CN116348406B - Fault diagnosis device for elevator - Google Patents

Fault diagnosis device for elevator Download PDF

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Publication number
CN116348406B
CN116348406B CN202080106615.6A CN202080106615A CN116348406B CN 116348406 B CN116348406 B CN 116348406B CN 202080106615 A CN202080106615 A CN 202080106615A CN 116348406 B CN116348406 B CN 116348406B
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time
model
structural
unit
parameter
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CN116348406A (en
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古谷史郎
志贺谕
长德典宏
远山泰弘
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Mitsubishi Electric Building Solutions Corp
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Mitsubishi Electric Building Solutions Corp
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
    • B66B5/00—Applications of checking, fault-correcting, or safety devices in elevators
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring

Abstract

Provided is a fault diagnosis device for an elevator, which can improve the detection precision of a sign of a fault of the device. The elevator fault diagnosis device is provided with: a structure model unit that acquires information of a plurality of detection values detected by a detector of an apparatus mounted on an elevator for each apparatus, and calculates a structure parameter of a structure model of the apparatus using the plurality of detection values and the structure model; a time model unit that calculates a time parameter of a time model of the device to be subjected to calculation using the time model and the structural parameter calculated by the structural model unit; a threshold value generation unit that generates a threshold value of the target device corresponding to the time parameter using the time parameter calculated by the time model unit; and a warning diagnosis unit that diagnoses whether or not a warning of a failure has occurred in the target device, using either one of the structural parameter calculated by the structural model unit and the time parameter calculated by the time model unit and the threshold value generated by the threshold value generation unit.

Description

Fault diagnosis device for elevator
Technical Field
The present invention relates to a fault diagnosis device for an elevator.
Background
Patent document 1 discloses a fault diagnosis device for an elevator. The fault diagnosis device diagnoses whether or not a fault is predicted based on how much the state quantity of the device to be diagnosed is deviated from the past state quantity distribution.
Prior art literature
Patent literature
Patent document 1: japanese patent laid-open No. 2019-008354
Disclosure of Invention
Problems to be solved by the invention
However, the failure diagnosis device described in patent document 1 uses a calculation model common to a plurality of devices to diagnose whether or not the device has a sign of failure. Therefore, the accuracy of diagnosing the sign of the failure of the device is not high.
The present invention has been made to solve the above-described problems. The invention aims to provide a fault diagnosis device of an elevator, which can improve the diagnosis accuracy of a sign of device fault.
Means for solving the problems
The elevator fault diagnosis device of the invention comprises: a structure model unit that acquires information of a plurality of detection values detected by a detector of an apparatus mounted on an elevator for each apparatus, and calculates a structure parameter of a structure model of the apparatus using the plurality of detection values and the structure model; a time model unit that calculates a time parameter of a time model of the device to be subjected to calculation using the time model and the structural parameter calculated by the structural model unit; a threshold value generation unit that generates a threshold value of the target device corresponding to the time parameter using the time parameter calculated by the time model unit; and a warning diagnosis unit that diagnoses whether or not a warning of a failure has occurred in the target device, using either one of the structural parameter calculated by the structural model unit and the time parameter calculated by the time model unit and the threshold value generated by the threshold value generation unit.
The structure model unit calculates a structure parameter of a structure model of the device to be subjected to regression analysis using the first detection values of the plurality of detection values detected by the detector and the second detection values of the plurality of detection values detected by the detector, with respect to the structure model in which the estimation values of the second detection values are output by inputting the first detection values.
Alternatively, the structure model unit calculates a plurality of structure parameters using the structure model and a plurality of detection values detected by the detector at different detection dates and times, and the time model unit calculates a time parameter of the time model by performing regression analysis using the plurality of structure parameters calculated by the structure model unit and a plurality of detection dates and times corresponding to the plurality of structure parameters, with respect to the time model in which an estimated value of the structure parameter corresponding to the detection date and time is output by inputting an elapsed time from the reference date and time to the detection date and time.
Alternatively, the structure model unit calculates a plurality of structure parameters using the structure model and a plurality of detection values detected by the detector at different detection dates and times, and the threshold value generating unit obtains information of the plurality of structure parameters generated by the structure model unit and information of the time parameter generated by the time model unit, calculates a deviation value indicating how much the values of the plurality of structure parameters deviate from the time model to which the time parameter is applied, and generates a threshold value of the structure parameter using the time model and the deviation value.
Effects of the invention
According to the present invention, the warning diagnosis unit diagnoses whether or not the warning of the occurrence of the fault is present in the target device, using a threshold function corresponding to the time parameter of the time model of the target device. Therefore, the accuracy of diagnosing the sign of the failure of the device can be improved.
Drawings
Fig. 1 is a diagram showing an outline of an elevator system and a diagnostic system to which the fault diagnosis apparatus of embodiment 1 is applied.
Fig. 2 is a block diagram of the fault diagnosis apparatus according to embodiment 1.
Fig. 3 is a graph showing transition of time difference values of speed command values at each start detected in the drive device to which the fault diagnosis apparatus of embodiment 1 is applied.
Fig. 4 is a graph showing the transition of the speed deviation value at each start detected in the drive device to which the failure diagnosis device of embodiment 1 is applied.
Fig. 5 is a graph showing transition of the correction torque value at each start detected in the drive device to which the failure diagnosis device of embodiment 1 is applied.
Fig. 6 is a graph showing the transition of the q-axis current value at each start-up detected in the drive device to which the fault diagnosis device of embodiment 1 is applied.
Fig. 7 is a graph showing an example of a time model stored in the fault diagnosis apparatus according to embodiment 1.
Fig. 8 is a graph showing an example of the threshold value generated by the fault diagnosis apparatus according to embodiment 1.
Fig. 9 is a flowchart for explaining the precursor diagnosis operation performed by the fault diagnosis apparatus according to embodiment 1.
Fig. 10 is a graph showing a modification of the threshold value generated by the fault diagnosis apparatus according to embodiment 1.
Fig. 11 is a hardware configuration diagram of the fault diagnosis apparatus according to embodiment 1.
Fig. 12 is a block diagram of a fault diagnosis apparatus according to embodiment 2.
Detailed Description
The manner in which the invention can be practiced is described with reference to the accompanying drawings. In the drawings, the same or corresponding portions are denoted by the same reference numerals. Repeated description of this portion is appropriately simplified or omitted.
Embodiment 1.
Fig. 1 is a diagram showing an outline of an elevator system and a diagnostic system to which the fault diagnosis apparatus of embodiment 1 is applied.
As shown in fig. 1, a plurality of elevator systems 1 are provided in a plurality of buildings 2, respectively. For example, the plurality of elevator systems 1 each have the same configuration.
In the elevator system 1, a hoistway 3 penetrates each floor of a building 2. The machine room 4 is disposed directly above the hoistway 3. The plurality of landing 5 are provided on each floor of the building 2. The plurality of landings 5 are respectively opposed to the hoistway 3. The plurality of landing doors 6 are provided at the entrances and exits of the plurality of landing stations 5. The driving device 7 is provided in the machine room 4.
The main rope 8 is wound around the driving device 7. The car 9 is disposed inside the hoistway 3. The car 9 is suspended on one side of the main rope 8. The counterweight 10 is disposed inside the hoistway 3. A counterweight 10 is suspended from the other side of the main ropes 8.
A plurality of detectors 11 are mounted to the drive device 7.
The control device 12 is provided in the machine room 4. The control device 12 is arranged to be able to control the elevator as a whole.
The monitoring device 13 is provided in the machine room 4. The monitoring device 13 is arranged to be able to monitor the status of the elevator on the basis of information from the control device 12.
The plurality of detectors 11 detect a plurality of detection values related to the operation of the driving device 7. The control device 12 rotates the driving device 7 based on a plurality of detection values from the plurality of detectors 11. The main rope 8 moves following the rotation of the driving device 7. The car 9 and the counterweight 10 move up and down in opposite directions within the hoistway 3 following the movement of the main ropes 8. The user moves between the car 9 and the landing 5 via the landing door 6.
The information center 100 is provided at a location apart from the building 2 where the elevator system 1 is provided. For example, the information center 100 is provided in a maintenance company of an elevator.
The diagnostic system 50 is disposed in an information center 100. The diagnostic system 50 includes a data storage device 20, a display device 21, a data collection device 22, and a fault diagnosis device 30.
The data storage 20 stores information.
For example, the display device 21 includes a display screen. The display screen displays information.
The data collection device 22 collects information of a plurality of detection values from the plurality of monitoring devices 13, respectively. The plurality of detection values are values detected for each of the plurality of driving devices 7. The plurality of detection values are values detected at each start-up. The start-up is an operation in a period from the start of the rotational movement of the driving device 7 to the stop thereof. The data collection device 22 causes the data storage device 20 to store information of a plurality of detection values.
The failure diagnosis device 30 performs a predictive diagnosis using information of a plurality of detection values of the data storage device 20. The failure diagnosis device 30 diagnoses whether or not a failure sign of the drive device is detected in the sign diagnosis. The fault diagnosis device 30 generates diagnosis information, which is the result of the precursor diagnosis. The fault diagnosis device 30 causes the display device 21 to display diagnosis information.
Next, the fault diagnosis device 30 will be described with reference to fig. 2.
Fig. 2 is a block diagram of the fault diagnosis apparatus according to embodiment 1.
As shown in fig. 2, the fault diagnosis apparatus 30 includes a data acquisition unit 31, a structure model unit 32, a time model unit 33, a threshold generation unit 34, and a precursor diagnosis unit 35.
The data acquisition unit 31 acquires information of a plurality of detection values from the data storage device 20.
The structure model section 32 stores information of the structure model. The structural model is a model that estimates the detection value of the driving device 7. For example, the structural model is expressed by a numerical expression.
In the structural model, the inputs are a plurality of first detection values as explanatory variables. In the structural model, the output is an estimated value of the second detection value as the target variable. The plurality of first detection values are selected from a plurality of detection values. The second detection value is one detection value other than the first detection values selected from the plurality of detection values. The structural model is constructed based on characteristics between a plurality of first detection values and second detection values in the driving device 7. Specifically, the characteristic is a mechanical characteristic, an electrical characteristic, a characteristic in control, or the like.
The structure model unit 32 acquires information of the plurality of first detection values and the plurality of second detection values from the data acquisition unit 31. The structure model unit 32 substitutes a plurality of first detection values and second detection values into the numerical expression of the structure model to perform regression analysis. By this regression analysis, the structure model unit 32 calculates a plurality of types of structure parameters corresponding to one start with respect to the target drive device 7. The structure model unit 32 stores the plurality of types of structure parameters as information of a set of structure parameters. The information of the set of structural parameters includes information of the detection date and time. The detection date and time is the date and time at which a plurality of detection values used in deriving the structural parameter are detected.
The time model unit 33 stores information of a plurality of time models. The plurality of time models correspond to a plurality of types of structural parameters, respectively. The time model is a model that estimates the values of the structural parameters of that kind. For example, the time model is expressed by a numerical expression.
In the time model, the input is a value of the elapsed time as an explanatory variable. The elapsed time is a time from the reference date and time to the detection date and time. The reference date and time is arbitrarily set in advance. In the time model, the output is a certain structural parameter as a target variable.
The time model unit 33 obtains information of a plurality of structural parameters of the same type but different detection dates and times from the structural model unit 32. The time model unit 33 substitutes a plurality of structural parameters of the type and a plurality of detection dates and times into the numerical expression of the time model to perform regression analysis. The time model unit 33 calculates a set of time parameters corresponding to the type of structural parameters in the regression analysis. A set of time parameters includes a plurality of categories of time parameters.
The threshold value generation unit 34 stores information of a plurality of time models identical to the plurality of time models stored in the time model unit 33.
The threshold value generation unit 34 obtains information of a plurality of structural parameters of the same type but different detection dates and times and information of a set of time parameters from the time model unit 33. The threshold value generation unit 34 substitutes the time parameter into the equation of the time model. The threshold value generation unit 34 generates information of the deviation value using a plurality of structural parameters of the same type as the time model but different detection dates and times. The deviation value is a value indicating how much the values of the plurality of structural parameters deviate from the time model.
The threshold value generation unit 34 generates a threshold value of a certain structural parameter using the time model and the deviation value. The threshold value generation unit 34 generates a plurality of threshold values corresponding to a plurality of types of structural parameters, respectively.
The precursor diagnosis unit 35 obtains information of a plurality of structural parameters of the same type but different detection dates and times from the structural model unit 32. The precursor diagnosis unit 35 obtains information of a plurality of thresholds from the threshold generation unit 34. The precursor diagnosis unit 35 stores information of a plurality of thresholds.
The precursor diagnosis unit 35 performs precursor diagnosis on each of a plurality of structural parameters of the same type but different detection dates and times. The precursor diagnosis unit 35 compares the value of the certain structural parameter with a corresponding threshold value. The precursor diagnosis unit 35 compares the structural parameter with a threshold value to determine whether or not the structural parameter is abnormal.
The precursor diagnosis unit 35 determines whether or not the number of structural parameters determined to be abnormal is greater than a predetermined number for a plurality of structural parameters of the same type but having different detection dates and times. When the structural parameter determined to be abnormal is greater than the predetermined number, the warning diagnosis unit 35 diagnoses that a warning of a failure exists in the target drive device 7 related to the structural parameter. When the structural parameter determined to be abnormal is equal to or less than a predetermined number, the warning diagnostic unit 35 diagnoses that there is no warning of a failure in the target drive device 7 related to the structural parameter.
The precursor diagnosis unit 35 performs precursor diagnosis on all kinds of structural parameters calculated by the structural model unit 32.
The precursor diagnosis unit 35 generates diagnosis information including the precursor diagnosis result concerning the target drive device 7. The precursor diagnosis unit 35 causes the display device 21 to display diagnosis information.
Next, an example of the structural model will be described with reference to fig. 3 to 6.
Fig. 3 is a graph showing transition of time difference values of speed command values at each start detected in the drive device to which the fault diagnosis apparatus of embodiment 1 is applied.
In fig. 3, the horizontal axis of the graph is the time elapsed since the start of the rotational movement of the drive device 7. The vertical axis of the graph is a value of the time difference of the speed command value for the drive device 7. The speed command value is a command value for the driving speed of the driving device 7. The time difference value of the speed command value is a value obtained by time-differentiating the speed command value.
Fig. 4 is a graph showing the transition of the speed deviation value at each start detected in the drive device to which the failure diagnosis device of embodiment 1 is applied.
In fig. 4, the horizontal axis of the graph is the time elapsed since the start of the rotational movement of the drive device 7. The vertical axis of the graph is the speed deviation value of the drive device 7. The speed deviation value is the difference between the command value of the driving speed and the actual speed value.
Fig. 5 is a graph showing transition of the correction torque value at each start detected in the drive device to which the failure diagnosis device of embodiment 1 is applied.
In fig. 5, the horizontal axis of the graph is the time elapsed since the start of the rotational movement of the drive device 7. The vertical axis of the graph is the value of the correction torque applied to the drive device 7. The correction torque is a torque applied to the driving device 7 for controlling the operation.
Fig. 6 is a graph showing the transition of the q-axis current value at each start-up detected in the drive device to which the fault diagnosis device of embodiment 1 is applied.
In fig. 6, the horizontal axis of the graph is the time elapsed since the start of the rotational movement of the drive device 7. The vertical axis of the graph is the value of the q-axis current in the driving device 7. The transition of the value of the q-axis current is shown by a solid line.
For example, the structural model is represented by the following formula (1).
[ number 1]
In formula (1), the structural parameter is a, b, c, d. The target variable is the value q (t) of the q-axis current of the driving device 7. The explanatory variables are a time difference value d { sw (t) }/dt of the speed command value, a speed deviation value vd (t), and a correction torque value fa (t).
The structure model unit 32 performs regression analysis by substituting the q-axis current value, the time difference value of the speed command value, the speed deviation value, and the correction torque value in the one-time start shown in fig. 3 to 6 into equation (1). The structure model unit 32 calculates a set of structure parameters a, b, c, d by the regression analysis.
When the structural parameter is substituted into the formula (1) of the structural model, the estimated value of the q-axis current is calculated from the time difference value of the speed command value, the speed deviation value, and the correction torque value. In fig. 6, the transition of the q-axis current estimation value is indicated by a broken line.
Next, an example of the time model will be described with reference to fig. 7.
Fig. 7 is a graph showing an example of a time model stored in the fault diagnosis apparatus according to embodiment 1.
In fig. 7, the horizontal axis of the graph is the elapsed time. The vertical axis of the graph is the value of the structural parameter a. Points plotted in the graph represent the relationship between the value of the structural parameter a and the detection date and time. A plurality of structural parameters a having different detection dates and times are shown in the graph.
For example, regarding the structural parameter a, the time model is represented by the following formula (2).
[ number 2]
a(t′)＝p sin(t′+q)+α(t′)+rβ(t′)+s (2)
In formula (2), the time parameter is p, q, r, s. The target variable is the structural parameter a (t') at a certain elapsed time. The explanatory variable is the elapsed time t'. sin (t' +q) represents the characteristic of the periodic variation of the structural parameter a with respect to the elapsed time. Specifically, sin (t' +q) represents a change in the structural parameter a caused by seasons. In this case, q is a value corresponding to 12 months, for example. α (t') is a function representing the characteristic of monotonically varying structural parameter a with respect to elapsed time. Specifically, α (t') represents a change in the value of a due to aging. For example, α (t ') is a function obtained by linearly combining powers of t', a logarithmic function of t ', an exponential function of t', or a function obtained by combining these functions. Beta (t') is a function representing the effect of the motion condition of each actuation on the structural parameters. Specifically, β (t') represents the influence of the weight on the value of a when the car 9 is started.
The time model unit 33 substitutes a plurality of structural parameters a having different detection dates and times and the detection dates and times into formula (2) to perform regression analysis. The time model unit 33 calculates a set of time parameters p, q, r, s by the regression analysis. The set of time parameters corresponds to the structural parameter a. In fig. 7, a time model into which a set of time parameters is substituted is represented by a curve.
The time model unit 33 generates information on the application period corresponding to a set of time parameters. The application period is a period from the earliest date and time to the latest date and time among a plurality of detection dates and times applied when a set of time parameters is calculated. In fig. 7, the application period is a period from 11 months in 2017 to 10 months in 2018.
Next, an operation example of the threshold value generation unit 34 and the precursor diagnosis unit 35 will be described with reference to fig. 8.
Fig. 8 is a graph showing an example of the threshold value generated by the fault diagnosis apparatus according to embodiment 1.
In fig. 8, the horizontal axis of the graph is the elapsed time t'. The vertical axis of the graph is the value of the structural parameter a. The time model into which a set of time parameters is substituted is represented by a solid curve.
The threshold value generation unit 34 stores information of the time model represented by expression (2) in advance. The threshold value generation unit 34 obtains information of a plurality of configuration parameters a having different detection dates and times and information of a set of time parameters corresponding to the configuration parameters a from the time model unit 33.
The threshold generation unit 34 substitutes a set of time parameters into the time model. The threshold generation unit 34 obtains residuals of each of the plurality of structural parameters a with respect to the time model. The threshold value generation unit 34 uses the calculated residuals to calculate a value of the standard deviation with respect to the residuals as a deviation value σ.
The threshold generation unit 34 calculates an upper threshold function corresponding to the structural parameter a using the deviation value σ and the time model. The upper threshold function is a function obtained by adding the n-fold deviation value σ to the time model. Where n is a positive number. The upper threshold function is a function of the elapsed time t'. In fig. 8, the upper threshold function is represented by a curve a of a broken line.
The threshold generation unit 34 calculates a lower threshold function corresponding to the structural parameter a using the deviation value σ and the time model. The lower threshold function is a function obtained by subtracting the n-fold deviation value σ from the time model. Where n is a positive number. The lower threshold function is a function of the elapsed time t'. In fig. 8, the lower threshold function is represented by a curve B of a broken line.
When the value of the structural parameter a exists between the curve of the upper threshold function and the curve of the lower threshold function, the warning diagnosis unit 35 determines that the structural parameter a is normal. For example, the warning diagnostic unit 35 determines that the structural parameter a indicated by a triangle in fig. 8 is normal. When the value of the structural parameter a does not exist between the upper limit threshold function curve and the lower limit threshold function curve, the warning diagnosis unit 35 determines that the structural parameter a is abnormal. For example, the warning diagnosis unit 35 determines that the structural parameter a indicated by a square in fig. 8 is abnormal.
Next, a precursor diagnosis operation performed by the fault diagnosis device 30 will be described with reference to fig. 9.
Fig. 9 is a flowchart for explaining the precursor diagnosis operation performed by the fault diagnosis apparatus according to embodiment 1.
For example, the fault diagnosis device 30 performs a predictive diagnosis operation for each predetermined period.
In step S001, the data acquisition unit 31 acquires information of a plurality of detection values of the target drive device 7 out of the plurality of drive devices 7 from the data storage device 20.
After that, the operation of step S002 is performed. In step S002, the structure model unit 32 calculates the structure parameters related to the target drive device 7.
Thereafter, the operation of step S003 is performed. In step S003, the time model unit 33 calculates a time parameter corresponding to a certain structural parameter.
After that, the operation of step S004 is performed. In step S004, the threshold value generation unit 34 generates a threshold value.
Thereafter, the operation of step S005 is performed. In step S005, the precursor diagnosis unit 35 obtains information of a plurality of structural parameters of the same type but different detection dates and times from the structural model unit 32. The precursor diagnosis unit 35 obtains information on the threshold value.
Thereafter, the operation of step S006 is performed. In step S006, the precursor diagnosis unit 35 performs precursor diagnosis on a plurality of structural parameters of the same type but different detection dates and times.
Thereafter, the operation of step S007 is performed. In step S007, the precursor diagnosis unit 35 determines whether or not precursor diagnosis is performed on all kinds of structural parameters.
When it is determined in step S007 that there are structural parameters of a type for which no prognosis diagnosis is performed, the operations of step S003 and subsequent operations are performed.
When it is determined in step S007 that the precursor diagnosis is performed on all the types of the structural parameters, the operation of step S008 is performed. In step S008, the precursor diagnosis unit 35 causes the display device 21 to display the diagnosis information.
Thereafter, the failure diagnosis device 30 ends the operation of the precursor diagnosis.
According to embodiment 1 described above, the fault diagnosis device 30 calculates a time parameter corresponding to the target drive device 7. The failure diagnosis device 30 generates a threshold value corresponding to the target drive device 7. The failure diagnosis device 30 diagnoses whether or not the target drive device 7 has a sign of failure. Therefore, in the case of performing the precursor diagnosis, a time model in which conditions such as the installation environment are considered to be different for each driving device 7 can be used. As a result, the accuracy of diagnosing the sign of the failure of the device can be improved.
In addition, the fault diagnosis device 30 stores information of the structural model. Therefore, if it is diagnosed that there is a sign of a failure, the structural parameter diagnosed as having an abnormality is highly likely to be associated with an abnormality of the mechanical or electrical property of the drive device 7. As a result, the cause of the failure warning can be easily estimated.
The fault diagnosis device 30 calculates the structural parameter using a plurality of detection values detected at each start. Therefore, the fault diagnosis apparatus 30 can use a structural model based on actual control. As a result, the accuracy of regression analysis of the calculation structure parameters can be improved.
In addition, the fault diagnosis device 30 stores information of the time model. Therefore, the relationship between the mechanical or electrical characteristics of the driving device 7 and the elapsed time can be derived.
In addition, the fault diagnosis apparatus 30 stores a time model as follows: the time model has terms reflecting the periodically varying characteristics of the structural parameters due to seasonal variations. Therefore, a predictive diagnosis reflecting a seasonal change can be performed. As a result, the accuracy of the prognosis diagnosis can be improved.
In addition, the fault diagnosis apparatus 30 stores a time model as follows: the time model has terms reflecting the characteristics of the structural parameter that change monotonically due to the passage of time. Therefore, a predictive diagnosis reflecting the aging, wear, and the like of the driving device 7 can be performed.
In addition, the fault diagnosis device 30 uses the time parameter to generate a threshold value of the structural parameter. The fault diagnosis device 30 performs a predictive diagnosis using the values of the structural parameters. Therefore, it is possible to perform a predictive diagnosis using the structural parameter based on the actually detected detection value and the threshold value reflecting the temporal change of the structural parameter. As a result, the accuracy of the prognosis diagnosis can be improved as compared with a diagnosis method using only the actually detected value.
Further, the fault diagnosis device 30 calculates the deviation value. The fault diagnosis device 30 generates a threshold function using the deviation value. Therefore, the warning diagnosis can be performed using a threshold value that takes into account the deviation actually generated in the driving device 7.
In addition, a plurality of elevator systems 1 may be provided in the building 2.
The detector 11 may detect a value of a tachometer, a weighing value of the car 9, an air temperature of the machine room 4, and the like. In this case, the fault diagnosis device 30 may perform the warning diagnosis using the value of the tachometer, the weighing value of the car 9, the air temperature of the machine room 4, and the like.
The failure diagnosis device 30 may be applied to an elevator apparatus other than the drive apparatus 7. In this case, the fault diagnosis apparatus 30 stores information of a structural model and a time model corresponding to the apparatus as an object.
The fault diagnosis device 30 may perform a predictive diagnosis at any timing. For example, the maintenance personnel may periodically send an instruction to perform the precursor diagnosis to the fault diagnosis device 30. In this case, the fault diagnosis device 30 performs the warning diagnosis when receiving the instruction to perform the warning diagnosis.
The numerical expression of the structural model may be based on known physical characteristics, electrical characteristics, and the like. For example, in the case of constructing a structural model of a machine that obtains an output signal from a plurality of input signals, the output signal may be set as a target variable. In this case, a plurality of input signals may be set as a plurality of explanatory variables.
The structure model unit 32 may store information of a plurality of structure models. The structural model unit 32 may select a structural model suitable for the operation state of the driving device 7. For example, in the elevator system 1, when an operation mode corresponding to the total weight of the car 9 is selected from a plurality of operation modes, the structure model unit 32 may store information of a plurality of structure models corresponding to the plurality of operation modes. In this case, the structure model unit 32 may acquire information on the selected operation mode. The structure model unit 32 may calculate a set of structure parameters corresponding to the operation mode.
The structure model unit 32 may determine the operation mode by classifying a plurality of detection values. For example, the structure model unit 32 may classify the plurality of detection values using a statistical method such as clustering. The structure model unit 32 may determine the selected operation mode by classifying the plurality of detection values.
The formula of the time model may show a change in the structural parameter with respect to the elapsed time. For example, the expression of the time model may be a expression in which a term such as uT (t') is added to the right of expression (2). Where T (T') denotes the temperature of the machine room 4. u is a time parameter.
In addition, the threshold value generation unit 34 may select the structural parameter to be used when calculating the deviation value σ. For example, the threshold value generation unit 34 may select a configuration parameter having a detection date and time in a period of 3 days or less from a date and time when the target drive device 7 is maintenance-checked.
The threshold value generation unit 34 may generate the threshold value by a general statistical method such as a mahalanobis distance.
The operation of the threshold generating unit 34 may be performed by the time model unit 33. In this case, the time model unit 33 may generate the threshold value by performing the same operation as the threshold value generation unit 34. The precursor diagnosis unit 35 may acquire threshold information from the time model unit 33.
In addition, when the diagnostic information such as the presence of a failure warning is acquired, the display device 21 may report this to a maintenance person located in the information center 100. The display device 21 may transmit the diagnostic information to the corresponding monitoring device 13 when the diagnostic information indicating the presence of the failure is acquired. In this case, the monitoring device 13 may report the driving device 7 diagnosed as having the failure sign to the user.
Next, a modification of embodiment 1 will be described.
Fig. 10 is a graph showing a modification of the threshold value generated by the fault diagnosis apparatus according to embodiment 1.
In fig. 10, the horizontal axis of the graph indicates the elapsed time. The vertical axis of the graph is the value of the structural parameter a. Two broken lines shown in the graph represent examples of the upper threshold function a and the lower threshold function B, respectively.
The precursor diagnosis unit 35 compares the structural parameter a in the prediction period with an upper threshold function and a lower threshold function. The prediction period is a period after the application period. For example, the warning diagnosis unit 35 determines that the structural parameter a indicated by the square in fig. 10 is abnormal. In this case, the warning diagnostic unit 35 diagnoses that the driving device 7 related to the structural parameter a has a warning of failure.
In the modification described above, the fault diagnosis device 30 makes an abnormality determination for the structural parameter in a period other than the application period. In this case, the failure diagnosis device 30 performs the predictive diagnosis with respect to the driving device 7 without performing the calculation of the time parameter. Therefore, the failure diagnosis device 30 can increase the frequency of the precursor diagnosis.
Next, an example of the hardware configuration of the fault diagnosis apparatus 30 will be described with reference to fig. 11.
Fig. 11 is a hardware configuration diagram of the fault diagnosis apparatus according to embodiment 1.
The functions of the fault diagnosis apparatus 30 can be realized by a processing circuit. For example, the processing circuitry is provided with at least one processor 200a and at least one memory 200b. For example, the processing circuitry is provided with at least one dedicated hardware 300.
In the case where the processing circuit includes at least one processor 200a and at least one memory 200b, each function of the fault diagnosis apparatus 30 is implemented by software, firmware, or a combination of software and firmware. At least one of the software and firmware is described as a program. At least one of the software and firmware is stored in at least one memory 200b. The at least one processor 200a realizes the respective functions of the fault diagnosis apparatus 30 by reading out and executing the program stored in the at least one memory 200b. The at least one processor 200a is also referred to as a central processing unit, a processing unit, an arithmetic unit, a microprocessor, a microcomputer, a DSP. For example, the at least one memory 200b is a nonvolatile or volatile semiconductor memory such as RAM, ROM, flash memory, EPROM, EEPROM, etc., a magnetic disk, a floppy disk, an optical disk, a compact disc (compact disc), a mini disc, a DVD, etc.
In the case of processing circuitry having at least one dedicated hardware 300, the processing circuitry is implemented, for example, by a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC, an FPGA, or a combination thereof. For example, each function of the fault diagnosis apparatus 30 is realized by a processing circuit. For example, the functions of the fault diagnosis apparatus 30 are realized by a processing circuit.
With respect to each function of the fault diagnosis apparatus 30, a part may be realized by dedicated hardware 300, and the other part may be realized by software or firmware. For example, the functions of the structure model unit 32 may be realized by a processing circuit which is dedicated hardware 300, and the functions other than the functions of the structure model unit 32 may be realized by at least one processor 200a reading out and executing a program stored in at least one memory 200b.
Thus, the processing circuit implements the functions of the fault diagnosis apparatus 30 by hardware 300, software, firmware, or a combination thereof.
Although not shown, each function of the data collection device 22 is also realized by a processing circuit equivalent to the processing circuit realizing each function of the failure diagnosis device 30. The functions of the data storage device 20 are also realized by processing circuits equivalent to those realizing the functions of the fault diagnosis device 30.
Embodiment 2
Fig. 12 is a block diagram of a fault diagnosis apparatus according to embodiment 2. The same or corresponding parts as those of embodiment 1 are denoted by the same reference numerals. The description of this portion is omitted.
In fig. 12, the threshold generating unit 34 obtains information of a group of time parameters from the time model unit 33.
The threshold value generation unit 34 generates a threshold value for each of a plurality of time parameters included in the group of time parameters. For example, the threshold value generation unit 34 sets a value obtained by adding a predetermined value to the obtained value of the time parameter as a threshold value.
The precursor diagnosis unit 35 obtains information on the threshold value of the time parameter from the threshold value generation unit 34. The precursor diagnosis unit 35 stores information on the threshold value.
The precursor diagnosis unit 35 obtains information on a plurality of time parameters from the time model unit 33. The precursor diagnosis unit 35 compares the plurality of time parameters with a plurality of corresponding thresholds. The precursor diagnosis unit 35 compares the threshold value to determine whether or not each of the plurality of time parameters is abnormal.
When it is determined that the time parameter is not abnormal, the warning diagnostic unit 35 diagnoses that no warning is present in the target drive device 7 related to the time parameter. When it is determined that the time parameter is abnormal, the warning diagnostic unit 35 diagnoses that a warning of a failure exists in the target drive device 7 related to the time parameter.
The precursor diagnosis unit 35 determines whether or not each of the plurality of types of time parameters is abnormal. The precursor diagnosis unit 35 determines whether or not each of a plurality of time parameters corresponding to a plurality of types of structural parameters is abnormal.
According to embodiment 2 described above, the fault diagnosis device 30 generates a threshold value of the time parameter. The fault diagnosis device 30 performs a predictive diagnosis by comparing the value of the time parameter with a threshold value. The value of the temporal parameter reflects the long-term variation of the structural parameter. Therefore, a sign of a failure due to a long-term change can be found.
The threshold value generated by the threshold value generating unit 34 is not limited to the threshold value described in embodiment 2.
Industrial applicability
As described above, the fault diagnosis apparatus of an elevator according to the present invention can be used in an elevator system.
Description of the reference numerals
1: an elevator system; 2: a building; 3: a hoistway; 4: a machine room; 5: a landing; 6: landing door; 7: a driving device; 8: a main rope; 9: a car; 10: a counterweight; 11: a detector; 12: a control device; 13: a monitoring device; 20: a data storage device; 21: a display device; 22: a data collection device; 30: a fault diagnosis device; 31: a data acquisition unit; 32: a structural model section; 33: a time model section; 34: a threshold value generation unit; 35: a precursor diagnosis unit; 50: a diagnostic system; 100: an information center; 200a: a processor; 200b: a memory; 300: hardware.

Claims (10)

1. A fault diagnosis device for an elevator, wherein the fault diagnosis device for an elevator comprises:
a structure model unit that acquires information of a plurality of detection values detected by a detector of the device attached to an elevator for each device, and calculates a structural parameter of the structure model of the device to be subjected to calculation using the plurality of detection values and the structure model;
a time model unit that calculates a time parameter of the time model of the device to be subjected, using a time model corresponding to the structural parameter and the structural parameter calculated by the structural model unit;
a threshold value generation unit that generates a threshold value of the device to be subjected, which corresponds to the time parameter, using the time parameter calculated by the time model unit; and
a warning diagnosis unit that diagnoses whether or not the device to be subjected to warning is defective using any one of the structural parameter calculated by the structural model unit and the time parameter calculated by the time model unit and the threshold value generated by the threshold value generation unit,
the structure model unit calculates the structure parameter of the structure model of the device to be subjected to regression analysis using a plurality of first detection values among the plurality of detection values detected by the detector and a second detection value among the plurality of detection values detected by the detector, with respect to the structure model in which an estimated value of a second detection value is output by inputting the plurality of first detection values.
2. The failure diagnosis apparatus of an elevator according to claim 1, wherein,
the structure model unit calculates the structure parameter of the structure model using the plurality of detection values detected by the structure model and the detector during a period from start to stop of the device.
3. The failure diagnosis apparatus of an elevator according to claim 1, wherein,
the structure model unit uses the time model reflecting at least one of mechanical characteristics and electrical characteristics of the device between the plurality of first detection values and the plurality of second detection values.
4. The failure diagnosis device of an elevator according to any one of claims 1 to 3, wherein,
the structure model section calculates a plurality of the structure parameters using the structure model and the plurality of detection values detected by the detector at different detection dates and times,
the time model unit calculates the time parameters of the time model by performing regression analysis using the plurality of structural parameters calculated by the structural model unit and the plurality of detection dates and times corresponding to the plurality of structural parameters, on a time model in which an estimated value of the structural parameter corresponding to the detection date and time is output by inputting an elapsed time from a reference date and time to the detection date and time.
5. A fault diagnosis device for an elevator, wherein the fault diagnosis device for an elevator comprises:
a structure model unit that acquires information of a plurality of detection values detected by a detector of the device attached to an elevator for each device, and calculates a structural parameter of the structure model of the device to be subjected to calculation using the plurality of detection values and the structure model;
a time model unit that calculates a time parameter of the time model of the device to be subjected, using a time model corresponding to the structural parameter and the structural parameter calculated by the structural model unit;
a threshold value generation unit that generates a threshold value of the device to be subjected, which corresponds to the time parameter, using the time parameter calculated by the time model unit; and
a warning diagnosis unit that diagnoses whether or not the device to be subjected to warning is defective using any one of the structural parameter calculated by the structural model unit and the time parameter calculated by the time model unit and the threshold value generated by the threshold value generation unit,
the structure model section calculates a plurality of the structure parameters using the structure model and the plurality of detection values detected by the detector at different detection dates and times,
the time model unit calculates the time parameters of the time model by performing regression analysis using the plurality of structural parameters calculated by the structural model unit and the plurality of detection dates and times corresponding to the plurality of structural parameters, on a time model in which an estimated value of the structural parameter corresponding to the detection date and time is output by inputting an elapsed time from a reference date and time to the detection date and time.
6. The failure diagnosis apparatus of an elevator according to claim 5, wherein,
the time model section uses the time model reflecting a characteristic that the structural parameter periodically changes due to a seasonal change.
7. The failure diagnosis apparatus of an elevator according to claim 5 or 6, wherein,
the time model section uses the time model reflecting characteristics of the structural parameter that monotonically change due to the passage of time.
8. The failure diagnosis apparatus of an elevator according to claim 1 or 5, wherein,
the structure model section calculates a plurality of the structure parameters using the structure model and the plurality of detection values detected by the detector at different detection dates and times,
the threshold value generation unit obtains information of the plurality of structural parameters generated by the structural model unit and information of the time parameter generated by the time model unit, calculates a deviation value indicating how much the values of the plurality of structural parameters deviate from the time model to which the time parameter is applied, and generates the threshold value of the structural parameter using the time model and the deviation value.
9. A fault diagnosis device for an elevator, wherein the fault diagnosis device for an elevator comprises:
a structure model unit that acquires information of a plurality of detection values detected by a detector of the device attached to an elevator for each device, and calculates a structural parameter of the structure model of the device to be subjected to calculation using the plurality of detection values and the structure model;
a time model unit that calculates a time parameter of the time model of the device to be subjected, using a time model corresponding to the structural parameter and the structural parameter calculated by the structural model unit;
a threshold value generation unit that generates a threshold value of the device to be subjected, which corresponds to the time parameter, using the time parameter calculated by the time model unit; and
a warning diagnosis unit that diagnoses whether or not the device to be subjected to warning is defective using any one of the structural parameter calculated by the structural model unit and the time parameter calculated by the time model unit and the threshold value generated by the threshold value generation unit,
the structure model section calculates a plurality of the structure parameters using the structure model and the plurality of detection values detected by the detector at different detection dates and times,
the threshold value generation unit obtains information of the plurality of structural parameters generated by the structural model unit and information of the time parameter generated by the time model unit, calculates a deviation value indicating how much the values of the plurality of structural parameters deviate from the time model to which the time parameter is applied, and generates the threshold value of the structural parameter using the time model and the deviation value.
10. The failure diagnosis apparatus of an elevator according to claim 9, wherein,
the sign diagnosing unit compares the value of the structural parameter generated by the structural model unit with the threshold value of the structural parameter generated by the threshold value generating unit, thereby diagnosing a sign of a failure of the device to be diagnosed.
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