured as internal memory for the CPU or GPU, or as an electromagnetic recording medium such as a hard disk drive (HDD) accessible from the CPU or GPU. The storage unit may be configured as a non-temporary readable medium. The storage unit may be configured integrally with the CPU or GPU, or as a separate unit from the CPU or GPU. The CPU or GPU may implement the functions of the remaining life prediction unit 11 or the remaining life prediction device 20 by reading and executing a program stored in the memory unit.

[0032] The remaining life prediction unit 11 or the remaining life prediction device 20 may be equipped with a communication interface for communicating with the detection device 40 or other devices such as a database by wire or wireless connection. The communication interface may be configured to communicate with other devices via the network 80, or it may be configured to communicate with other devices without going through the network 80. The communication interface may communicate based on a wired communication standard or based on a wireless communication standard. The wireless communication standard may include cellular phone communication standards such as 4G (4th Generation) or 5G (5th Generation). The wireless communication standard may also include IEEE 802.11 and Bluetooth®. The communication interface may support one or more of these communication standards. The communication interface is not limited to these examples and may communicate with other devices and input / output data based on various standards.

[0033] The remaining life prediction unit 11 or the remaining life prediction device 20 may include an input device. The input device may include, for example, a keyboard or physical keys, or a pointing device such as a touch panel or touch sensor or mouse. The input device is not limited to these examples and may include various other devices.

[0034] The remaining life prediction unit 11 or remaining life prediction device 20 may be configured to include a computer equipped with the above-mentioned components.

[0035] The remaining life prediction unit 11 or the remaining life prediction device 20 may be implemented in an on-premise environment or using cloud services. The remaining life prediction unit 11 or the remaining life prediction device 20 may also be implemented in a hybrid form combining an on-premise environment and cloud services.

[0036] <Operating Condition Determination Unit 12 or Operating Condition Determination Device 60> The operation condition determination unit 12 or operation condition determination device 60 determines, as necessary, to change the operation conditions of the managed object 30 based on the predicted remaining lifespan of the managed object 30.

[0037] The operation condition determination unit 12 or operation condition determination device 60 may include a CPU or GPU for performing various controls. The operation condition determination unit 12 or operation condition determination device 60 may include a storage unit for storing programs or necessary information or data. The storage unit may be configured identically or similarly to the storage unit of the remaining life prediction unit 11 or remaining life prediction device 20. The CPU or GPU may realize the functions of the operation condition determination unit 12 or operation condition determination device 60 by reading and executing programs stored in the storage unit. The operation condition determination unit 12 or operation condition determination device 60 may include a communication interface for communicating with the remaining life prediction unit 11 or remaining life prediction device 20 or the output unit 13 or output device 70. The communication interface may be configured identically or similarly to the communication interface of the remaining life prediction unit 11 or remaining life prediction device 20. Furthermore, the operation condition determination unit 12 or operation condition determination device 60 may include a computer equipped with the above-mentioned components.

[0038] <Output unit 13 or output device 70> The output unit 13 or output device 70 outputs the operating conditions of the managed object 30 determined by the operating condition determination unit 12 or operating condition determination device 60. The output unit 13 or output device 70 may also output the remaining life prediction result of the managed object 30 by the remaining life prediction unit 11 or remaining life prediction device 20.

[0039] The output unit 13 or output device 70 includes an output device. The output device may include a display device, or may be connected to the display device by wire or wireless. The display device may include various displays, such as liquid crystal displays. The output device may notify the user of information by outputting auditory information, such as sound, directly or via an external device. The output device may include an audio output device, such as a speaker, or may be connected to the audio output device by wire or wireless. The output device may include a vibration device. The output device may notify the user of information not only by outputting visual, auditory, or tactile information, but also by outputting information that the user can perceive with other senses, directly or via an external device.

[0040] The output unit 13 or output device 70 may include a CPU or GPU for performing various controls. The output unit 13 or output device 70 may include a storage unit for storing programs or information or data to be output. The storage unit may be configured identically or similarly to the storage unit of the remaining life prediction unit 11 or remaining life prediction device 20. The CPU or GPU may realize the functions of the output unit 13 or output device 70 by reading and executing the program stored in the storage unit. The output unit 13 or output device 70 may include a communication interface for communicating with the remaining life prediction unit 11 or remaining life prediction device 20 or the operation condition determination unit 12 or operation condition determination device 60. The communication interface may be configured identically or similarly to the communication interface of the remaining life prediction unit 11 or remaining life prediction device 20. The output unit 13 or output device 70 may include a computer equipped with the above-mentioned components.

[0041] <Detection device 40> The detection device 40 may include a sensor attached to the object to be managed 30. The detection device 40 may include a camera that photographs at least a portion of the object to be managed 30. The detection device 40 may be configured to communicate with the sensor attached to the object to be managed 30 or the camera that photographs the object to be managed 30 and to acquire detection results from the sensor or camera.

[0042] The sensors attached to the managed object 30 may include, for example, vibration sensors, acceleration sensors, load sensors, or acoustic sensors, so as to be able to detect data when the managed object 30 is operating or when the managed object 30 is in operation.

[0043] The vibration sensor detects mechanical vibrations of at least a portion of the object 30 being managed. The vibration sensor may output a frequency spectrum of vibration. The frequency spectrum of vibration includes the intensity of vibration for each frequency component. In other words, the vibration sensor may detect the frequency and intensity of vibration.

[0044] The accelerometer detects the acceleration of at least a portion of the object 30 being managed. The accelerometer may also detect the acceleration of vibrations. The accelerometer may also detect the acceleration caused by an external force acting on at least a portion of the object 30 being managed. In addition to the accelerometer, the sensors attached to the object 30 may also include a gyroscope or an inertial sensor. The accelerometer may be replaced by an inertial sensor.

[0045] The load sensor detects loads such as tension, compression, shear, bending, or torsion applied to at least a portion of the object 30 being managed. The load sensor may include a load cell. The load sensor may be configured to detect static loads. The load sensor may be configured to detect dynamic loads, including impact loads or repeated loads.

[0046] The acoustic sensor detects sound emanating from at least a portion of the object 30 being managed. The acoustic sensor may include an AE (Acoustic Emission) sensor. The AE sensor can detect sounds in the ultrasonic range. The acoustic sensor may detect sound pressure levels. The acoustic sensor may output a sound frequency spectrum. The sound frequency spectrum includes the intensity of the sound for each frequency component.

[0047] The detection device 40 detects abnormalities occurring in the managed object 30 based on the detection results of a sensor or camera. The detection device 40 may be equipped with a CPU or GPU for performing various controls. The detection device 40 may be equipped with a storage unit for storing programs or output information or data. The storage unit may be configured identically or similarly to the storage unit of the remaining life prediction unit 11 or the remaining life prediction device 20. The CPU or GPU may realize the functions of the detection device 40 by reading and executing the program stored in the storage unit. The detection device 40 may be equipped with a communication interface for communicating with the remaining life prediction unit 11 or the remaining life prediction device 20. The communication interface may be configured identically or similarly to the communication interface of the remaining life prediction unit 11 or the remaining life prediction device 20.

[0048] <database> Databases such as the management database 50, the remaining lifespan database 14, the factor database 15, or the correlation database 16 may store data used by the management system 1.

[0049] The management database 50 may store data relating to the managed object 30. The data relating to the managed object 30 is also referred to as the data relating to the managed object 30. The data relating to the managed object 30 may include operational data relating to the managed object 30. The data relating to the managed object 30 may include operating conditions relating to the managed object 30. The data relating to the managed object 30 may include maintenance plan data that specifies the maintenance timing or the content of maintenance work for the managed object 30.

[0050] The data for the managed object 30 may include material data for the managed object 30. The material data for the managed object 30 is data that identifies the material of at least one part of the managed object 30. The material data for the managed object 30 may include data that identifies the name of the material of the part, the shape of the part, or its mechanical properties. The mechanical properties may include tensile strength, yield stress, proof stress, Young's modulus, Poisson's ratio, or density.

[0051] The remaining life database 14 may store data used by the remaining life prediction unit 11 or the remaining life prediction device 20 to predict the remaining life of the managed object 30. The data used to predict the remaining life of the managed object 30 is also called remaining life data.

[0052] The factor database 15 or correlation database 16 may store data used by the operation condition determination unit 12 or operation condition determination device 60 to determine the operation conditions of the managed object 30. The factor database 15 may store information that identifies the relationship between the remaining lifespan of the managed object 30 and factors that affect the remaining lifespan of the managed object 30. Factors that affect the remaining lifespan of the managed object 30 are also called influencing factors. In other words, the factor database 15 may store information that identifies the relationship between the remaining lifespan of the managed object 30 and influencing factors. The factor database 15 may store information that identifies the relationship between influencing factors and operational parameters included in the operation conditions that affect the influencing factors. Operational parameters that affect influencing factors are also called influencing parameters. In other words, the factor database 15 may store information that identifies the relationship between influencing factors and influencing parameters. Influencing parameters also affect the remaining lifespan of the managed object 30 through influencing factors. In other words, the factor database 15 may store information that identifies the relationship between the remaining lifespan of the managed object 30 and influencing parameters. The correlation database 16 may store information such as formulas or tables that identify the correlation between a numerical value representing the remaining lifespan of the managed object 30 and a numerical value representing the influencing factor. The correlation database 16 may store information such as formulas or tables that identify the correlation between a numerical value representing the influencing factor and a numerical value representing the influencing parameter. The correlation database 16 may store information such as formulas or tables that identify the correlation between a numerical value representing the remaining lifespan of the managed object 30 and a numerical value representing the influencing parameter.

[0053] The database may be implemented in an on-premises environment, or it may be implemented using cloud services. The database may also be implemented in a hybrid form, combining on-premises and cloud services.

[0054] (Example of operation of management system 1) In the management system 1 according to this embodiment, the detection device 40 detects an abnormality in the managed object 30. The remaining life prediction unit 11 or remaining life prediction device 20 predicts the remaining life of the managed object 30 based on the detection result of the abnormality in the managed object 30. The operation condition determination unit 12 or operation condition determination device 60 determines that it is necessary to extend the remaining life of the managed object 30 if the predicted value of the remaining life of the managed object 30 is less than a predetermined period, and determines the operation conditions of the managed object 30 that have been changed to extend the remaining life of the managed object 30. The output unit 13 or output device 70 outputs the changed operation conditions of the managed object 30.

[0055] The following describes examples of the operation of each component of the management system 1. In this example, the object to be managed 30 is assumed to be a steel plate sizing press. The object to be managed 30 is not limited to the sizing press in this example, but may be various other devices, equipment, or infrastructure.

[0056] <Detection of anomalies> The detection device 40 detects abnormalities in the managed object 30. An abnormality in the managed object 30 may correspond to the managed object 30 deviating from its normal state. In this disclosure, the managed object 30 is considered abnormal if it has changed from its initial state. An abnormality in the managed object 30 may include the occurrence of damage such as cracks, thinning, or deformation. An abnormality in the managed object 30 may include a decrease in the amount of lubricant. An abnormality in the managed object 30 is not limited to these examples and may include various other changes.

[0057] In this example of operation, the sizing press, which is the object under management 30, is a device that presses a steel plate in the width direction by narrowing the distance between blocks located on both sides of the steel plate in the width direction. The sizing press controls the distance between the blocks by moving the blocks along the width direction of the steel plate in order to narrow the distance between the blocks. The sizing press moves the blocks along the width direction of the steel plate by rotating a crankshaft, which is connected to the blocks via a connecting rod. As the sizing press repeatedly presses the steel plate in the width direction, a repeated load is applied to the crankshaft. This repeated load on the crankshaft can cause cracks to form in the crank bearings used in the crankshaft. The detection device 40 may detect the occurrence of cracks as an abnormality. The location of the crack that is the focus of consideration for estimating the remaining life is not limited to the crank bearings, but may be various other parts or components. The detection device 40 may also detect other events as abnormalities, such as poor lubrication, in addition to the occurrence of cracks.

[0058] The detection device 40 may detect the occurrence of a crack if a crack is visible in the image of the object being managed 30. The detection device 40 may detect the occurrence of a crack from vibration or acoustic data of the object being managed 30. The detection device 40 may detect the occurrence of a crack using ultrasonic testing or X-ray testing, etc.

[0059] <Predicting remaining lifespan> The remaining life prediction unit 11 or remaining life prediction device 20, when it detects an abnormality in the managed object 30, predicts the length of time until the managed object 30 fails or stops due to the abnormality as the remaining life of the managed object 30. The remaining life of the managed object 30 may be determined by at least one of the time or number of times the managed object 30 operates.

[0060] The remaining lifespan of the managed object 30 corresponds to the length of the operating period from the time the remaining lifespan of the managed object 30 is predicted until the managed object 30 is predicted to fail or stop. The remaining lifespan of the managed object 30 may be specified by the operating time of the managed object 30 or the number of times the managed object 30 is operated. If the managed object 30 is equipment or a device, the remaining lifespan of the managed object 30 may be specified by at least one of the operating time or number of times the equipment or device is operated. If the managed object 30 is infrastructure such as a road, the remaining lifespan of the managed object 30 may be specified by at least one of the operating time of vehicles on the road or the number or weight of vehicles on the road.

[0061] In this example of operation, the remaining life of the sizing press, which is the managed object 30, is specified as the operating time or number of operations of the sizing press until a crack that has occurred in the crank bearing reaches a predetermined length. The remaining life prediction unit 11 or remaining life prediction device 20 may predict the propagation of the crack and predict the operating time or number of operations when the propagated crack reaches a predetermined length as the remaining life of the sizing press. The remaining life prediction unit 11 or remaining life prediction device 20 may predict the propagation of the crack detected by the detection device 40 based on the data stored in the remaining life database 14.

[0062] <Determination of operating conditions> The operation condition determination unit 12 or the operation condition determination device 60 determines modified operation conditions to extend the remaining lifespan of the managed object 30 if the remaining lifespan of the managed object 30 is less than a predetermined period. The predetermined period represents the length of time until the next maintenance of the managed object 30. The predetermined period is a numerical value used for comparison with the predicted remaining lifespan of the managed object 30, and is also called the remaining lifespan threshold. The operation condition determination unit 12 or the operation condition determination device 60 may obtain the predetermined period to be compared with the predicted remaining lifespan of the managed object 30 from the information of the managed object 30 itself, the information of the factory or other facility or line where the managed object 30 is installed, or the maintenance plan information for the managed object 30, facility or line, which are stored in the management database 50.

[0063] The operation condition determination unit 12 or operation condition determination device 60 obtains influencing factors and influencing parameters that affect abnormalities in the managed object 30 from the factor database 15. The factor database 15 identifies, for example, the relationship between the managed object 30, which is a sizing press; the bearing, which is a component of the sizing press; the crack, which is an abnormality occurring in the bearing; the load and cycle number, which are influencing factors that affect crack propagation; the plate composition and reduction width, which are influencing parameters that affect the load; and the line speed, which is an influencing parameter that affects the cycle number, as shown in Figure 3.

[0064] The operation condition determination unit 12 or operation condition determination device 60 refers to the database illustrated in Figure 3 to determine that the influencing factors affecting crack propagation in the bearings of the sizing press are load and cycle number. The operation condition determination unit 12 or operation condition determination device 60 also refers to the database illustrated in Figure 3 to determine that the influencing parameters affecting load are plate composition and reduction width, and the influencing parameter affecting cycle number is line speed. The operation condition determination unit 12 or operation condition determination device 60 also determines that the influencing parameters affecting crack propagation in the bearings of the sizing press are plate composition, reduction width, and line speed.

[0065] The operating condition determination unit 12 or the operating condition determination device 60 obtains the correlation between the period until a crack in the bearing of the sizing press propagates to a predetermined length, i.e., the remaining life, and the influencing parameters from the correlation database 16.

[0066] The correlation database 16 may include, for example, a formula that shows how the remaining lifetime of the managed object 30 changes when the influence parameter X is changed from its initial value (Xinit). The formula can also be represented as a graph, as illustrated in Figure 4A. The graph in Figure 4A shows that when the influence parameter X is at its initial value (Xinit) and the corresponding remaining lifetime is less than a predetermined period A, the remaining lifetime increases by decreasing the influence parameter X, and when the influence parameter X falls below a threshold, the remaining lifetime becomes greater than or equal to the predetermined period A.

[0067] The correlation database 16 may also be represented as a table as illustrated in Figure 4B. This table also shows that if the influence parameter X falls below a threshold, the remaining lifetime becomes a predetermined period A.

[0068] The operation condition determination unit 12 or the operation condition determination device 60, by referring to the correlation database 16, can determine that the remaining lifespan of the managed object 30 can be extended beyond a predetermined period by reducing the influence parameter X. In other words, the operation condition determination unit 12 or the operation condition determination device 60 may determine the modified conditions to set the influence parameter X below a threshold so as to extend the remaining lifespan of the managed object 30 beyond a predetermined period.

[0069] The correlation database 16 may not only show a direct correlation between the remaining lifespan of the managed object 30 and the influence parameter X, as illustrated in Figure 4A or Figure 4B, but may also show the correlation between the remaining lifespan of the managed object 30 and the influence parameter X in a format that combines the correlation between the influence parameter X and the influence factor Y, and the correlation between the influence factor Y and the remaining lifespan of the managed object 30, as illustrated in Figure 5.

[0070] The correlation database 16 may represent not only the correlation between one influence parameter X or one influence factor Y and the remaining lifespan of the managed object 30, as described above, but also the correlation between multiple influence parameters or multiple influence factors and the remaining lifespan of the managed object 30. For example, as shown in Figure 6, the correlation database 16 may represent the correlation between influence parameters X1 and X2 and the remaining lifespan of the managed object 30 in table format. In the example in Figure 6, it is assumed that the predicted value of the remaining lifespan is 40 when the initial values ​​of the influence parameters are (X1init,X2init)=(250,50). When the predetermined period is set to 100, the operation condition determination unit 12 or the operation condition determination device 60 can determine from the table in Figure 6 the combinations of influence parameters X1 and X2 that result in a remaining lifespan of 100 or more. The operation condition determination unit 12 or the operation condition determination device 60 may select one combination from the combinations of influence parameters X1 and X2 that result in a remaining lifespan of 100 or more and determine it as the modified condition.

[0071] The operating condition determination unit 12 or the operating condition determination device 60 may determine one operating condition from among multiple options if it can identify multiple options that can change the influencing parameters so that the remaining lifespan can be extended beyond a predetermined period, or it may determine each of the multiple options as a candidate condition. The candidate conditions are operating conditions that can extend the remaining lifespan beyond a predetermined period.

[0072] If the object to be managed 30 is a device or equipment, possible candidate conditions may be modified conditions such as reducing the load or reducing the number of operations. If the object to be managed 30 is infrastructure such as a road, possible candidate conditions may be restrictions on the number of vehicles that can pass or the time of day during which vehicles can pass, i.e., traffic restrictions.

[0073] <Output of determined operating conditions> The output unit 13 or output device 70 outputs the modified operating conditions determined by the operating condition determination unit 12 or operating condition determination device 60. The output unit 13 or output device 70 may notify the person in charge of operating the managed object 30 or the manager of the managed object 30 of the modified operating conditions. The person in charge or manager may confirm the notification of the modified operating conditions and decide whether to change the operating conditions of the managed object 30 to the notified conditions. The output unit 13 or output device 70 may also output the modified operating conditions to the managed object 30. If the managed object 30 can change its own operating conditions, it may change to the operating conditions determined by the management system 1 and continue operating the managed object 30.

[0074] The output unit 13 or output device 70 outputs multiple operating conditions included in the candidate conditions when the operating condition determination unit 12 or operating condition determination device 60 has determined candidate conditions. The person in charge or manager may check the notification of candidate conditions, select one condition from the candidate conditions, and decide whether to change the operating conditions of the managed object 30 to the selected condition. If the managed object 30 can change its own operating conditions, it may select one condition from the candidate conditions, change to the selected operating conditions, and continue operating the managed object 30.

[0075] The output unit 13 or output device 70 may display a correlation graph between the remaining lifespan of the managed object 30 and the influence parameters, and may display the influence parameters corresponding to a predetermined period and current operating conditions, as well as the changed influence parameters, on the graph. If the remaining lifespan of the managed object 30 is correlated with multiple influence parameters, the output unit 13 or output device 70 may display the influence parameters corresponding to a predetermined period and current operating conditions, as well as the changed influence parameters, on a two-dimensional map. If three or more influence parameters are correlated with the remaining lifespan, the output unit 13 or output device 70 may allow the person in charge of operating the managed object 30 to set the vertical or horizontal axis of the two-dimensional map. The output unit 13 or output device 70 may display a threshold value for the parameter entered by the person in charge, etc., among the multiple influence parameters, for which the remaining lifespan is equal to or greater than a predetermined period.

[0076] <Example flowchart> The following describes an example of operation in which the management device 10, as illustrated in Figure 1, performs a management method including the steps in the flowchart illustrated in Figure 7 to predict the remaining lifespan of the managed object 30, and to determine and output the operating conditions of the managed object 30. The management method may also be performed by the remaining lifespan prediction device 20, the operating conditions determination device 60, and the output device 70, as illustrated in Figure 2. The management method may be implemented as a management program executed by the remaining lifespan prediction unit 11, the operating conditions determination unit 12, and the output unit 13 of the management device 10, or by the remaining lifespan prediction device 20, the operating conditions determination device 60, and the output device 70. The management program may be stored on a non-temporary computer-readable medium.

[0077] The remaining life prediction unit 11 obtains the detection result of an abnormality in the managed object 30 from the detection device 40 (step S1). If an abnormality has occurred in the managed object 30, the remaining life prediction unit 11 predicts the remaining life of the managed object 30 (step S2). Specifically, the remaining life prediction unit 11 may predict the remaining life of the managed object 30 as the period until the managed object 30 fails or stops due to the abnormality. If the abnormality in the managed object 30 is a crack that has occurred in the bearing of the sizing press, the remaining life prediction unit 11 may predict the number of cycles at which the crack length exceeds the part replacement criterion as the remaining life of the sizing press, based on the relationship between the number of operating cycles of the sizing press and the progression of the crack length, as illustrated in Figure 8.

[0078] Returning to Figure 7, the operation condition determination unit 12 sets a predetermined period (step S3). The operation condition determination unit 12 obtains the predetermined period for the sizing press, which is the managed object 30, from the information stored in the management database 50.

[0079] The operation condition determination unit 12 determines whether the predicted remaining lifespan of the managed object 30 is less than a predetermined period (step S4). For example, in the relationship between the number of cycles and the progression of crack length shown in Figure 8, the current operating conditions of the sizing press are assumed to be a reduction width of W1. In this case, the number of cycles until the crack length reaches the part replacement criterion is assumed to be N1. And N1 is assumed to be less than a predetermined period A. In other words, if the operation of the sizing press is continued under the current operating conditions, the remaining lifespan of the sizing press will be less than a predetermined period. In this case, the operation condition determination unit 12 determines that the predicted remaining lifespan of the managed object 30 is less than a predetermined period.

[0080] Returning to Figure 7, the operating condition determination unit 12 outputs the predicted remaining lifespan of the managed object 30 to the output unit 13 (step S5) without changing the operating conditions if the predicted remaining lifespan of the managed object 30 is not less than the predetermined period (step S4: NO), that is, if the predicted remaining lifespan of the managed object 30 is equal to or greater than the predetermined period. The output unit 13 outputs the predicted remaining lifespan of the managed object 30. After executing the procedure in step S5, the management device 10 finishes executing the procedure in the flowchart of Figure 7.

[0081] If the predicted remaining lifespan of the managed object 30 is less than a predetermined period (step S4: YES), the operational condition determination unit 12 extracts candidate conditions that satisfy the condition that the remaining lifespan is greater than or equal to a predetermined period (step S6). The operational condition determination unit 12 may extract candidate conditions by, for example, executing the steps in the flowchart of Figure 9.

[0082] The operational condition determination unit 12 obtains influence parameters that affect abnormalities in the managed object 30 from the factor database 15 (step S11).

[0083] The operational condition determination unit 12 determines the assumed values ​​of the influence parameters (step S12). The assumed values ​​of the influence parameters are the values ​​assumed to be changed from the current values ​​of the influence parameters, i.e., the initial values. The operational condition determination unit 12 may determine multiple assumed values. For example, in the table in Figure 6, the operational condition determination unit 12 may determine 25 possible assumed values ​​by determining the assumed values ​​of influence parameter X1 in 5 ways from 50 to 250 and the assumed values ​​of X2 in 5 ways from 10 to 50. The operational conditions with the influence parameters set as assumed values ​​are also called the assumption conditions.

[0084] Returning to Figure 9, the operational condition determination unit 12 calculates an assumed value of the remaining life based on the correlation between the remaining life and the influence parameters stored in the correlation database 16 (step S13). The operational condition determination unit 12 can calculate the assumed value of the remaining life by applying the assumed values ​​of the influence parameters to the correlation. The operational condition determination unit 12 may calculate an assumed value of the remaining life corresponding to each of the multiple assumed values ​​of the influence parameters. For example, the operational condition determination unit 12 may calculate the remaining life corresponding to each of the 25 assumed values ​​in the table in Figure 6 as the assumed value of the remaining life.

[0085] Returning to Figure 9, the operational condition determination unit 12 extracts the assumed values ​​of the influencing parameters when the assumed value of remaining life is less than a predetermined period (step S14). For example, in the example of Figure 6, the operational condition determination unit 12 extracts the combinations of influencing parameters X1 and X2 when the assumed value of remaining life is less than 100, which is a predetermined period.

[0086] Returning to Figure 9, the operational condition determination unit 12 determines the acceptable range for the influencing parameters (step S15). Specifically, the operational condition determination unit 12 removes the assumed value of the influencing parameter when the assumed value of the remaining life is less than a predetermined period, and determines the range specified by the remaining value as the acceptable range for the influencing parameter. The operational condition determination unit 12 may determine an acceptable range for each of multiple influencing parameters. The operational condition determination unit 12 may determine an acceptable range for a combination of multiple influencing parameters. If the operational condition determination unit 12 can determine one condition from the acceptable range of the influencing parameters, it may determine that condition as the modified operational condition. If the acceptable range of the influencing parameters includes multiple operational conditions, the operational condition determination unit 12 may determine those multiple operational conditions as candidate conditions. After executing the procedure in step S15, the operational condition determination unit 12 returns to the procedure in step S7 in Figure 7.

[0087] The operating condition determination unit 12 may extract candidate conditions by, for example, executing the steps in the flowchart of Figure 10.

[0088] The operational condition determination unit 12 obtains influencing factors that affect abnormalities in the managed object 30 from the factor database 15 (step S21).

[0089] The operational condition determination unit 12 determines the assumed values ​​of the influencing factors (step S22). The assumed values ​​of the influencing factors are the values ​​assumed to be changed from the current values ​​of the influencing factors, i.e., the initial values. The operational condition determination unit 12 may determine multiple assumed values.

[0090] The operational condition determination unit 12 calculates an assumed value of the remaining life based on the correlation between the remaining life and the influencing factors stored in the correlation database 16 (step S23). The operational condition determination unit 12 can calculate the assumed value of the remaining life by applying the assumed values ​​of the influencing factors to the correlation. The operational condition determination unit 12 may calculate an assumed value of the remaining life corresponding to each of the multiple assumed values ​​of the influencing factors.

[0091] The operating condition determination unit 12 extracts the assumed values ​​of influencing factors when the assumed value of remaining life is less than a predetermined period (step S24).

[0092] The operational condition determination unit 12 determines the acceptable range for the influencing factors (step S25). Specifically, the operational condition determination unit 12 removes the assumed values ​​of the influencing factors when the assumed value of the remaining life is less than a predetermined period, and determines the range specified by the remaining values ​​as the acceptable range for the influencing factors. The operational condition determination unit 12 may determine an acceptable range for each of multiple influencing factors. The operational condition determination unit 12 may determine an acceptable range for a combination of multiple influencing factors.

[0093] The operational condition determination unit 12 determines the acceptable range of the influencing parameter from the acceptable range of the influencing factor (step S26). The operational condition determination unit 12 may obtain the correlation between the influencing factor and the influencing parameter from the correlation database 16 and convert the acceptable range of the influencing factor to the acceptable range of the influencing parameter based on the correlation. If the operational condition determination unit 12 can determine one condition from the acceptable range of the influencing parameter, it may determine that condition as the modified operational condition. If the acceptable range of the influencing parameter includes multiple operational conditions, the operational condition determination unit 12 may determine those multiple operational conditions as candidate conditions. After executing the procedure in step S26, the operational condition determination unit 12 returns to the procedure in step S7 in Figure 7.

[0094] Returning to Figure 7, the operation condition determination unit 12 outputs candidate conditions (step S7). The operation condition determination unit 12 may also determine and output one operation condition from among the candidate conditions. The output unit 13 outputs the candidate condition or operation condition determined by the operation condition determination unit 12. After executing the procedure in step S7, the management device 10 finishes executing the procedure in the flowchart of Figure 7.

[0095] <Summary> As described above, the management system 1 relating to this disclosure can change the operating conditions so that the remaining lifespan of the managed object 30 is greater than or equal to a predetermined period, based on the relationship between the remaining lifespan of the managed object 30 and the impact parameters. In this way, the management system 1 can operate the managed object 30 so that no fatal damage occurs to the managed object 30 until the time of planned maintenance. As a result, the impact of unexpected maintenance on the managed object 30 is reduced.

[0096] (Generation of factor database 15 and correlation database 16) The factor database 15 and the correlation database 16 may be generated by executing a database generation method that includes the steps of the flowchart illustrated in Figure 11. The database generation method may be executed, for example, by the operational condition determination unit 12. The database generation method is not limited to this and may be executed by one or more of the following: other components, other devices, external components, or external devices. In other words, the database generation method may be executed by other computers and / or external computers. Furthermore, the database generation method may be executed in an on-premises environment or in a cloud service. The database generation method may be implemented as a database generation program executed by a processor. The database generation program may be stored on a non-temporary computer-readable medium.

[0097] The operation condition determination unit 12 identifies the factors influencing the remaining lifespan of the managed object 30 (step S31). The operation condition determination unit 12 may accept input of influencing factors from the person in charge of operating the managed object 30 or the manager of the managed object 30.

[0098] The operation condition determination unit 12 identifies operation parameters related to influencing factors (step S32). The operation parameters are parameters included in the operation conditions of the managed object 30. The operation condition determination unit 12 may accept input of operation parameters from the person in charge of operating the managed object 30 or the manager of the managed object 30.

[0099] The operation condition determination unit 12 extracts influence parameters that have a strong correlation with the remaining lifespan from the operation parameters (step S33). The operation condition determination unit 12 may accept input for extracting influence parameters from the person in charge of operating the managed object 30 or the manager of the managed object 30.

[0100] The operational condition determination unit 12 assigns priority to the influencing parameters (step S34). When the operational condition determination unit 12 determines the assumed values ​​of the influencing parameters as described above and calculates the assumed value of the remaining life, it may prioritize determining the assumed values ​​of parameters that have a significant impact on the remaining life and then calculate the assumed value of the remaining life. In other words, the operational condition determination unit 12 may assign priority to the influencing parameters in order of their correlation with the remaining life.

[0101] The operational condition determination unit 12 creates a database of the correlation between remaining lifespan, influencing factors, and influencing parameters for each managed object 30 (step S35). In other words, the operational condition determination unit 12 creates a factor database 15 and a correlation database 16 for each managed object 30. After executing the procedure in step S35, the operational condition determination unit 12 completes the execution of the procedure in the flowchart of Figure 11.

[0102] The factor database 15 and the correlation database 16 are useful for implementing the management methods described above. In other words, the database generation method and databases relating to this disclosure are useful for operating the managed object 30 in such a way that no fatal damage occurs to the managed object 30 until planned maintenance.

[0103] While embodiments of this disclosure have been described based on the drawings and examples, it should be noted that those skilled in the art can make various modifications or alterations based on this disclosure. Therefore, it should be noted that these modifications or alterations are included within the scope of this disclosure. For example, the functions included in each component or step can be rearranged in a logically consistent manner, and multiple components or steps can be combined into one or divided. Embodiments relating to this disclosure can also be realized as programs executed by a processor in the device or as storage media recording such programs. These should also be understood to be included within the scope of this disclosure. [Explanation of Symbols]

[0104] 1 Management System 10. Management device (11: remaining life prediction unit, 12: operating condition determination unit, 13: output unit, 14: remaining life database, 15: factor database, 16: correlation database) 20. Remaining Life Prediction Device 30. Items under management 40 Detection device 50 Management Databases 60 Operating condition determination device (61: Assumption unit, 62: Extraction unit, 63: Determination unit) 70 Output device 80 Networks

Claims

1. The management device predicts the remaining lifespan of the managed object based on an abnormality detected from the managed object, The management device, when the predicted remaining lifespan of the managed object is less than a predetermined period, calculates an assumed value of the remaining lifespan of the managed object based on information identifying an influencing parameter among the operational parameters included in the operational conditions of the managed object that affects the remaining lifespan of the managed object, and information identifying the correlation between the remaining lifespan of the managed object and the influencing parameter, by applying at least one assumed condition to the operational conditions of the managed object. The management device outputs as a candidate condition an assumption condition which is applied when the assumption value of the remaining lifespan of the managed object is equal to or greater than the predetermined period among the at least one assumption condition. Management methods, including those mentioned above.

2. The information for identifying the influence parameters includes information for identifying influencing factors that affect the remaining lifespan of the managed object, and information for identifying the operational parameters that affect the influencing factors as the influence parameters. The information identifying the correlation between the remaining lifespan of the managed object and the influence parameter includes information identifying the correlation between the remaining lifespan of the managed object and the influence factor, and information identifying the correlation between the influence factor and the influence parameter. In the step of calculating the assumed remaining lifespan of the managed object, the management device calculates the assumed value of the factor when the assumed conditions are applied to the operating conditions of the managed object, and calculates the assumed remaining lifespan of the managed object based on the assumed value of the factor. The management method described in claim 1.

3. The management method according to claim 1 or 2, further comprising the step of determining from the candidate conditions to be newly applied to the operating conditions of the managed object.

4. A database generation method comprising the step of generating a database of information that identifies the correlation between the remaining lifespan of a managed object and an influencing parameter among the operational parameters included in the operating conditions of the managed object that affects the remaining lifespan of the managed object.

5. The database generation method according to claim 4, wherein the information for identifying the correlation between the remaining lifespan of the managed object and the influence parameters includes information for identifying the correlation between the remaining lifespan of the managed object and an influencing factor that affects the remaining lifespan of the managed object, and information for identifying the correlation between the influencing factor and an influencing parameter that affects the influencing factor.

6. A database that stores information identifying the correlation between the remaining lifespan of a managed object and an influencing parameter among the operational parameters included in the operating conditions of the managed object that affects the remaining lifespan of the managed object.

7. The database according to claim 6, wherein the information for identifying the correlation between the remaining lifespan of the managed object and the influence parameters includes information for identifying the correlation between the remaining lifespan of the managed object and an influencing factor that affects the remaining lifespan of the managed object, and information for identifying the correlation between the influencing factor and an influencing parameter that affects the influencing factor.

8. The system comprises a detection device for detecting abnormalities in a managed object, a database for storing information that identifies the correlation between the remaining lifespan of the managed object and influencing parameters among the operating conditions of the managed object that affect the remaining lifespan of the managed object, and a management device. The aforementioned control device is A remaining life prediction unit predicts the remaining life of the managed object based on the detection result of an abnormality of the managed object obtained from the detection device, An operation condition determination unit that, when the predicted remaining lifespan of the managed object is less than a predetermined period, calculates an assumed value of the remaining lifespan of the managed object when at least one assumed condition is applied to the operation conditions of the managed object, based on information that identifies an influencing parameter among the operation parameters included in the operation conditions of the managed object that affects the remaining lifespan of the managed object, and information that identifies the correlation between the remaining lifespan of the managed object and the influencing parameter, and determines as a candidate condition the assumed condition applied when the assumed value of the remaining lifespan of the managed object is equal to or greater than the predetermined period among the at least one assumed condition, An output unit that outputs the aforementioned candidate conditions and A management system equipped with the following features.

9. An assumption unit calculates an assumed value of the remaining lifespan of the managed object when at least one assumption condition is applied to the operating conditions of the managed object, based on information that identifies an influencing parameter among the operating parameters included in the operating conditions of the managed object that affects the remaining lifespan of the managed object, and information that identifies the correlation between the remaining lifespan of the managed object and the influencing parameter. An extraction unit extracts as a candidate condition an assumption condition that is applied among the at least one assumption condition when the assumption value of the remaining lifespan of the managed object is greater than or equal to a predetermined period. An operating condition determination device equipped with the following features.

10. An output device that outputs the candidate conditions extracted by the operating condition determination device described in claim 9.

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