Crane diagnostic methods and systems

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 diagnoses a control system incorporating feedback control and more accurately grasps the state of the control system.

Background Art

[0002] When a failure occurs in a crane that handles cargo at a logistics facility such as a container terminal, the cargo handling operation is interrupted, causing a major obstacle to the logistics. In particular, when long-lead-time parts such as a control device (e.g., PLC) or an inverter fail, it takes a long time to restore the crane, and there is a risk that the interruption of the cargo handling operation will last for a long time. Therefore, it is necessary to regularly inspect and diagnose the crane for maintenance management.

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

[0004] In the estimation method proposed above, process data represents the mechanical operating state of a machine or component, and the service life of the machine or component is estimated by analyzing the mechanical failure pattern. However, failure patterns based on fluctuations in the mechanical operating state cannot capture the electrical failure trends within the control system. In particular, in crane control systems that incorporate feedback control, the feedback control corrects the motor's output value (e.g., rotational speed or output torque) to match the input value (e.g., input value from the operation of the operating device) within the control system. In other words, even if an abnormality that is a precursor to failure occurs within the control system, the motor's output value will generally show a normal value due to the correction performed within the control system. Therefore, there is room for improvement in diagnosing crane control systems that incorporate feedback control and more accurately understanding the state of that control system. [Prior art documents] [Patent Documents]

[0005] [Patent Document 1] Japanese Patent Publication No. 2018-14092 [Overview of the Initiative] [Problems that the invention aims to solve]

[0006] The object of the present invention is to provide a crane diagnostic method and system that more accurately grasps the state of a control system, which incorporates feedback control, when the control system is the target of the diagnosis. [Means for solving the problem]

[0007] The present invention provides a crane diagnostic method for achieving the above objective, in which a control device diagnoses the state of a control system that incorporates feedback control, in which the electric motor of a crane is the control target, the control device generates a command value based on the difference between the output value of the electric motor acquired by an output acquisition device and the input value input to the control device, and the inverter supplies power to the electric motor based on the generated command value to drive the electric motor, and the state of the control system is diagnosed by a computing device, wherein, in a situation where the state of the control system is not abnormal, the correlation between the command value and the actual power actually supplied to the electric motor from the inverter based on the command value is acquired, and the computing device performs data processing to estimate the actual power based on the command value and the correlation known in advance, and the actual power is acquired by a power acquisition device, and the computing device performs data processing to calculate the degree of deviation between the estimated value and the actual power acquired by the power acquisition device. The state of the control system is defined as the state of electrical processing within the control system, excluding the motor, which is not reflected in the output value due to the correction by the feedback control. The calculated degree of deviation is used as an indicator. Examine It is characterized by its ability to make a decision. The present invention provides a crane diagnostic method in which a control system is diagnoses the state of a control system that incorporates feedback control, in which the electric motor of a crane is the control target, and the control device generates a command value based on the difference between the output value of the electric motor acquired by an output acquisition device and the input value input to the control device, and the inverter supplies power to the electric motor based on the generated command value to drive the electric motor, and the state of the control system is diagnoses the state of the control system in which the state of the control system is not abnormal, and the command value and the actual power actually supplied from the inverter to the electric motor based on the command value are diagnosed by a computing device. The system is characterized by acquiring multiple correlations for each output value, performing data processing to select a correlation from among the multiple correlations corresponding to the output value acquired by the output acquisition device, and estimating an estimated value of the actual power based on the command value and the selected correlation, and acquiring the actual power by the power acquisition device, and performing data processing by the calculation device to calculate the degree of deviation between the estimated value and the actual power acquired by the power acquisition device, and using the calculated degree of deviation as an indicator to diagnose the state of the control system. The present invention provides a crane diagnostic method in which a control system is diagnoses the state of a control system that incorporates feedback control, in which the electric motor of a crane is the control target, and the control device generates a command value based on the difference between the output value of the electric motor acquired by an output acquisition device and the input value input to the control device, and the inverter supplies power to the electric motor based on the generated command value to drive the electric motor, and the state of the control system is diagnoses the state of the control system by an arithmetic unit, and in a situation in which the state of the control system is not abnormal, the correlation between the command value and the actual power actually supplied to the electric motor from the inverter based on the command value and the correlation between the command value and the output value are examined. The system is characterized by the following: the system acquires the command value and performs data processing using the arithmetic unit to estimate the actual power and the output value based on the pre-known correlation between the command value and the respective other values; the system acquires the actual power using the power acquisition device and the output value using the output acquisition device; the system performs data processing to calculate the degree of deviation between the estimated actual power and the actual power acquired by the power acquisition device; and the system performs data processing to calculate the degree of deviation between the estimated output value and the output value acquired by the output acquisition device; and uses the calculated multiple degrees of deviation as indicators to diagnose the state of the control system. The present invention provides a crane diagnostic method in which a control system is diagnoses the state of a control system that incorporates feedback control, in which the control device generates a command value based on the difference between the output value of the motor acquired by an output acquisition device and the input value input to the control device, and the inverter supplies power to the motor based on the generated command value to drive the motor, with the electric motor of the crane as the control target, and the state of the control system is diagnoses the state of the control system by a calculation device, in a situation in which the state of the control system is not abnormal, and the correlation between the command value and the actual power actually supplied to the motor from the inverter based on the command value, and the drive of the motor based on the command value The system is characterized by obtaining the correlation of measured values ​​that indicate the state of the operating parts that are operated by motion, estimating the actual power and the estimated measured values ​​based on the command value and the pre-known correlations, performing data processing by the calculation device to estimate the actual power and the estimated measured values, obtaining the actual power by the power acquisition device, performing data processing to calculate the degree of deviation between the estimated actual power and the actual power obtained by the power acquisition device, and performing data processing by the calculation device to calculate the degree of deviation between the estimated measured values ​​and the measured values ​​obtained by the state acquisition device, and using the calculated multiple degrees of deviation as indicators to diagnose the state of the control system.

[0008] The crane diagnostic system of the present invention, which achieves the above objective, comprises an electric motor, a control device, an inverter, and an output acquisition device, wherein the control device performs data processing to generate a command value based on the difference between the output value of the electric motor acquired by the output acquisition device and the input value input to the control device, the inverter supplies power to the electric motor based on the generated command value, and a calculation device diagnoses the state of a control system that incorporates feedback control driving the electric motor, which is the controlled object, wherein the system comprises a power acquisition device that acquires the actual power actually supplied from the inverter to the electric motor, and the calculation device determines the correlation between the command value and the actual power when the state of the control system is not abnormal. There are multiple such values ​​for each of the aforementioned output values. It has a database, Data processing to select a correlation from among multiple correlations that corresponds to the output value obtained by the output acquisition device, The command value and the selected correlationThe system is characterized by a configuration that performs data processing to estimate the actual power based on the above, data processing to calculate the degree of discrepancy between the estimated value and the actual power obtained by the power acquisition device, and data processing to diagnose the state of the control system using the calculated degree of discrepancy as an indicator. The present invention provides a crane diagnostic system comprising an electric motor, a control device, an inverter, and an output acquisition device, wherein the control device performs data processing to generate a command value based on the difference between the output value of the electric motor acquired by the output acquisition device and the input value input to the control device, the inverter supplies power to the electric motor based on the generated command value, and the system includes a calculation device for diagnosing the state of a control system that incorporates feedback control to drive the electric motor, which is the controlled object, and further comprises a power acquisition device that acquires the actual power actually supplied from the inverter to the electric motor. The arithmetic unit is configured to have a database in which multiple correlations exist for each output value between the command value and the actual power when the state of the control system is not abnormal, and to perform data processing to select a correlation from among the multiple correlations that corresponds to the output value acquired by the output acquisition device, to estimate an estimated value of the actual power based on the command value and the selected correlation, to calculate the degree of deviation between the estimated value and the actual power acquired by the power acquisition device, and to diagnose the state of the control system using the calculated degree of deviation as an indicator. [Effects of the Invention]

[0009] According to this invention, the estimated value represents the theoretical value when the control system is not in an abnormal state, and the actual power represents the measured value when the control system is in a normal state at the time of diagnosis. In other words, the degree of deviation between the estimated value and the actual power indicates the degree to which it has been internally corrected by feedback control. When the control system is in an abnormal state, the degree of deviation is higher than when it is in a good state, and this increase can be considered 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 degree of deviation is advantageous for more accurately understanding the state of a control system that incorporates feedback control. [Brief explanation of the drawing]

[0010] [Figure 1] This is an explanatory diagram illustrating an example of a crane diagnostic system. [Figure 2] This is an explanatory diagram illustrating a database. [Figure 3] This is a flowchart illustrating the procedure for diagnosing a crane. [Figure 4] Figure 2 is a graph illustrating the correlation between command values ​​shown in the database and actual power consumption. [Figure 5] This is a flowchart illustrating the procedure in modified example 1 of the embodiment. [Figure 6] This is an explanatory diagram illustrating the database used in Example 1. [Figure 7] Figure 6 is a graph illustrating the correlation between command values ​​and actual power for each output value shown in the database. [Figure 8]It is a flowchart exemplifying the procedure in Modification 2 of the embodiment. [Figure 9] It is an explanatory diagram exemplifying the correlation between the command value and the output value used in Modification 2. [Figure 10] It is an explanatory diagram exemplifying Modification 3 of the embodiment. [Figure 11] It is an explanatory diagram exemplifying the database used in Modification 3.

Mode for Carrying Out the Invention

[0011] Hereinafter, the crane diagnosis method and system of the present invention will be described based on the embodiments shown in the drawings.

[0012] The crane diagnosis method is implemented using the embodiment of the crane diagnosis system 10 illustrated in FIG. 1. This diagnosis system 10 and diagnosis method are used to diagnose the control system 1 of the crane. That is, according to this embodiment, as will be described later, the diagnosis result of the control system 1 is output.

[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 lifting equipment, the trolley traversing device, the structural travel device, etc., during cargo handling of a crane (not shown). Various known cranes can be used. For example, the crane is a transfer crane (gantry crane) that travels along the storage lanes, straddling the storage lanes, in a container terminal, which is a logistics facility. Various known logistics facilities can be used, such as container terminals (port facilities) and warehouse facilities that serve as logistics hubs. Note that logistics facilities also include manufacturing facilities that manufacture and ship steel plates, etc. As for cranes, overhead cranes, gantry cranes, jib cranes, and mobile cranes (truck cranes, rail cranes) can also be used. The operating part refers to the part that operates during cargo handling of the crane, and includes combinations of lifting equipment and lifting devices, combinations of boom and slewing devices, combinations of trolley and traversing devices, and combinations of structures with girders and travel devices. The operating part only needs to be driven by the electric motor 2 and is not particularly limited.

[0015] The control system 1 can use a known system that adjusts the rotational speed or output torque of the electric motor 2 by feedback control. The control system 1 includes an 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 the input value ω* and the 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. Through 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 section and serves as the driving source for the operation of the operating section. The electric motor 2 can be any known electric motor, such as a three-phase induction motor or a synchronous motor. A gearbox or other transmission may be interposed between the electric motor 2 and the operating section.

[0017] The operating device 3 can be any known operating device that is operated manually, such as a button, lever, or handle. The operating device 3 is located in the operator's cab installed on the crane or in a control room located at a distance from the crane. An input value ω* is generated by operating the operating device 3, and this generated input value ω* is input to the control device 4. The operating device 3 is not mandatory, and a calculation device that generates the input value ω* may be provided instead of the operating device 3. Alternatively, the calculation device and the operating device 3 may be used in combination to switch between automatic operation by the calculation device and manual operation by the operating device 3.

[0018] The control device 4 can be any known computer, such as a programmable logic controller (PLC) or a direct digital controller. The control device 4 has a central processing unit (CPU), main memory, auxiliary storage (e.g., HDD), and input / output unit (e.g., I / O module or network adapter). The control device 4 receives the input value ω* from the operating 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 can be any known inverter that converts power supplied from a power source such as an AC power source used in a logistics facility, an AC power source installed on a crane, or a DC power source such as a battery installed on a crane, and supplies power to the motor 2. The inverter 5 supplies power to the motor 2 based on the command value T* generated by the control device 4. The inverter 5 may be integrated with the motor 2.

[0020] The output acquisition device 6 acquires the output value ω of the electric motor 2. The output acquisition device 6 can use various known sensors such as a speed sensor (pulse generator). The output value ω indicates the output of the electric motor 2, and is, for example, rotational speed or output torque. The output value ω is of the same type as the input value ω* input to the control device 4, and the sensor corresponding to the input value ω* is selected for the output acquisition device 6. If the input value ω* is rotational speed, the output acquisition device 6 acquires the rotational speed of the electric motor 2 as the output value ω. The output acquisition device 6 may also be configured to acquire the output value ω of the electric motor 2 indirectly. For example, the output acquisition device 6 may acquire measured values ​​indicating the state of the operating part (amount of movement, speed of movement, load, etc.) and acquire the output value ω of the electric motor 2 from the acquired measured values. In this case, the control device 4 may perform data processing to calculate the output value ω of the electric motor 2 based on the measured values ​​acquired by the output acquisition device 6 and the speed change in the power transmission path from the electric motor 2 to the operating part.

[0021] In Figure 1, the input value ω* and output value ω represent rotational speed. The command value T* represents torque. Alternatively, the three-phase current command value iq* may be used as the command value T*.

[0022] Control system 1 consists of two control systems: a speed control system and a current control system. The speed control system uses feedback control to match the output value ω to the input value ω*. The current control system uses various known vector control methods, such as sensorless vector control and vector control with encoder feedback (vector control with PG). In the current control system, for example, vector control with encoder feedback is used, and the output acquisition device 6 is used as the encoder. Instead of the output acquisition device 6, a magnetic sensor installed inside the motor 2 may be used, and in the case where the output acquisition device 6 acquires the output torque as the output value ω, a speed sensor separate from the output acquisition device 6 may be used. Control system 1 is not limited to vector control, and V / f control may also be used.

[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 calculation device 12. The diagnostic system 10 may have multiple control systems 1 to be diagnosed, and it is also possible to diagnose multiple control systems 1, each of which is located in a large number of cranes operating in a logistics facility.

[0025] The power acquisition device 11 can use 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 also considers the three-phase voltage command values ​​Vu, Vv, and Vw output from the control device 4 to the inverter 5 as the three-phase voltage and acquires those three-phase voltage command values ​​Vu, Vv, and Vw. The ammeter 11b acquires predetermined two-phase currents iu and 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, or actual voltage and actual current, that is actually supplied from the inverter 5 to the motor 2. Furthermore, the power acquisition device 11 is not limited to a single device having a voltmeter 11a and an ammeter 11b; the voltmeter 11a and the ammeter 11b may be separate devices.

[0026] The arithmetic unit 12 can use various known computers. The arithmetic unit 12 has a central processing unit (CPU) 13, a main memory unit (memory) 14, an auxiliary storage unit (e.g., HDD) 15, and an input / output unit 16 (e.g., a keyboard, mouse, display, I / O module, network adapter, etc.). The auxiliary storage unit 15 stores a predetermined program and a database D1. When the predetermined program is started and executed, the arithmetic unit 12 executes each data processing instructed by that program. After executing each data processing, it outputs the diagnostic result of the control system 1.

[0027] The computing device 12 may be installed on the crane or installed outside the crane. If the computing device 12 is installed outside the crane, the computing device 12, the control device 4, and the power acquisition device 11 are connected to each other via various known networks. The computing device 12 and the power acquisition device 11 do not have to be directly connected to each other for communication; they may be connected indirectly for communication via the control device 4.

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

[0029] More specifically, database D1 contains a large collection of actual power P supplied from inverter 5 to motor 2, based on 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 the sample number (1 to n) for command value T*.

[0030] The actual power P is obtained by the power acquisition device 11. The actual power P may be expressed as a power value calculated based on the three-phase voltage value and the three-phase current value, or it may be expressed as those three-phase voltage value and three-phase current value.

[0031] Database D1 is created by the arithmetic unit 12 using data acquired during crane loading and unloading operations when the control system 1 is functioning normally. Specifically, command values ​​T* generated by the control device 4 during crane loading and unloading operations are stored in the auxiliary storage unit 15 of the arithmetic unit 12. Furthermore, the actual power P supplied from the inverter 5 to the motor 2 based on the stored command values ​​T* is acquired by the power acquisition device 11 and stored in the auxiliary storage unit 15 of the arithmetic unit 12. Database D1 is created using these command values ​​T* and actual power P stored in the auxiliary storage unit 15. Database D1 can also utilize the vast amount of command values ​​T* and actual power P accumulated by crane manufacturers and managers of logistics facilities that manage and operate cranes. Additionally, database D1 can utilize laboratory data such as the results of numerous experiments and tests and computer simulations conducted during crane research and development.

[0032] Next, an embodiment of the crane diagnostic method will be described.

[0033] As illustrated in Figure 3, the procedure for diagnosing the crane is as follows: First, the calculation device 12 estimates the estimated value P* and the power acquisition device 11 acquires the actual power P (S110-S130). Next, the calculation device 12 calculates the deviation degree ΔP (S140), and the calculated deviation degree ΔP is used as an indicator to diagnose the state of the control system 1 (S150). The details of each step (S110-S150) are described below.

[0034] In step (S110), the arithmetic unit 12 performs data processing to estimate the 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 unit 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 operating device 3. Next, the arithmetic unit 12 estimates the estimated value P* based on the received command value T* and the correlation between the command value T* and the actual power P (estimation model 20) which is known in advance using the database D1.

[0035] The graph illustrating the correlation between the command value T* and the actual power P, as exemplified in Figure 4, was created based on database D1. The black dots in Figure 4 are plotted at the corresponding locations of the command value T* and the actual power P in database D1. To derive the estimated model 20 (correlation between the command value T* and the actual power P) from this plotted group of black dots, various known regression analyses can be used, such as linear regression using a generalized linear model or nonlinear regression using a nonlinear model. In Figure 4, the straight line approximating the group of black dots represents the estimated model 20.

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

[0037] In step (S130), which determines whether the command value T* has been changed, the arithmetic unit 12 performs 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 mandatory and can be omitted. However, performing this step (130) makes it possible to acquire multiple actual power P during the period until the command value T* is changed, increasing the number of samples (the number of actual power P acquired). Also, the rotational speed of the motor 2, which is driven by the power supplied from the inverter 5 based on the command value T*, is variable over time, and the actual power P is also variable over time. In other words, the number of types of samples (the number of actual power P with different values) also increases. Using a wide variety of samples in this way is advantageous in improving the reliability of the diagnostic results.

[0038] In the step of calculating the deviation degree ΔP (S140), the arithmetic unit 12 performs data processing to calculate the deviation degree ΔP between the estimated value P* and the acquired actual power P. Various known calculation methods can be used to calculate the deviation degree ΔP. The calculation method is not limited to a method that compares the estimated value P* and the actual power P on a one-to-one basis, but may also be used to compare the estimated value P* with multiple actual power Ps over the period until the command value T* is changed.

[0039] The estimated value P* represents the theoretical value of the actual power P when the control system 1 is not in an abnormal state, while the actual power P is the measured value in the state of the control system 1 at the time of diagnosis. In other words, the deviation degree 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 degree ΔP, the closer the actual power P is to the estimated value P*, and the higher the deviation degree ΔP, the greater the difference between the actual power P and the estimated value P*.

[0040] The deviation ΔP can be the difference between the estimated value P* and the actual power P, as well as known accuracy evaluation indices, information criteria, and hypothesis test results. Examples of accuracy evaluation indices include the mean squared error, mean absolute error, coefficient of determination, and mean squared error. Examples of information criteria include the minimum description length (MDL), Bayesian information criterion (BIC), and Akaike information criterion (AIC). Examples of hypothesis tests include the chi-squared test, Kolmogorov-Smirnov test (KS test), Anderson-Darling test (AD test), and Shapiro-Wilk test (SW test). Multiple actual power values ​​P may be used as is, or a moving average over the period until the command value T* is changed may be used. When using multiple actual power values ​​P in this way, using a moving average is advantageous in suppressing misdiagnosis due to the variability of multiple actual power values ​​P by smoothing the actual power values ​​P that fluctuate over time.

[0041] In step (S150), the calculation unit 12 performs data processing to diagnose the state of the control system 1 using the calculated deviation degree ΔP as an indicator. The state of the control system 1 to be diagnosed is the state of electrical processing within the control system 1. Specifically, this includes the acquisition state of the output value ω of the output acquisition device 6, the transmission and reception state of signals between each device, the data processing state of the control device 4, and the output state of the inverter 5. If the state of electrical processing within the control system 1 deteriorates, the measured values ​​within the control system 1 will differ significantly from the theoretical values. Since the deviation degree ΔP indicates the degree of deviation between the measured value and the theoretical value, a high deviation degree ΔP means that the state of electrical processing within the control system 1 has deteriorated. Therefore, by using the deviation degree ΔP as an indicator, the state of the control system 1 can be grasped. For example, a lower deviation degree ΔP indicates that the electrical processing within the control system 1 is in good condition, while a higher deviation degree ΔP indicates that the electrical processing within the control system 1 is abnormal. In this way, it becomes possible to more accurately understand the state of the control system 1 based on the calculated deviation degree ΔP.

[0042] The degree of the state of control system 1 (whether it is good or abnormal) is generally understood by those skilled in the art, such as crane manufacturers and managers of logistics facilities that manage and operate cranes, based on accumulated data from crane handling operations, numerous experiments and test data, or accumulated computer simulation results. It can also be generally understood based on the accumulation of diagnostic results obtained by continuously diagnosing cranes to which control system 1 is applied. By utilizing the knowledge and accumulated diagnostic results of those skilled in the art as described above, any state other than the good state of control system 1 is detected as an abnormal state of control system 1.

[0043] Specifically, a baseline is set for when the control system 1 is in good condition, and a threshold value is used to indicate this baseline. When the deviation ΔP is higher than the baseline (when the deviation ΔP is higher than the threshold), it can be estimated that the control system 1 is in an abnormal state. Furthermore, the number of times the deviation ΔP has exceeded the baseline can be used to estimate that the control system 1 is in an abnormal state if that number exceeds a predetermined number. The threshold values ​​(baseline levels) for high and low deviation ΔP can be set arbitrarily. The threshold should be set to allow for the identification of sudden abnormalities in each device of the control system 1 and the deterioration of each device over time by comparing the situation when the control system 1 is in good condition with the situation when the control system 1 is in an abnormal state. The threshold can also be calculated by classifying the data for when the control system 1 is in good condition and the data for when it is in an abnormal state using a classification model generated by machine learning. Similarly, the predetermined number can also be set arbitrarily. The predetermined number should be set to allow for the identification of abnormalities due to deterioration over time, rather than sudden abnormalities.

[0044] The diagnostic results are output to the input / output unit 16 of the arithmetic unit 12. The output of the diagnostic results from the input / output unit 16 may include, for example, displaying the diagnostic results on a monitor or notifying other computers of the diagnostic results. Alternatively, only diagnostic results indicating an abnormal state of the control system 1 may be output. If an abnormal state of the control system 1 is detected as a result of the diagnostic, the arithmetic unit 12 may perform data processing to display the diagnostic results on a monitor or notify other computers.

[0045] The diagnostic result may output a deviation degree ΔP. Alternatively, the diagnostic result may output an abnormality level set according to the difference between the deviation degree ΔP and a threshold. The abnormality level indicates the degree to which the control system 1 has deteriorated over time from a good state (a state without abnormalities). A higher abnormality level indicates a more abnormal state of the control system 1, while a lower abnormality level indicates a better state of the control system 1. Furthermore, the diagnostic result can be output as a rate of decrease in the power supplied from the inverter 5 to the motor 2, using the deviation degree ΔP. The diagnostic result can be expressed, for example, as X (X: 1~100)% of the maximum power, and voltage and current may be expressed individually. In this way, instead of expressing the state of the control system 1 as good or abnormal as a diagnostic result, quantifying the state is advantageous for a more concrete understanding of the changes in the state of the control system 1.

[0046] In one embodiment, the degree of deviation ΔP between the estimated value P* and the actual power P was used. However, the state of the control system 1 may also be diagnosed by estimating the voltage and current of the power supplied from the inverter 5 to the motor 2, and using the degree of deviation between the voltage and current as indicators. In this case, the data D1 will include the three-phase voltage and three-phase current as the actual power P.

[0047] As described above, according to this embodiment, the estimated value P* represents the theoretical value when the control system 1 is in a good state, and the actual power P represents the measured value when the control system 1 is in a good state during diagnosis. That is, the degree of 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 degree of deviation ΔP is higher than when it is in a good state, and this increase can be considered 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 degree of deviation ΔP is advantageous for more accurately understanding the state of the control system 1 in which feedback control is incorporated.

[0048] If the electrical processing equipment within the control system 1, such as the control device 4, inverter 5, or output acquisition device 6, actually fails and the control system 1 ceases to function, crane loading and unloading operations will be interrupted, causing significant disruption to the logistics facility. However, because the control system 1 incorporates feedback control, it is difficult to grasp the failure tendencies of these devices. Conventionally, time-based maintenance, such as replacement or overhaul based mainly on years of service, has been recommended and implemented. According to this embodiment, by more accurately understanding the state of the control system 1, the failure tendencies of the equipment can be grasped. Therefore, equipment failures can be identified in advance, and condition-based maintenance (maintenance performed when the condition deteriorates, without unnecessary equipment replacement or maintenance) can be implemented. This significantly reduces the cost of maintenance compared to corrective maintenance or time-based maintenance, while also significantly reducing the frequency of interruptions to loading and unloading operations due to equipment failures. Furthermore, by checking the history of diagnostic results, it becomes possible to identify the cause of equipment failure at the time of the failure, enabling immediate recovery from interruptions to loading and unloading operations due to sudden failures.

[0049] In particular, by understanding the failure trends of long-lead-time components such as the control device 4 and inverter 5, it becomes possible to arrange for these components in advance. This is advantageous in further reducing the time spent interrupting crane loading and unloading operations. Furthermore, based on the diagnostic results, it becomes possible to revise the crane operation plan at the logistics facility, which greatly contributes to improving the long-term productivity of loading and unloading operations.

[0050] Next, a modified example 1 of the crane diagnostic method and system will be described.

[0051] Figure 5 shows an example of the procedure for diagnosing a crane in Modification 1. In Modification 1, another step (S210) is added to the procedure in Figure 3 described above. That is, in the procedure of Modification 1, before estimating the estimated value P* (S110), the computing device 12 selects a target estimation model (for example, estimation model 20b) from among several estimation models (20a, 20b, 20c, 20d) according to the output value ω (S210). Therefore, in Modification 1, the computing device 12 uses the selected target estimation model as estimation model 20 to estimate the estimated value P* (S110).

[0052] Figure 6 shows an example of database D1a used in Modification 1. Database D1a is the same as database D1 in Figure 2 described above, but with the addition of output values ​​ω (ω1, ω2, ..., ωn) in the far right column of the table. When the motor 2 is connected to the lifting device of the lifting sling, the state of the motor 2 can be either when the lifting sling is not lifting a load or when the lifting sling is lifting a load, and furthermore, each state differs depending on the weight of the load. That is, even with the same command value T*, the output value ω will differ depending on the state. By adding the output value ω to database D1a, it becomes possible to grasp multiple correlations between the command value T* and the actual power P according to each state of the motor 2.

[0053] In step (S210), the arithmetic unit 12 performs data processing to select a target estimation model (for example, estimation model 20b) from among multiple 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 represents the actual output of the electric motor 2 driven by power supplied from the inverter 5, based on the command value T* used to estimate the estimation value P* in step (S110).

[0054] The graph illustrating the correlation between the command value T* and the actual power P, as illustrated in Figure 7, was created based on database D1a. Each point in Figure 7 (white circle, black circle, black square, black triangle) is plotted at the corresponding location of the command value T* and actual power P in database D1a. The classification (difference) of each point represents the classification (difference) of the output value ω. The output value ω can be classified in detail, for example, every 1 m / s, but the more detailed the classification, the larger the sample size (n) in database D1a becomes. Therefore, it is preferable to classify the output value ω into predetermined ranges. In Figure 7, the output value ω is classified into four ranges, with the output value ω (rotational speed) belonging to each range decreasing in the order of white circle, black circle, black square, and black triangle. To obtain the estimated models 20a to 20d (correlation between command value T* and actual power P) for each output value ω (for each classified range) from these plotted point clouds, various known regression analyses can be used for each output value ω. In Figure 7, the four straight lines approximating each point cloud represent the estimated models 20a to 20d for each output value ω.

[0055] Conventionally, for equipment whose state changes, only data from a specific state was extracted, and diagnosis was performed based on the extracted data. For example, with cranes, diagnosis was performed in the unloaded state (when the lifting device is not lifting a load). Therefore, in addition to only obtaining diagnostic results for a short period during which the equipment was maintained in a specific state, diagnostic results for the remaining states excluding the specific state were not obtained. In the above-described modified example 1, a target estimation model corresponding to the output value ω is selected from among 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 motor 2 changes, the state of the control system 1 can be diagnosed regardless of the state of the motor 2 by using an estimation model (correlation) corresponding to that change. As a result, it is advantageous to grasp the state of the control system 1 over a longer period and under a larger number of conditions.

[0056] In cranes, the amount of movement from the start point to the end point of operation during the lifting and lowering of the lifting equipment, the traversing of the trolley, and the movement of the structure may be set using pre-set fixed acceleration, rated speed, and deceleration periods, respectively. During the acceleration period, the operation of the moving parts may be unstable, which may reduce diagnostic accuracy. Therefore, it is desirable for the calculation device 12 to prohibit the diagnosis of the state of the control system 1 during the acceleration period. This is advantageous for improving diagnostic accuracy.

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

[0058] Figure 8 shows an example of the procedure for diagnosing the crane in Modified Example 2. In Modified Example 2, an additional step (S310-S340) is added to the procedure in Figure 3 described above. That is, in the procedure of Modified Example 2, the deviation degree Δω is calculated by the calculation device 12 separately from the calculation of the deviation degree ΔP (S310-S340). Therefore, in Modified Example 2, the state of the control system 1 is diagnosed by the calculation device 12 using multiple indicators, namely the deviation degree ΔP and the deviation degree Δω (S150). The details of each step (S310-S340) are described below.

[0059] In step (S310), the arithmetic unit 12 performs data processing to estimate the 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 unit 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 operating device 3. Next, the arithmetic unit 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 which is known in advance using the database D1a.

[0060] The graph illustrating the correlation between command value T* and output value ω, as illustrated in Figure 9, was created based on database D1a. The black dots in Figure 9 are plotted at the corresponding locations of command value T* and output value ω in database D1a. Various known regression analyses can be used to determine the correlation between command value T* and output value ω from this plotted group of black dots. In Figure 9, the straight line approximating the group of black dots represents the correlation between command value T* and output value ω.

[0061] In step (S320), the output value ω output from the motor 2 is acquired by the output acquisition device 6. The output value ω represents the actual output of the 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 the same as step (S130) shown in Figure 3 above, so a detailed explanation is omitted.

[0063] In step (S340), the arithmetic unit 12 performs data processing to calculate the degree of deviation Δω between the estimated value ω** and the acquired output value ω. Various known calculation methods can be used to calculate the degree of deviation Δω, similar to the method for calculating the degree of deviation ΔP. The degree of deviation Δω indicates the degree of deviation between the estimated value ω** and the output value ω. The estimated value ω** represents the theoretical value of the output of the motor 2 to which feedback control is applied, and the output value ω is the measured value of the output of the motor 2 at the time of diagnosis. The lower the degree of deviation Δω, the closer the output value ω is to the estimated value ω**, and the higher the degree of deviation Δω, the greater the difference between the output value ω and the estimated value ω**.

[0064] In step S150, which diagnoses the state of the control system 1, the arithmetic unit 12 performs data processing to diagnose the state of the control system 1, using both the calculated deviation degree ΔP and deviation degree Δω as indicators. A high deviation degree Δω indicates a deterioration in the acquisition state of the output value ω of the output acquisition device 6 within the electrical processing inside the control system 1. Therefore, by using the deviation degree Δω as an indicator, the acquisition state of the output value ω of the output acquisition device 6 can be grasped. For example, the lower the deviation degree Δω, the better the acquisition state of the output value ω of the output acquisition device 6 (high accuracy of the output value ω), and the higher the deviation degree Δω, the more abnormal the acquisition state of the output value ω of the output acquisition device 6 (low accuracy of the output value ω).

[0065] Diagnosing the state of control system 1 using deviation degree ΔP as an indicator allows for diagnosis of the state of electrical processing within control system 1. This 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 of the control device 4, and the output state of the inverter 5. Therefore, according to Modification 2, by using deviation degree Δω in addition to deviation degree ΔP as an indicator, it becomes possible to isolate the cause of the abnormality occurring in control system 1. If deviation degree Δω is high, the acquisition state of the output value ω of the output acquisition device 6 has deteriorated, and therefore it can be estimated that the output acquisition device 6 is the cause of the abnormality occurring in control system 1. If deviation degree Δω is low and deviation degree ΔP is high, the signal transmission and reception state between each device, the data processing state of the control device 4, and the output state of the inverter 5 have deteriorated, and therefore it can be estimated that the control device 4 and inverter 5 are the cause of the abnormality occurring in control system 1. Isolating the cause of the abnormality occurring in control system 1 in this way is advantageous for identifying the device prone to failure.

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

[0067] In the third modified example of the diagnostic system 10 illustrated in Figure 10, a state acquisition device 7 is added to the configuration of the control system 1 in Figure 1 described above. In addition, the auxiliary storage unit 15 of the arithmetic unit 12 of the diagnostic system 10 stores the database D1b.

[0068] The status acquisition device 7 acquires measured values ​​indicating the state of the operating part that is driven by the motor 2 based on the command value T*. The status acquisition device 7 only needs to be able to acquire measured values, and various known sensors such as load cells can be used. For example, the status acquisition device 7 uses a load cell and acquires the load N acting on the wire rope that lifts and supports the crane's lifting device 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 overload, and stops the drive of the motor 2 when an overload is detected.

[0069] Figure 11 shows an example of database D1b used in Modification 3. Database D1b is the same as database D1 in Figure 2 described above, but with the addition of load N(N1, N2, ..., Nn) in the far right column of the table.

[0070] The procedure for diagnosing the crane in Modification 3 is the same as the procedure in Figure 8 described above, except that the data used is different. Therefore, a flowchart illustrating the procedure for this diagnosis method is omitted. In the procedure of Modification 3, the calculation unit 12 estimates the estimated value N* of the load N using the command value T* and the correlation between the command value T* and the load N, which is known in advance from the database D1b. Next, the deviation degree ΔN between the estimated value N* and the load N acquired by the load acquisition device 7 is calculated. Therefore, in Modification 3, the calculation unit 12 diagnoses the state of the control system 1 using multiple indicators, namely the deviation degree ΔP and the deviation degree ΔN.

[0071] A high deviation degree ΔN indicates a deterioration in the load acquisition state of the load acquisition device 7. Therefore, by using the deviation degree ΔN as an indicator, the load acquisition state of the load acquisition device 7 can be understood. For example, a lower deviation degree ΔN indicates that the load acquisition state of the load acquisition device 7 is good (high accuracy of load N), while a higher deviation degree ΔN indicates that the load acquisition state of the load acquisition device 7 is abnormal (low accuracy of load N).

[0072] As described above, according to Modification 3, the state of the state acquisition device 7, which acquires measured values ​​indicating the state of the operating part driven by the electric motor 2, can also be grasped. This makes it possible to grasp the operating status of the operating part with higher accuracy.

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

[0074] In the embodiments described above, the control target of the control system 1 was the electric motor 2, but the control target may also be the actuation unit. When the control target is the actuation unit, the input and output values ​​should be determined by considering the gear ratio in the power transmission path from the electric motor 2 to the actuation unit in relation 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 rotational 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 composed of two types of sensors, a speed sensor and a torque sensor.

[0076] In the above-described modification 1, a database D1a containing output values ​​ω was used, but a database D1b containing loads N can also be used. That is, multiple estimation models (correlation relationships) are acquired for each load N, and the calculation unit 12 performs data processing to select a target estimation model from among the multiple estimation models according to the load N acquired by the load acquisition device 7, and the selected target estimation model is used to estimate the estimated value ΔP. Note that a database integrating databases D1a and D1b can also be used.

[0077] The above-described variations 1 to 3 can also be applied in combination. For example, the estimation model used with variations 2 and 3 may be selected from among multiple estimation models, as in variation 1. By applying variations 1 to 3 in combination, the indicators used to diagnose the state of the control system 1 become more diverse, which is advantageous in identifying the cause of any abnormalities that occur 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 Actual Power P* Estimate ΔP deviation degree

Claims

1. In a crane diagnostic method in which a control system is diagnoses the state of a control system that incorporates feedback control, in which the electric motor of a crane is the control target, the control device generates a command value based on the difference between the output value of the electric motor acquired by an output acquisition device and the input value input to the control device, and the inverter supplies power to the electric motor according to the generated command value to drive the electric motor, the state of the control system is diagnosed by a computing device, While the state of the control system is not abnormal, the correlation between the command value and the actual power actually supplied from the inverter to the motor based on that command value is obtained. The calculation device performs data processing to estimate the actual power based on the command value and its pre-established correlation, and the actual power is acquired by the power acquisition device. A crane diagnostic method comprising: performing data processing by the calculation device to calculate the degree of deviation between the estimated value and the actual power obtained by the power acquisition device; and diagnosing the state of the control system, using the calculated degree of deviation as an indicator, the state of electrical processing within the control system, excluding the electric motor, which is not reflected in the output value due to correction by the feedback control.

2. In a crane diagnostic method in which a control system is diagnoses the state of a control system that incorporates feedback control, in which the electric motor of a crane is the control target, the control device generates a command value based on the difference between the output value of the electric motor acquired by an output acquisition device and the input value input to the control device, and the inverter supplies power to the electric motor according to the generated command value to drive the electric motor, the state of the control system is diagnosed by a computing device, When the state of the control system is not abnormal, multiple correlations between the command value and the actual power actually supplied from the inverter to the motor based on that command value are acquired for each output value. The arithmetic unit performs data processing to select a correlation from among a plurality of correlations corresponding to the output value obtained by the output acquisition device, and data processing to estimate the actual power based on the command value and the selected correlation, and the actual power is acquired by the power acquisition device. A method for diagnosing a crane, comprising: performing data processing using the calculation device to calculate the degree of deviation between the estimated value and the actual power obtained by the power acquisition device, and using the calculated degree of deviation as an indicator to diagnose the state of the control system.

3. In a crane diagnostic method in which a control system is diagnoses the state of a control system that incorporates feedback control, in which the electric motor of a crane is the control target, the control device generates a command value based on the difference between the output value of the electric motor acquired by an output acquisition device and the input value input to the control device, and the inverter supplies power to the electric motor according to the generated command value to drive the electric motor, the state of the control system is diagnosed by a computing device, While the state of the control system is not abnormal, the correlation between the command value and the actual power actually supplied from the inverter to the motor based on the command value, and the correlation between the command value and the output value are obtained. The arithmetic unit performs data processing to estimate the actual power and the output value based on the command value and the respective correlations known in advance, and acquires the actual power by the power acquisition device and the output value by the output acquisition device. A method for diagnosing a crane, comprising: performing data processing by the calculation device to calculate the degree of deviation between the estimated value of the estimated actual power and the actual power obtained by the power acquisition device; and performing data processing to calculate the degree of deviation between the estimated value of the estimated output value and the output value obtained by the output acquisition device; and diagnosing the state of the control system using a plurality of the calculated degree of deviation as an indicator.

4. In a crane diagnostic method in which a control system is diagnoses the state of a control system that incorporates feedback control, in which the electric motor of a crane is the control target, the control device generates a command value based on the difference between the output value of the electric motor acquired by an output acquisition device and the input value input to the control device, and the inverter supplies power to the electric motor according to the generated command value to drive the electric motor, the state of the control system is diagnosed by a computing device, While the state of the control system is not abnormal, the correlation between the command value and the actual power actually supplied from the inverter to the motor based on the command value, and the correlation between the command value and the measured value indicating the state of the operating part that operates due to the drive of the motor based on the command value are obtained, The calculation device performs data processing to estimate the actual power and the measured value based on the command value and the respective correlations known in advance, and the actual power is acquired by the power acquisition device. A method for diagnosing a crane, comprising: performing data processing by the calculation device to calculate the degree of deviation between the estimated value of the estimated actual power and the actual power obtained by the power acquisition device; and performing data processing to calculate the degree of deviation between the estimated value of the estimated measured value and the measured value obtained by the state acquisition device; and using a plurality of the calculated degree of deviation as an indicator to diagnose the state of the control system.

5. A method for diagnosing a crane according to any one of claims 1 to 4, wherein the calculation device determines that the state of the control system is abnormal when the degree of deviation is higher than a standard, or when the number of times the degree of deviation has been higher than a predetermined number has exceeded a predetermined number.

6. The method for diagnosing a crane according to any one of claims 1 to 4, wherein the state of the control system is selected from the state of acquiring the output value of the output acquisition device, the state of transmitting and receiving signals between each device of the control system, the state of data processing in the control device, and the output state of the inverter.

7. A diagnostic system for a crane, comprising an electric motor, a control device, an inverter, and an output acquisition device, wherein the control device performs data processing to generate a command value based on the difference between the output value of the electric motor acquired by the output acquisition device and the input value input to the control device, the inverter supplies power to the electric motor based on the generated command value, and a calculation device for diagnosing the state of a control system that incorporates feedback control driving the electric motor, which is the controlled object, The system includes a power acquisition device that acquires the actual power supplied from the inverter to the motor, A crane diagnostic system having a arithmetic unit that has a database showing the correlation between the command value and the actual power when the state of the control system is not abnormal, and which performs data processing to estimate the actual power based on the command value and the database, data processing to calculate the degree of deviation between the estimated value and the actual power obtained by the power acquisition device, and data processing to diagnose the state of the control system, excluding the motor, which is the state of electrical processing inside the control system that does not appear in the output value due to correction by the feedback control, using the calculated degree of deviation as an indicator.

8. A diagnostic system for a crane, comprising an electric motor, a control device, an inverter, and an output acquisition device, wherein the control device performs data processing to generate a command value based on the difference between the output value of the electric motor acquired by the output acquisition device and the input value input to the control device, the inverter supplies power to the electric motor based on the generated command value, and a calculation device for diagnosing the state of a control system that incorporates feedback control driving the electric motor, which is the controlled object, The system includes a power acquisition device that acquires the actual power supplied from the inverter to the motor, A diagnostic system for a crane, wherein the calculation unit has a database containing multiple correlations between the command value and the actual power for each output value when the state of the control system is not abnormal, and performs data processing to select a correlation from among the multiple correlations corresponding to the output value acquired by the output acquisition device, data processing to estimate an estimated value of the actual power based on the command value and the selected correlation, data processing to calculate the degree of 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 degree of deviation as an indicator.

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