Method and system for diagnosing crane

Abstract

To provide a method and a system for diagnosing a crane, which can more faithfully grasp a state of a control system with built-in feedback control, which is to be diagnosed.SOLUTION: This method for diagnosing the crane, which diagnoses a state of a control system 1 with built-in feedback control by a calculation device 12, executes data processing for acquiring a correlation between a command value T* and real power P in a situation where the state of the control system 1 is not abnormal and then estimating an estimation value P* of the real power P on the basis of the command value T* and the acquired correlation by the calculation device 12, executes data processing for acquiring the real power P by a power acquisition unit 11, and calculating deviation ΔP between the estimation value P* and the acquired real power P by the calculation device 12, and diagnoses the state of the control system 1 using the calculated deviation ΔP as an index.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a method and system for diagnosing a crane, and more particularly to a method and system for diagnosing a crane that targets a control system incorporating feedback control and more faithfully grasps the state of the control system. [Background technology]

[0002] When a crane used to load and unload cargo at a logistics facility such as a container terminal breaks down, loading and unloading operations are halted, causing major disruptions to logistics. In particular, when a control device (e.g., PLC) or inverter, which is a long-term delivery item, breaks down, it can take a long time to restore the crane, and loading and unloading operations may be interrupted for a long time. For this reason, it is necessary to regularly inspect and diagnose cranes and maintain them.

[0003] A method for estimating the expected service life of machine parts has been proposed, with machines such as cranes or each part of a crane as the diagnosis target (see, for example, Patent Document 1). This proposed estimation method analyzes failure patterns using a database that accumulates work cycle process data such as the start and end points of the work cycle, equipment speed, load, power, consumption, temperature, and hydraulic conditions.

[0004] In the above proposed estimation method, the process data represents the mechanical operation state of the machine or part, and the useful life of the machine or part is estimated by analyzing the mechanical failure pattern. However, the failure pattern based on the fluctuation of the mechanical operation state cannot grasp the electrical failure tendency inside the control system. In particular, in a crane control system incorporating feedback control, the feedback control causes a correction inside the control system so that the output value of the motor (e.g., rotation speed and output torque) matches the input value (e.g., input value by operation of the operating device). In other words, even if an abnormality that is a precursor of a failure occurs inside the control system, the output value of the motor will show a generally normal value by being corrected inside the control system. Therefore, there is room for improvement in diagnosing a crane control system incorporating feedback control to grasp the state of the control system more faithfully. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2018-14092 A Summary of the Invention [Problem to be solved by the invention]

[0006] An object of the present invention is to provide a method and system for diagnosing a crane that targets a control system incorporating feedback control and grasps the state of the control system more faithfully. [Means for solving the problem]

[0007] The crane diagnosis method of the present invention that achieves the above-mentioned object comprises a crane motor as a control object, and a control device that generates a command value based on the difference between an output value of the motor acquired by an output acquisition device and an input value input to the control device, and uses a calculation device to diagnose the state of a control system incorporating feedback control that drives the motor by causing an inverter to supply power to the motor based on the generated command value, the method being characterized in that, when the state of the control system is not abnormal, a correlation between the command value and an actual power actually supplied from the inverter to the motor based on the command value is acquired, and the calculation device performs data processing to estimate an estimate of the actual power based on the command value and the previously determined correlation, and the actual power is acquired by a power acquisition device, and the calculation device performs data processing to calculate a deviation between the estimated value and the actual power acquired by the power acquisition device, and the calculated deviation is used as an index to diagnose the state of the control system.

[0008] The crane diagnostic system of the present invention that achieves the above-mentioned object includes an electric motor, a control device, an inverter, and an output acquisition device, and the control device executes data processing to generate a command value based on the difference between an output value of the electric motor acquired by the output acquisition device and an input value input to the control device, whereby the inverter supplies power based on the generated command value to the electric motor, thereby diagnosing a state of a control system incorporating feedback control in which the electric motor that is a control target is driven. The crane diagnostic system is characterized in that it includes a power acquisition device that acquires actual power actually supplied from the inverter to the electric motor, and the arithmetic device has a database that indicates the correlation between the command value and the actual power in a situation in which the state of the control system is not abnormal, and is configured to execute the following: data processing to estimate an estimate of the actual power based on the command value and the database, data processing to calculate a deviation between the estimated value and the actual power acquired by the power acquisition device, and data processing to diagnose the state of the control system using the calculated deviation as an index. Effect of the Invention

[0009] According to the present invention, the estimated value represents a theoretical value when the state of the control system is not abnormal, and the actual power represents an actual measurement value when the state of the control system is at the time of diagnosis. That is, the deviation between the estimated value and the actual power indicates the degree of internal correction by feedback control. When the state of the control system is abnormal, the deviation is higher than when the state of the control system is good, and the increase can be regarded as a change in state due to the progression of deterioration of the control system over time or the occurrence of a sudden abnormality. Therefore, using this deviation is advantageous for more faithfully grasping the state of a control system incorporating feedback control. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 is an explanatory diagram illustrating an embodiment of a diagnostic system for a crane. [Diagram 2]FIG. 2 is an explanatory diagram illustrating an example of a database. [Diagram 3] FIG. 1 is a flow chart illustrating the steps of a method for diagnosing a crane. [Figure 4] 3 is a graph illustrating the correlation between the command value and the actual power shown in the database of FIG. 2. [Diagram 5] FIG. 11 is a flow diagram illustrating a procedure in a first modified example of an embodiment. [Figure 6] FIG. 13 is an explanatory diagram illustrating a database used in the first modification example. [Figure 7] 7 is a graph illustrating the correlation between the command value and the actual power for each output value shown in the database of FIG. 6. [Figure 8] FIG. 11 is a flow diagram illustrating a procedure in a second modified example of an embodiment. [Figure 9] 13 is an explanatory diagram illustrating an example of a correlation between a command value and an output value used in Modification 2. FIG. [Figure 10] FIG. 11 is an explanatory diagram illustrating a third modified example of the embodiment. [Figure 11] FIG. 13 is an explanatory diagram illustrating a database used in Modification 3. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS A method and system for diagnosing a crane according to the present invention will be described below based on the embodiments shown in the drawings.

[0012] A method for diagnosing a crane is implemented using an embodiment of a diagnostic system 10 for a crane illustrated in Figure 1. The diagnostic system 10 and the method for diagnosing a control system 1 for a crane are used to diagnose a control system 1 for a crane. That is, the embodiment outputs a diagnosis result for the control system 1, as described below.

[0013] First, the control system 1 will be described.

[0014] The control system 1 controls an electric motor 2 that operates the lifting device of the hoist, the traverse device of the trolley, the traveling device of the structure, and the like during loading and unloading of the crane (not shown). The crane may be any of various known cranes. The crane may be, for example, a transfer crane (gantry crane) that straddles and travels along a storage lane at a container terminal that is a logistics facility. The logistics facility may be any of various known logistics facilities, such as a container terminal (port facility) that is a logistics base, or a warehouse facility. The logistics facility may also include a manufacturing facility that manufactures and ships steel plates and the like. The crane may be an overhead crane, a gantry crane, a jib crane, or a mobile crane (truck crane, railway crane). The operating unit refers to a part that operates during loading and unloading of the crane, and may be a combination of a hoist and a lifting device, a combination of a boom and a swivel device, a combination of a trolley and a traverse device, or a combination of a structure having a girder and a traveling device. The operating unit may be any unit that uses the electric motor 2 as a driving source, and is not particularly limited.

[0015] The control system 1 may be a known system that adjusts the rotation speed or output torque of the electric motor 2 by feedback control. The control system 1 has the electric motor 2, an operating device 3, a control device 4, an inverter 5, and an output acquisition device 6. The control system 1 incorporates feedback control in which the control device 4 generates a command value T* based on the difference between an input value ω* and an output value ω, and the inverter 5 supplies power to the electric motor 2 based on the generated command value T* to drive the electric motor 2. By using the incorporated feedback control, the control system 1 adjusts the output value ω to match the input value ω*.

[0016] The electric motor 2 is connected to the operating part and serves as a drive source for operating the operating part. A known electric motor such as a three-phase induction motor or a synchronous motor can be used as the electric motor 2. A transmission such as a reduction gear may be interposed between the electric motor 2 and the operating part.

[0017] The operation device 3 may be any of various known operation devices that are operated manually, such as a button, a lever, or a handle. The operation device 3 is disposed in a driver's cab installed on the crane or in an operation room installed at a position away from the crane. An input value ω* is generated by operating the operation device 3, and the generated input value ω* is input to the control device 4. The operation device 3 is not essential, and a calculation device that generates the input value ω* may be provided instead of the operation device 3. Also, the calculation device may be used in combination with the operation device 3 to switch between automatic operation by the calculation device and manual operation by the operation device 3.

[0018] The control device 4 may be any of various known computers such as a programmable logic controller (PLC) or a direct digital controller. The control device 4 has a central processing unit (CPU), a main storage unit (memory), an auxiliary storage unit (e.g., HDD), and an input / output unit (e.g., an I / O module or a network adapter). The control device 4 receives the input value ω* input from the operation device 3 and the output value ω acquired by the output acquisition device 6, and generates a command value T* by executing data processing instructed by a predetermined program. The control device 4 may be integrated with the inverter 5.

[0019] The inverter 5 may be any of various known inverters that convert power supplied from a power source such as an AC power source used in a logistics facility or an AC power source installed on a crane, or a DC power source such as a battery installed on a crane, and supply the power to the electric motor 2. The inverter 5 supplies the electric power to the electric motor 2 based on the command value T* generated by the control device 4. The inverter 5 may be integrated with the electric motor 2.

[0020] The output acquisition device 6 acquires the output value ω of the electric motor 2. The output acquisition device 6 may use various known sensors such as a speed sensor (pulse generator). The output value ω indicates the output of the electric motor 2, for example, a rotation speed or an output torque. The output value ω is the same as the input value ω* input to the control device 4, and the output acquisition device 6 selects a sensor according to the input value ω*. When the input value ω* is a rotation speed, the output acquisition device 6 acquires the rotation speed of the electric motor 2 as the output value ω. The output acquisition device 6 may be configured to indirectly acquire the output value ω of the electric motor 2, for example, the output acquisition device 6 may acquire a measured value indicating the state of the operating part (movement amount, moving speed, load, etc.) and acquire the output value ω of the electric motor 2 from the acquired measured value. In this case, the control device 4 may execute data processing to calculate the output value ω of the electric motor 2 based on the measured value acquired by the output acquisition device 6 and a shift in the power transmission path from the electric motor 2 to the operating part.

[0021] In Fig. 1, the input value ω* and the output value ω indicate the rotation speed. The command value T* indicates the torque. Note that the command value T* may be a three-phase current command value iq*.

[0022] The control system 1 is composed of two control systems, a speed control system and a current control system. The speed control system uses feedback control that makes the output value ω equal to the input value ω*. The current control system uses various known vector controls, such as sensorless vector control and vector control with encoder feedback (vector control with PG). The current control system uses, for example, vector control with encoder feedback, and uses the output acquisition device 6 as an encoder. Instead of the output acquisition device 6, a magnetic sensor installed inside the electric motor 2 may be used, or in the case where the output acquisition device 6 is configured to acquire the output torque as the output value ω, a speed sensor separate from the output acquisition device 6 may be used. The control system 1 is not limited to vector control, and may use V / f control.

[0023] Next, an embodiment of the diagnostic system 10 will be described.

[0024] The diagnostic system 10 includes a power acquisition device 11 and a computing device 12. The diagnostic system 10 may have a plurality of control systems 1 to be diagnosed, and can also diagnose a large number of control systems 1 each of a large number of cranes operated in a logistics facility.

[0025] The power acquisition device 11 may be any of various known power acquisition devices, and acquires the actual power actually supplied from the inverter 5 to the motor 2. The power acquisition device 11 has, for example, two voltmeters 11a and two ammeters 11b. The voltmeter 11a may directly acquire the three-phase voltage supplied from the inverter 5 to the motor 2, but it acquires the three-phase voltage command values ​​Vu, Vv, Vw output from the control device 4 to the inverter 5 as three-phase voltages. The ammeter 11b acquires predetermined two-phase currents iu, iw from the three-phase current supplied from the inverter 5 to the motor. The power acquisition device 11 is not particularly limited as long as it can acquire the actual power actually supplied from the inverter 5 to the motor 2, or the actual voltage and actual current. The power acquisition device 11 is not limited to being a single device having the voltmeter 11a and the ammeter 11b, and the voltmeter 11a and the ammeter 11b may be separate devices.

[0026] The arithmetic device 12 may be any of various known computers. The arithmetic device 12 has a central processing unit (CPU) 13, a main storage unit (memory) 14, an auxiliary storage unit (e.g., HDD) 15, and an input / output unit 16 (e.g., a keyboard, a mouse, a display, an I / O module, a network adapter, etc.). A predetermined program and a database D1 are stored in the auxiliary storage unit 15. When a predetermined program is started and executed, the arithmetic device 12 executes each data processing instructed by the program. Then, each data processing is executed and a diagnosis result of the control system 1 is output.

[0027] The computing device 12 may be installed on the crane or may be installed outside the crane. When the computing device 12 is installed outside the crane, the computing device 12, the control device 4, and the power acquisition device 11 are communicatively connected to each other via various known networks. Note that the computing device 12 and the power acquisition device 11 do not need to be directly communicatively connected to each other, and may be indirectly communicatively connected via the control device 4.

[0028] 2 has actual powers P (P1, P2, . . . , Pn) for a large number of command values ​​T* (T*1, T*2, . . . , T*n) in a situation where the state of the control system 1 is not abnormal (a situation where the state of the control system 1 is good). In other words, the database D1 shows the correlation between the command value T* and the actual power P in a situation where the state of the control system 1 is not abnormal.

[0029] More specifically, database D1 accumulates a large number of actual powers P actually supplied from inverter 5 to motor 2 based on the samples of command value T* listed in the left column of the table. The samples of command value T* (T*1, T*2, . . . , T*n) indicate sample numbers (1 to n) in command value T*.

[0030] The real power P is acquired by the power acquisition device 11. The real power P may be indicated by a power value calculated based on three-phase voltage values ​​and three-phase current values, or may be indicated by these three-phase voltage values ​​and three-phase current values.

[0031] The database D1 is created by the arithmetic device 12 using data acquired during the loading and unloading operation of the crane when the state of the control system 1 is not abnormal. Specifically, the command value T* generated by the control device 4 during the loading and unloading operation of the crane is stored in the auxiliary storage unit 15 of the arithmetic device 12. In addition, the actual power P actually supplied from the inverter 5 to the electric motor 2 based on the stored command value T* is acquired by the power acquisition device 11 and stored in the auxiliary storage unit 15 of the arithmetic device 12. The database D1 is created using the command value T* and the actual power P stored in the auxiliary storage unit 15. In addition, the database D1 can use a huge amount of command values ​​T* and actual power P that have been accumulated by manufacturers of cranes and managers of logistics facilities that manage and operate cranes. In addition, the database D1 can also use laboratory data such as the results of numerous experiments and tests in research and development of cranes and the results of computer simulations.

[0032] Next, an embodiment of a method for diagnosing a crane will be described.

[0033] 3, the procedure of the method for diagnosing a crane is as follows: first, the calculation device 12 estimates an estimated value P*, and the power acquisition device 11 acquires an actual power P (S110 to S130). Next, the calculation device 12 calculates a deviation ΔP (S140), and the state of the control system 1 is diagnosed using the calculated deviation ΔP as an index (S150). Each step (S110 to S150) will be described in detail below.

[0034] In step (S110), the arithmetic device 12 executes data processing to estimate an estimated value P* of the actual power P based on the command value T* generated by the control device 4 and the database D1. Specifically, the arithmetic device 12 receives the command value T* generated by the control device 4 based on the input value ω* input to the control device 4 by the operation device 3. Next, the arithmetic device 12 estimates the estimated value P* based on the received command value T* and a correlation (estimation model 20) between the command value T* and the actual power P that is grasped in advance using the database D1.

[0035] The graph diagram showing the correlation between the command value T* and the actual power P shown in FIG. 4 is created based on the database D1. The black dots in FIG. 4 are plotted at the positions corresponding to the command value T* and the actual power P in the database D1. In order to obtain an estimation model 20 (the correlation between the command value T* and the actual power P) from the group of plotted black dots, various known regression analyses such as linear regression using a generalized linear model and nonlinear regression using a nonlinear model can be used. In FIG. 4, the straight line approximating the group of black dots indicates the estimation model 20.

[0036] In step (S120), the power acquisition device 11 acquires the actual power P actually supplied from the inverter 5 to the electric motor 2. The actual power P indicates the power actually supplied from the inverter 5 to the electric motor 2 based on the command value T* used to estimate the estimated value P* in step (S110). Note that steps (S110) and (S120) may be performed in any order, and may be processed in parallel.

[0037] In the step (S130) of determining whether the command value T* has been changed, the arithmetic device 12 executes data processing to determine whether the command value T* generated by the control device 4 has been changed, that is, whether a new input value ω* has been input from the operating device 3 to the control device 4. This step (S130) is not essential and can be omitted. However, by performing this step (130), it becomes possible to acquire multiple actual powers P during the period until the command value T* is changed, and the number of samples (the number of acquired actual powers P) increases. In addition, the rotation speed of the electric motor 2 driven by the power supplied from the inverter 5 based on the command value T* varies over time, and the actual power P also varies over time accordingly. In other words, the number of types of samples (the number of actual powers P with different values) also increases. In this way, using a wide variety of samples is advantageous for improving the reliability of the diagnosis result.

[0038] In the step (S140) of calculating the deviation ΔP, the arithmetic device 12 executes data processing to calculate the deviation ΔP between the estimated value P* and the acquired actual power P. Various known calculation methods can be used to calculate the deviation ΔP. The calculation method is not limited to a method of comparing the estimated value P* and the actual power P one-to-one, and a method of comparing the estimated value P* with a plurality of actual powers P during the period until the command value T* is changed may be used.

[0039] The estimated value P* indicates the theoretical value of the actual power P when the state of the control system 1 is not abnormal, and the actual power P is the actual measured value in the state of the control system 1 at the time of diagnosis. In other words, the deviation ΔP indicates the degree to which the deviation between the estimated value P* and the actual power P is internally corrected by feedback control. The lower the deviation ΔP, the closer the actual power P is to the estimated value P*, and the higher the deviation ΔP, the more the actual power P differs from the estimated value P*.

[0040] As the deviation ΔP, in addition to the difference between the estimated value P* and the actual power P, a known accuracy evaluation index, information criterion, hypothesis test result, etc. can be used. Examples of the accuracy evaluation index include the mean square error, the mean absolute error, the coefficient of determination, and the mean square error. Examples of the information criterion include the minimum description length (MDL), the Bayesian information criterion (BIC), and the Akaike information criterion (AIC). Examples of the hypothesis test include the chi-square test, the Kolmogorov-Smirnov test (KS test), the Anderson-Darling test (AD test), and the Shapiro-Wilk test (SW test). Note that the multiple actual powers P may be used as they are, but a moving average of the period until the command value T* is changed may also be used. In this way, when multiple actual powers P are used, the moving average is used to smooth the actual powers P that vary over time, which is advantageous in suppressing erroneous diagnosis due to the variation of the multiple actual powers P.

[0041] In step (S150), the arithmetic device 12 executes data processing to diagnose the state of the control system 1 using the calculated deviation ΔP as an index. The state of the control system 1 to be diagnosed is the state of electrical processing inside the control system 1. Specifically, it is the acquisition state of the output value ω of the output acquisition device 6, the state of signal transmission and reception between each device, the data processing state in the control device 4, the output state of the inverter 5, etc. When the state of electrical processing inside the control system 1 deteriorates, the actual measurement value inside the control system 1 differs greatly from the theoretical value. The deviation ΔP indicates the degree of deviation between the actual measurement value and the theoretical value, so the magnitude of the deviation ΔP means the deterioration of the state of electrical processing inside the control system 1. Therefore, by using the deviation ΔP as an index, the state of the control system 1 can be grasped. For example, it can be estimated that the lower the deviation ΔP, the better the state of electrical processing within the control system 1, and the higher the deviation ΔP, the more abnormal the state of electrical processing within the control system 1. In this way, it is possible to grasp the state of the control system 1 more faithfully based on the level of the calculated deviation ΔP.

[0042] The degree of the state of the control system 1 (whether it is good or abnormal) is generally understood by a person skilled in the art, such as a crane manufacturer or a manager of a logistics facility that manages and operates the crane, based on the accumulation of data during loading and unloading by the crane, the accumulation of a large amount of experimental and test data, or the accumulation of computer simulation results. It can also be roughly understood based on the accumulation of diagnostic results obtained by continuing to diagnose the crane to which the control system 1 is applied. Using the above-mentioned knowledge of a person skilled in the art and the accumulation of diagnostic results, a state other than a good state of the control system 1 is detected as an abnormal state of the control system 1.

[0043] Specifically, a situation in which the control system 1 is in a good state is defined as a standard, and a threshold value indicating the standard is used. When the deviation degree ΔP is higher than the standard (when the deviation degree ΔP is higher than the threshold value), it can be estimated that the state of the control system 1 is abnormal. In addition, by using the number of times that the deviation degree ΔP becomes higher than the standard, it can be estimated that the state of the control system 1 is abnormal when the number of times becomes greater than a predetermined number. The high and low thresholds (standard levels) of the deviation degree ΔP can be set arbitrarily. The threshold value may be set so that a situation in which the control system 1 is in a good state and a situation in which the control system 1 is in an abnormal state can be compared to grasp an abnormality that occurs suddenly in each device of the control system 1 and deterioration over time of each device. The threshold value may also be calculated by classifying data of a situation in which the control system 1 is in a good state and data of an abnormal state using a classification model generated by machine learning. Similarly, the predetermined number can also be set arbitrarily. The predetermined number may be set so that an abnormality due to deterioration over time can be grasped, not an abnormality that occurs suddenly.

[0044] The diagnostic result is output to the input / output unit 16 of the arithmetic device 12. The output of the diagnostic result from the input / output unit 16 is, for example, displaying the diagnostic result on a monitor, notifying another computer of the diagnostic result, etc. Note that only the diagnostic result indicating that the state of the control system 1 is abnormal may be output, and when an abnormality in the state of the control system 1 is found as the diagnostic result, the arithmetic device 12 may execute data processing to display the diagnostic result on a monitor or notify another computer.

[0045] The diagnosis result may be output as the deviation ΔP. Also, the diagnosis result may be output as the degree of abnormality set according to the difference between the deviation ΔP and the threshold. The degree of abnormality indicates the degree of deterioration over time from a good state (a state without abnormality) of the control system 1. The higher the degree of abnormality, the more abnormal the state of the control system 1 is, and the lower the degree of abnormality, the better the state of the control system 1 is. Also, the diagnosis result can be output as a reduction rate of the power supplied from the inverter 5 to the motor 2 using the deviation ΔP. The diagnosis result can be expressed, for example, as X (X: 1 to 100)% of the maximum power, and the voltage and current may be expressed separately. In this way, rather than expressing the state of the control system 1 as good or abnormal as the diagnosis result, it is advantageous to quantify the state to more specifically grasp the fluctuation of the state of the control system 1.

[0046] In the embodiment, the deviation ΔP between the estimated value P* and the actual power P is used, but the estimated values ​​of the voltage and current of the power supplied from the inverter 5 to the motor 2 may be estimated, and the deviations between the voltage and current may be used as indices to diagnose the state of the control system 1. In this case, the data D1 accumulates three-phase voltages and three-phase currents as the actual power P.

[0047] As described above, according to this embodiment, the estimated value P* represents a theoretical value when the control system 1 is in a good state, and the actual power P represents an actual measurement value when the control system 1 is in a good state at the time of diagnosis. That is, the deviation ΔP between the estimated value P* and the actual power P indicates the degree of internal correction by feedback control. When the control system 1 is in an abnormal state, the deviation ΔP is higher than when the control system 1 is in a good state, and this increase can be regarded as a change in state due to the progression of deterioration of the control system 1 over time or the occurrence of a sudden abnormality. Therefore, using this deviation ΔP is advantageous for more faithfully grasping the state of the control system 1 incorporating feedback control.

[0048] If the devices that perform electrical processing inside the control system 1, such as the control device 4, the inverter 5, or the output acquisition device 6, actually fail and the control system 1 stops functioning, the loading and unloading work of the crane will be interrupted, causing a major disruption to the logistics facility. However, since the control system 1 incorporates feedback control, it is difficult to grasp the failure tendency of those devices, and in the past, time-based maintenance such as replacement and overhaul based mainly on the number of years of use was recommended and performed. According to this embodiment, the failure tendency of the devices can be grasped by grasping the state of the control system 1 more faithfully. Therefore, it is possible to grasp the failure of the devices in advance and perform condition-based maintenance (maintenance performed when the condition deteriorates without performing unnecessary device replacement or maintenance). This makes it possible to significantly reduce the cost required for maintenance compared to after-the-fact maintenance and time-based maintenance, while significantly reducing the frequency of interruptions to loading and unloading work due to equipment failure. In addition, by checking the history of the diagnosis results, it is possible to identify the cause of the failure when the equipment fails, and it is possible to immediately restore the interruption of loading and unloading work due to a sudden failure.

[0049] In particular, by understanding the failure trends of long-term delivery items such as the control device 4 and inverter 5, it is possible to arrange for long-term delivery items in advance. This is advantageous in further shortening the downtime of the crane's loading and unloading operations. In addition, it is possible to review the crane operation plan at the logistics facility based on the diagnosis results, which greatly contributes to improving the long-term productivity of loading and unloading operations.

[0050] Next, a first modified example of the embodiment of the crane diagnostic method and system will be described.

[0051] FIG. 5 shows an example of the procedure of the diagnosis method for a crane in the modified example 1. In the modified example 1, another step (S210) is added to the procedure in FIG. 3 described above. That is, in the procedure of the modified example 1, before estimating the estimated value P* (S110), the calculation device 12 selects (S210) a target estimation model (e.g., estimation model 20b) according to the output value ω from among a plurality of estimation models (20a, 20b, 20c, 20d). Therefore, in the modified example 1, the calculation device 12 estimates the estimated value P* by using the selected target estimation model as the estimation model 20 (S110).

[0052] FIG. 6 shows an example of the database D1a used in the first modification. In the database D1a, output values ​​ω (ω1, ω2, . . . , ωn) are added to the rightmost column of the table in comparison with the database D1 in FIG. 2 described above. When the electric motor 2 is connected to the lifting device of the hoist, the state of the electric motor 2 is a state in which the hoist does not suspend a load and a state in which the hoist suspends a load, and further, there are different states depending on the weight of the load. That is, even for the same command value T*, the output value ω differs depending on the state. By adding the output value ω to the database D1a, it becomes possible to grasp multiple correlations between the command value T* and the actual power P corresponding to each state of the electric motor 2.

[0053] In step (S210), the arithmetic device 12 executes data processing to select a target estimation model (e.g., estimation model 20b) from among a plurality of estimation models (20a, 20b, 20c, 20d) according to the output value ω acquired by the output acquisition device 6. The output value ω acquired by the output acquisition device 6 indicates the actual output of the electric motor 2 driven by the power supplied from the inverter 5 based on the command value T* used to estimate the estimated value P* in step (S110).

[0054] The graph showing the correlation between the command value T* and the actual power P illustrated in FIG. 7 is created based on the database D1a. Each point (white circle, black circle, black square, black triangle) in FIG. 7 is plotted at the corresponding position of the command value T* and the actual power P in the database D1a. The classification (difference) of each point represents the classification (difference) of the output value ω. The output value ω can be classified finely, for example, every 1 m / s, but the more detailed the classification, the larger the number of samples (n) in the database D1a becomes. Therefore, it is preferable to classify the output value ω into a predetermined range. In FIG. 7, the output value ω is classified into four ranges, and the output value ω (rotation speed) belonging to each range decreases in the order of white circle, black circle, black square, and black triangle. To obtain the estimation models 20a-20d (correlation between the command value T* and the actual power P) for each output value ω (each classified range) from each of the plotted point groups, various known regression analyses can be used for each output value ω. In Fig. 7, four straight lines approximating each point group indicate the estimation models 20a-20d for each output value ω.

[0055] Conventionally, for equipment whose state changes, only data in a specific state is extracted, and diagnosis is performed based on the extracted data. For example, in the case of a crane, diagnosis is performed in an empty state (a state in which the hoist does not suspend a load). Therefore, in addition to obtaining only a diagnosis result for a short period in which the equipment is maintained in a specific state, diagnosis results for the remaining states excluding the specific state are not obtained. In the above-mentioned modified example 1, a target estimation model corresponding to the output value ω is selected from the multiple estimation models 20a to 20d (S210), and an estimated value P* of the actual power P is estimated based on the command value T* and the selected target estimation model (S110). Therefore, even if the state of the electric motor 2 changes, the state of the control system 1 can be diagnosed regardless of the state of the electric motor 2 by using an estimation model (correlation) corresponding to the change. As a result, it is advantageous to grasp the state of the control system 1 for a longer period and in a larger number of situations.

[0056] In a crane, the amount of movement from the start point of operation to the end point of operation during the lifting and lowering of the hoist, the traverse of the trolley, and the travel of the structure may be set as an acceleration period, a rated speed period, and a deceleration period using preset fixed acceleration, rated speed, and deceleration. During the acceleration period, the operation of the operating part is unstable, which may cause a decrease in diagnostic accuracy. Therefore, it is desirable for the computing device 12 to prohibit diagnosis of the state of the control system 1 during the acceleration period. This is advantageous for improving diagnostic accuracy.

[0057] Next, a second modified example of the embodiment of the crane diagnostic method and system will be described.

[0058] Fig. 8 shows an example of the procedure of the crane diagnosis method in Modification 2. In Modification 2, other steps (S310 to S340) are added to the procedure in Fig. 3 described above. That is, in the procedure of Modification 2, the arithmetic device 12 calculates the deviation Δω in addition to the calculation of the deviation ΔP (S310 to S340). Therefore, in Modification 2, the arithmetic device 12 diagnoses the state of the control system 1 using a plurality of indexes, the deviation ΔP and the deviation Δω (S150). Details of each step (S310 to S340) will be described below.

[0059] In step (S310), the arithmetic device 12 executes data processing to estimate an estimated value ω** of the output value ω based on the command value T* generated by the control device 4 and the database D1a. Specifically, the arithmetic device 12 receives the command value T* generated by the control device 4 based on the input value ω* input to the control device 4 by the operation device 3. Next, the arithmetic device 12 estimates the estimated value ω** based on the received command value T* and the correlation between the command value T* and the output value ω that is grasped in advance using the database D1a.

[0060] The graph diagram showing the correlation between the command value T* and the output value ω shown in FIG. 9 is created based on the database D1a. The black dots in FIG. 9 are plotted at the positions corresponding to the command value T* and the output value ω in the database D1a. To obtain the correlation between the command value T* and the output value ω from the group of plotted black dots, various known regression analyses can be used. In FIG. 9, the straight line approximating the group of black dots shows the correlation between the command value T* and the output value ω.

[0061] In step (S320), the output value ω output from the electric motor 2 is acquired by the output acquisition device 6. The output value ω indicates the actual output of the electric motor 2 driven by the power supplied from the inverter 5 based on the command value T* used to estimate the estimated value ω** in step (S310).

[0062] Step (S330) is similar to step (S130) shown in FIG. 3 described above, and therefore a detailed description thereof will be omitted.

[0063] In step (S340), the arithmetic device 12 executes data processing to calculate the deviation Δω between the estimated value ω** and the acquired output value ω. The deviation Δω can be calculated using various known calculation methods, similar to the calculation method for the deviation ΔP. The deviation Δω indicates the degree of deviation between the estimated value ω** and the output value ω. The estimated value ω** indicates a theoretical value of the output of the electric motor 2 to which feedback control is applied, and the output value ω is an actual measured value of the output of the electric motor 2 at the time of diagnosis. The lower the deviation Δω, the closer the output value ω is to the estimated value ω**, and the higher the deviation Δω, the greater the difference between the output value ω and the estimated value ω**.

[0064] In the step (S150) of diagnosing the state of the control system 1, the arithmetic device 12 executes data processing to diagnose the state of the control system 1 using both the calculated deviation ΔP and deviation Δω as indexes. The high deviation Δω means a deterioration in the acquisition state of the output value ω of the output acquisition device 6 in the electrical processing inside the control system 1. Therefore, by using the deviation Δω as an index, the acquisition state of the output value ω of the output acquisition device 6 can be grasped. For example, it can be estimated that the lower the deviation Δω, the better the acquisition state of the output value ω of the output acquisition device 6 (state in which the accuracy of the output value ω is high), and the higher the deviation Δω, the more abnormal the acquisition state of the output value ω of the output acquisition device 6 is (state in which the accuracy of the output value ω is low).

[0065] In the diagnosis of the state of the control system 1 using the deviation ΔP as an index, the state of electrical processing inside the control system 1 can be diagnosed, and the electrical processing includes the acquisition state of the output value ω of the output acquisition device 6, the signal transmission and reception state between each device, the data processing state in the control device 4, the output state of the inverter 5, and the like. Therefore, according to the modified example 2, by using the deviation Δω as an index in addition to the deviation ΔP, it is possible to isolate the cause of the abnormality occurring in the control system 1. When the deviation Δω is high, the acquisition state of the output value ω of the output acquisition device 6 has deteriorated, so that the output acquisition device 6 can be presumed to be the cause of the abnormality occurring in the control system 1. When the deviation Δω is low and the deviation ΔP is high, the signal transmission and reception state between each device, the data processing state in the control device 4, the output state of the inverter 5, and the like have deteriorated, so that the control device 4 and the inverter 5 can be presumed to be the cause of the abnormality occurring in the control system 1. In this way, isolating the cause of the abnormality occurring in the control system 1 is advantageous in identifying the device with a tendency to fail.

[0066] Next, a third modified example of the embodiment of the crane diagnostic method and system will be described.

[0067] In a third modified embodiment of a diagnostic system 10 illustrated in Fig. 10, a status acquisition device 7 is added to the configuration of the control system 1 illustrated in Fig. 1. In addition, a database D1b is stored in the auxiliary storage unit 15 of the arithmetic device 12 of the diagnostic system 10.

[0068] The status acquisition device 7 acquires a measured value indicating the state of the operating part operated by driving the electric motor 2 based on the command value T*. The status acquisition device 7 only needs to acquire a measured value, and various known sensors such as a load cell can be used. The status acquisition device 7 uses, for example, a load cell, and acquires the load N acting on the wire rope that hoists and supports the hoist of the crane as a measured value indicating the state of the operating part. Based on the load N acquired by the status acquisition device 7, the control device 4 detects an overload, and stops driving the electric motor 2 when an overload is detected.

[0069] Fig. 11 shows an example of a database D1b used in Modification 3. In database D1b, weights N (N1, N2, . . . , Nn) are added to the rightmost column of the table in database D1 of Fig. 2 described above.

[0070] The procedure of the method for diagnosing a crane in the modified example 3 is similar to the procedure in FIG. 8 described above, and only the data used is different. Therefore, a flow diagram illustrating the procedure of the diagnosing method is omitted. In the procedure in the modified example 3, the calculation device 12 estimates an estimated value N* of the load N using the command value T* and the correlation between the command value T* and the load N previously grasped by the database D1b. Next, the deviation ΔN between the estimated value N* and the load N acquired by the load acquisition device 7 is calculated. Therefore, in the modified example 3, the calculation device 12 diagnoses the state of the control system 1 using multiple indexes, the deviation ΔP and the deviation ΔN.

[0071] The magnitude of the deviation ΔN means a deterioration in the state of acquisition of the load N by the load acquisition device 7. Therefore, by using the deviation ΔN as an index, it is possible to grasp the state of acquisition of the load N by the load acquisition device 7. For example, it can be estimated that the lower the deviation ΔN, the better the state of acquisition of the load N by the load acquisition device 7 (state in which the accuracy of the load N is high), and the higher the deviation ΔN, the more abnormal the state of acquisition of the load N by the load acquisition device 7 (state in which the accuracy of the load N is low).

[0072] As described above, according to the third modification, it is possible to grasp the state of the status acquisition device 7 that acquires the measured values ​​indicating the states of the operating parts that are operated by driving the electric motor 2. This makes it possible to grasp the operating conditions of the operating parts with higher accuracy.

[0073] Although an embodiment of the present invention has been described above, the crane diagnosis method and system of the present invention are not limited to the specific embodiment, and various modifications and changes are possible within the scope of the gist of the present invention.

[0074] In the embodiment described above, the control system 1 has set the electric motor 2 as the controlled object, but the controlled object may be an actuating part. When the controlled object is an actuating part, the input value and the output value may take into consideration the gear ratio in the power transmission path from the electric motor 2 to the actuating part with respect to the output value of the electric motor 2.

[0075] The output acquisition device 6 may acquire the output torque of the electric motor 2. A known torque sensor can be used as the output acquisition device 6. When a torque sensor is used as the output acquisition device 6, the torque acting on the electric motor 2 can be calculated based on the load N acquired by the load acquisition device 7, and the rotation speed of the electric motor 2 can be calculated based on the calculated torque and the output value acquired by the output acquisition device 6. The output acquisition device 6 can also be configured with two types of sensors, a speed sensor and a torque sensor.

[0076] In the above-mentioned modified example 1, the database D1a having the output value ω is used, but the database D1b having the load N can also be used. That is, a plurality of estimation models (correlations) are acquired for each load N, and the calculation device 12 performs data processing to select an object estimation model corresponding to the load N acquired by the load acquisition device 7 from the plurality of estimation models, and the selected object estimation model is used to estimate the estimated value ΔP. Note that a database in which the databases D1a and D1b are integrated can also be used.

[0077] The above-described Modifications 1 to 3 can also be applied together. For example, the estimation model used with Modifications 2 and 3 may be selected from a plurality of estimation models as in Modification 1. By applying Modifications 1 to 3 together, the indexes used to diagnose the state of the control system 1 become more diverse, which is advantageous in identifying the cause of an abnormality that has occurred in the control system 1. [Explanation of symbols]

[0078] 1. Control System 2 electric motor 3 Operating device 4. Control device 5. Inverter 6 Output acquisition device 7 Load acquisition device 10 Diagnostic Systems 11 Power acquisition device 12 Arithmetic unit ω output value ω* Input value T* command value P Real power P* estimate ΔP deviation degree

Claims

1. A method for diagnosing a crane, in which a control system is provided with a feedback control for driving a crane by generating a command value by a control device based on a difference between an output value of the crane motor acquired by an output acquisition device and an input value input to the control device, and supplying power from an inverter to the motor based on the generated command value, comprising the steps of: obtaining a correlation between the command value and an actual power actually supplied from the inverter to the motor based on the command value in a situation where the state of the control system is not abnormal; performing data processing by the arithmetic device to estimate an estimated value of the actual power based on the command value and a correlation therebetween that has been grasped in advance, and acquiring the actual power by a power acquisition device; A crane diagnosis method in which the arithmetic device performs data processing to calculate the degree of deviation between the estimated value and the actual power acquired by the power acquisition device, and the calculated degree of deviation is used as an index to diagnose the condition of the control system.

2. 2. The method for diagnosing a crane according to claim 1, wherein a plurality of correlations that differ for each of the output values ​​are acquired, and data processing is performed by the arithmetic device to select from the plurality of correlations a correlation that corresponds to the output value acquired by the output acquisition device, and the selected correlation is used to estimate the estimated value.

3. A correlation between the command value and the output value is obtained in a state where the control system is not in an abnormal state; performing data processing by the arithmetic unit to estimate an estimated value of the output value based on the command value and a correlation therebetween that has been previously determined; 2. The method for diagnosing a crane as described in claim 1, wherein the arithmetic device performs data processing to calculate a deviation between the estimated value and the output value acquired by the output acquisition device, and a plurality of the deviations are used as indices in diagnosing the state of the control system.

4. obtaining a correlation between the command value and a measurement value indicating a state of an operating part operated by driving the electric motor based on the command value in a situation where the state of the control system is not abnormal; performing data processing by the arithmetic unit to estimate the estimated value of the measurement value based on the command value and a correlation therebetween that has been previously determined; 2. A method for diagnosing a crane as described in claim 1, wherein the arithmetic device performs data processing to calculate a deviation between the estimated value and the measured value acquired by a status acquisition device, and a plurality of the deviations are used as indices in diagnosing the status of the control system.

5. The method for diagnosing a crane according to any one of claims 1 to 4, wherein the computing device determines that the state of the control system is abnormal when the deviation is higher than a reference value.

6. 5. The method for diagnosing a crane according to claim 1, wherein the computing device determines that the state of the control system is abnormal when the number of times the deviation degree becomes higher than the reference value exceeds a predetermined number.

7. A diagnostic system for a crane includes an electric motor, a control device, an inverter, and an output acquisition device, and the control device executes data processing to generate a command value based on a difference between an output value of the electric motor acquired by the output acquisition device and an input value input to the control device, and the inverter supplies power based on the generated command value to the electric motor, thereby diagnosing a state of a control system incorporating feedback control in which the electric motor, which is a control target, is driven, the system comprising: A power acquisition device is provided for acquiring actual power actually supplied from the inverter to the electric motor, The crane diagnostic system is configured so that the arithmetic device has a database indicating the correlation between the command value and the actual power when the state of the control system is not abnormal, and performs data processing to estimate an estimate of the actual power based on the command value and the database, data processing to calculate a deviation between the estimated value and the actual power acquired by the power acquisition device, and data processing to diagnose the state of the control system using the calculated deviation as an index.

8. The database includes a plurality of the correlations for each of the output values, 8. The crane diagnosis system according to claim 7, wherein the calculation device executes data processing to select a correlation corresponding to the output value acquired by the output acquisition device from among the plurality of correlations, and the selected correlation is used in data processing to estimate the estimated value.

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