JP7096964B2 - Elevator remaining life predictor - Google Patents

Description

The present invention relates to an elevator remaining life predictor.

Patent Document 1 discloses a device for predicting the remaining life of a semiconductor device. The remaining life predicting device predicts the remaining life of the semiconductor element.

Japanese Patent Application Laid-Open No. 2002-101668

However, the remaining life predicting device described in Patent Document 1 predicts the remaining life under the same usage conditions as those up to now. Therefore, when the remaining life prediction device is applied to an elevator, the accuracy of predicting the remaining life of the elevator becomes a problem.

The present invention has been made to solve the above-mentioned problems. An object of the present invention is to provide an elevator remaining life predictor capable of improving the accuracy of predicting the remaining life of an elevator.

The remaining life prediction device for an elevator according to the present invention is the above-mentioned for an elevator including an inverter equipped with a semiconductor module having a semiconductor element, a motor driven by the inverter, and a cage driven by the motor. An analysis unit that creates a future operation amount prediction model based on the operation history of the inverter, a measurement unit that measures the temperature of the semiconductor element, and an operation amount prediction model and the measurement unit that are measured by the analysis unit. The analysis unit includes a prediction unit that predicts the remaining life of the semiconductor module based on the temperature, and the analysis unit is at least one of seasonal fluctuation, trend fluctuation, periodic fluctuation, and irregular fluctuation. Create a future driving amount prediction model that takes this into consideration .

According to the present invention, the remaining life prediction device predicts the remaining life of the semiconductor module based on the operation amount prediction model and the measured value of the temperature of the semiconductor element. Therefore, the accuracy of predicting the remaining life of the elevator can be improved.

FIG. 5 is an overall configuration diagram of an elevator to which the elevator remaining life prediction device according to the first embodiment is applied. FIG. 5 is a side view of a main part of an elevator inverter to which the elevator remaining life prediction device according to the first embodiment is applied. It is a block diagram which shows the functional structure of the determination device as the remaining life prediction device of an elevator in Embodiment 1. FIG. It is a figure which shows the life curve calculated by the determination device as the remaining life prediction apparatus of an elevator in Embodiment 1. FIG. FIG. 5 is a hardware configuration diagram of an elevator control device to which the elevator remaining life prediction device according to the first embodiment is applied. FIG. 3 is an overall configuration diagram of an elevator to which the elevator remaining life prediction device according to the second embodiment is applied.

A mode for carrying out the present invention will be described with reference to the accompanying drawings. In each figure, the same or corresponding parts are designated by the same reference numerals. The duplicate description of the relevant part will be simplified or omitted as appropriate.

Embodiment 1.
FIG. 1 is an overall configuration diagram of an elevator to which the remaining life prediction device of the elevator according to the first embodiment is applied.

In FIG. 1, the power supply 1 is provided in a building. For example, the power source 1 is a commercial power source. Elevators are installed in buildings.

In the elevator, the hoisting machine 2 is provided in a machine room (not shown) of the elevator. The hoisting machine 2 includes a motor 3 and a sheave 4. The motor 3 is provided so that it can be rotationally driven when a voltage is applied. The sheave 4 is attached to the rotating shaft of the motor 3.

The rope 5 is wound around the sheave 4. The basket 6 is supported on one side of the rope 5. The counterweight 7 is supported on the other side of the rope 5.

The control device 8 is provided in the machine room. The control device 8 includes a converter 9, a capacitor 10, an inverter 11, a controller 12, a determination device 13, and a notification device 14.

The converter 9 converts the AC voltage from the power supply 1 into a DC voltage. The capacitor 10 smoothes the ripple of the DC voltage converted by the converter 9. The inverter 11 converts the DC voltage whose ripple is smoothed by the capacitor 10 into an AC voltage.

The motor 3 is rotationally driven by the voltage applied from the inverter 11. The sheave 4 rotates following the rotational drive of the motor 3. The rope 5 moves following the rotation of the sheave 4. The car 6 and the counterweight 7 move up and down in opposite directions following the movement of the rope 5.

The controller 12 controls the operation of the elevator as a whole. For example, the controller 12 controls the operation of the inverter 11. The determination device 13 predicts the remaining life of the semiconductor module provided in the inverter 11 as the remaining life predicting device. The alarm 14 notifies the information according to the prediction result of the determination device 13.

Next, the main part of the inverter 11 will be described with reference to FIG.
FIG. 2 is a side view of a main part of an elevator inverter to which the elevator remaining life prediction device according to the first embodiment is applied.

As shown in FIG. 2, the semiconductor module 15 is mounted on the inverter 11. The semiconductor module 15 includes a metal base 16, an insulating substrate 17, a semiconductor element 18, and a heat sink 19.

The metal base 16 serves as a base for the semiconductor module 15. The lower surface of the insulating substrate 17 is joined to the upper surface of the metal base 16 via the solder 20a. The lower surface of the semiconductor element 18 is joined to the semiconductor element 18 via the solder 20b. For example, the heat sink 19 is a water-cooled heat sink. For example, the heat sink 19 is an air-cooled heat sink. The heat sink 19 includes a plurality of heat dissipation fins. The upper surface of the heat sink 19 is joined to the lower surface of the metal base 16.

The metal pattern 21a is formed between the insulating substrate 17 and the solder 20a. The metal pattern 21b is formed between the insulating substrate 17 and the solder 20b.

The semiconductor module 15 generates heat when it operates. The heat is dissipated through the heat sink 19. The heat sink 19 may be either water-cooled or air-cooled.

Next, the determination device 13 will be described with reference to FIG.
FIG. 3 is a block diagram showing a functional configuration of a determination device as an elevator remaining life prediction device according to the first embodiment.

As shown in FIG. 3, the determination device 13 includes a measurement unit 22, a storage unit 23, an analysis unit 24, and a prediction unit 25.

The measuring unit 22 measures the temperature of the surface of the semiconductor element 18 in which the elevator is operating. For example, the measuring unit 22 measures the temperature of the surface of the semiconductor element 18 in a state of being connected from the outside during maintenance of the elevator. For example, the measuring unit 22 is attached to the surface of the semiconductor element 18 in advance and constantly measures the temperature of the surface of the semiconductor element 18 during the operation of the elevator.

The storage unit 23 stores information on the life characteristics and the life threshold of the semiconductor module 15 in advance. The life characteristic shows the relationship between the degree of deterioration of the semiconductor module 15 and the temperature. For example, the life characteristic numerically represents the relationship between the ratio (porosity) of the solder 20b between the semiconductor element 18 and the metal pattern 21b and the temperature. For example, the life characteristic numerically represents the relationship between the ratio (porosity) of the solder 20b between the semiconductor element 18 and the metal pattern 21b and the temperature. The life threshold is a standard for considering a failure when the porosity is more than what percentage. When the temperature of the semiconductor element 18 becomes higher than what temperature, it can be determined whether or not the failure occurs.

The storage unit 23 stores information on the actually measured value measured by the measurement unit 22. The representative value of the temperature of the semiconductor element 18 is any one of the median value of the temperature of the semiconductor element 18, the average value of the temperature of the semiconductor element 18, and the maximum value of the temperature of the semiconductor element 18.

The analysis unit 24 performs time-series analysis on the operation history information acquired from the outside that manages the elevator operation history of a building maintenance company or the like, and creates a future operation amount prediction model. The operating amount of the elevator corresponds to the operating time of the elevator. When the operating mode of the elevator is always periodic, the number of times the elevator is operated may be defined as the operating amount.

For time-series analysis, ARIMA model (Outo Regressive Integrated Moving Average analysis), SARIMA model (Seasonal Auto Regressive Integrated Integrated Analysis, etc.), GARCH model (Generigate), etc.

The ARIMA model is a composite model of an AR model (Auto Regressive model), an MA model (Moving Average model), and an I model (Integrated model). The AR model is a model represented by the weighted sum of time series data. The MA model is a model represented by the weighted sum of past residuals. The I model is a sum model that takes the difference in time series data.

The SARIMA model is an extension of the ARIMA model. SARIMA is a model that takes into account seasonal fluctuations.

The ARIMA model and the SARIMA model are models predicted on the assumption that the variance is constant white noise. The GARCH model is a model predicted in consideration of the non-uniformity of dispersion.

It should be noted that a plurality of driving amount prediction models may be created by using a plurality of time series analysis methods.

Further, when there is a deficiency in the operation history information, the deficiency may be complemented by the least squares method or may be complemented by using another operation history.

The prediction unit 25 predicts the remaining life of the semiconductor module 15 based on the operation amount prediction model and the life characteristics of the semiconductor module 15.

When a plurality of operation amount prediction models are created, the operation amount prediction model expected to have high goodness of fit may be selected and the predicted value of the remaining life may be calculated. At this time, the goodness of fit between the goodness of fit of the operation amount prediction model and the time series data of the past operation amount may be calculated by a chi-square test or the like, and the operation amount prediction model expected to have a high goodness of fit may be selected.

The information on the remaining life calculated by the prediction unit 25 is notified to the outside that manages the operation information of the elevator such as a building maintenance company by the alarm 14.

For example, the alarm 14 notifies the predicted failure time of the semiconductor module 15. For example, the alarm 14 notifies whether or not the measured value of the temperature of the semiconductor element 18 measured by the measuring unit 22 has detected an abnormal value. For example, when the actual operating amount is significantly different from the operating amount predicted value calculated by the prediction unit 25, the alarm 14 notifies the elevator management company to that effect.

When the measured value of the temperature of the semiconductor element 18 measured by the measuring unit 22 exceeds the threshold value or the measured value detects an abnormal value, the predicting unit 25 transmits the abnormality detection information to the controller 12.

The controller 12 forcibly stops the operation of the elevator when the abnormality detection signal is received.

Information on the measured value of the temperature of the upper surface of the semiconductor element 18 may always be transmitted to the controller 12. In this case, in the controller 12, the measured value may be used for controlling the inverter 11.

Next, an example of a method for predicting the remaining life of the semiconductor module 15 will be described with reference to FIG.
FIG. 4 is a diagram showing a life curve calculated by a determination device as an elevator remaining life prediction device according to the first embodiment.

As shown in FIG. 4, the prediction unit 25 calculates a life curve A representing the relationship between the life and time of the semiconductor module 15. Specifically, the prediction unit 25 calculates the life curve A based on the predicted value of the operating amount for each month, the measured value of the temperature of the semiconductor element 18, and the life characteristic.

For example, the prediction unit 25 is based on the difference between the measured values of the temperature of the semiconductor element 18 measured by the measuring unit 22 before and after the use of the elevator, the measured values of the operating amount so far, and the predicted values of the operating amount in the future. Therefore, the temperature change of the semiconductor element 18 in the future is predicted. The prediction unit 25 predicts a change in the porosity of the solder 20b in the future based on the predicted value of the temperature change and the life characteristic. The prediction unit 25 calculates the life curve A of the semiconductor module 15 based on the change in the porosity.

For example, the prediction unit 25 refers to the life threshold value from the information stored in the storage unit 23, and predicts the period until the life curve A falls below the life threshold value as the remaining life of the semiconductor module 15.

According to the first embodiment described above, the determination device 13 predicts the remaining life of the semiconductor module 15 based on the operation amount prediction model and the measured value of the temperature of the semiconductor element 18. Therefore, the accuracy of predicting the remaining life of the elevator can be improved. As a result, the inverter 11 on which the semiconductor module 15 is mounted can be repaired or replaced at the time of maintenance before the life of the elevator is reached.

Specifically, the determination device 13 performs time-series analysis on the operation history of the elevator to create a future operation amount prediction model. Therefore, it is possible to predict the future operation amount and improve the prediction accuracy of the remaining life of the elevator.

In the life curve A, the life consumption for each period can be predicted by using the operation amount for each preset period estimated by the operation amount prediction model. For example, the monthly life consumption can be predicted by using the monthly operation amount. Therefore, it is possible to predict the failure time of the semiconductor module with high accuracy.

In addition, if necessary, the operating amount is measured using an analysis method that focuses on changes due to various factors such as seasonal fluctuation elements, trendy fluctuation elements, periodic fluctuation elements, and irregular factors included in each operation amount prediction model. It may be on the preliminary side. The operating amount may be predicted by using other analytical methods such as primary exponential smoothing, secondary exponential smoothing, and tertiary exponential smoothing.

When seasonal fluctuation factors, trend fluctuation factors, periodic fluctuation factors, irregular factors, etc. are taken into consideration, the remaining life of the elevator can be predicted with higher accuracy.

Seasonal fluctuation factors are factors related to elevator utilization, such as temperature, weather, calendar holidays, Obon, and customary events such as the New Year. At this time, when predicting future driving volume, enter the temperature and long-range weather forecast announced by the Meteorological Agency, and enter information such as future holidays, Obon, New Year, and other days when people come and go differently than usual. By inputting, it is possible to improve the prediction accuracy of the operating amount.

Trendy variability factors are those associated with long-term, sustained change. Specifically, the trend variable element is an element that indicates whether it is an upward trend or a downward trend in the long term. For example, in an elevator in a newly built building, there are many users at first, but as the building ages, the number of users tends to gradually decrease. For example, if the surrounding population is gradually increasing, the number of users will also be gradually increasing. At this time, when predicting the future driving amount, the accuracy of predicting the driving amount can be improved by inputting information such as the age of the building and the increase / decrease in the surrounding population.

The periodic fluctuation element is an up-and-down fluctuation that is repeated periodically, and is an element related to economic fluctuations in economic activities. For example, in an elevator installed in a building or the like where the number of visitors increases when the economy is improving, the amount of operation increases. At this time, it is possible to improve the prediction accuracy of the driving amount by inputting the information of the future economic forecast at the time of predicting the future driving amount.

Irregular variable elements are variable elements other than seasonal variable elements, trendy variable elements, and periodic variable elements. For example, as irregular fluctuation factors, there are factors such as changes in the surrounding environment where elevators are installed, natural disasters, and the like. For example, there are irregular fluctuations such as a rapid population increase when stations and condominiums are built in the vicinity, an increase in visitors due to information being posted on TV, magazines, the Internet, etc., and a decrease in visitors due to natural disasters, etc. Become an element. At this time, by inputting the information of these irregularly occurring events, the accuracy of predicting the operating amount can be improved.

If necessary, measured values such as thermal resistance, temperature change, and voltage of the semiconductor element 18 may be measured. In this case as well, the progress of deterioration of the semiconductor module 15 can be observed. Therefore, the same effect can be obtained. At this time, as the life characteristic, the life characteristic corresponding to the thermal resistance value, the temperature change, the voltage, etc. may be used.

If the remaining life of the elevator is predicted with high accuracy, it is possible to prevent the elevator from failing. In addition, by being able to correctly evaluate the time when the elevator fails, it is possible to determine in advance when the elevator should be stopped and repaired. As a result, it is possible to inform the elevator user of the time of repair.

The analysis unit 24 may perform machine learning on the past operation history acquired from the outside, autonomously search for the rules and rules of the operation history, and create a future operation amount prediction model. In this case as well, the accuracy of predicting the remaining life of the elevator can be improved.

Next, an example of the control device will be described with reference to FIG.
FIG. 5 is a hardware configuration diagram of an elevator control device to which the elevator remaining life prediction device according to the first embodiment is applied.

Each function of the control device 8 can be realized by a processing circuit. For example, the processing circuit comprises at least one processor 100a and at least one memory 100b. For example, the processing circuit comprises at least one dedicated hardware 200.

When the processing circuit includes at least one processor 100a and at least one memory 100b, each function of the control device 8 is realized by software, firmware, or a combination of software and firmware. At least one of the software and firmware is written as a program. At least one of the software and firmware is stored in at least one memory 100b. At least one processor 100a realizes each function of the control device 8 by reading and executing a program stored in at least one memory 100b. At least one processor 100a is also referred to as a central processing unit, a processing unit, an arithmetic unit, a microprocessor, a microcomputer, and a DSP. For example, at least one memory 100b is a non-volatile or volatile semiconductor memory such as RAM, ROM, flash memory, EPROM, EEPROM, a magnetic disk, a flexible disk, an optical disk, a compact disk, a mini disk, a DVD, or the like.

If the processing circuit comprises at least one dedicated hardware 200, the processing circuit may be implemented, for example, as a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC, an FPGA, or a combination thereof. To. For example, each function of the control device is realized by a processing circuit. For example, each function of the control device 8 is collectively realized by a processing circuit.

For each function of the control device 8, a part may be realized by the dedicated hardware 200, and the other part may be realized by software or firmware. For example, the function of the controller 12 is realized by a processing circuit as dedicated hardware 200, and for the functions other than the function of the controller 12, at least one processor 100a reads a program stored in at least one memory 100b. It may be realized by executing the above.

In this way, the processing circuit realizes each function of the control device 8 by hardware 200, software, firmware, or a combination thereof.

Embodiment 2.
FIG. 6 is an overall configuration diagram of an elevator to which the remaining life prediction device of the elevator according to the second embodiment is applied. The same or corresponding parts as those of the first embodiment are designated by the same reference numerals. The explanation of this part is omitted.

In the second embodiment, the elevator includes a timing controller 26. The timing controller 26 is connected between the controller 12 and the inverter 11. The timing controller 26 controls the timing of the measurement start and the measurement end of the measurement unit 22 (not shown in FIG. 6) connected to the inverter 11 as the timing control unit of the life prediction device.

The timing controller 26 determines whether or not the measuring unit 22 has performed the measurement by the time when the preset period elapses. If the measuring unit 22 has not performed the measurement by the elapse of the preset period, the timing controller 26 determines whether or not the elevator has operated by the elapse of the preset time. If the elevator is not operating by the time the preset time elapses, the timing controller 26 determines whether or not the car 6 is loaded. When the car 6 is not loaded, the timing controller 26 operates the elevator for a preset time via the controller 12. After that, when a preset time elapses, the measuring unit 22 measures the temperature of the surface of the semiconductor element 18.

According to the second embodiment described above, the determination device 13 controls the measurement timing of the measurement unit 22. At this time, the conditions for measuring the temperature of the surface of the semiconductor element 18 can be made uniform. Therefore, it is possible to reduce the measurement error of the temperature of the surface of the semiconductor element 18. As a result, the prediction accuracy of the life curve A can be improved.

Further, the temperature of the surface of the semiconductor element 18 is periodically and automatically acquired. Therefore, it is possible to save time and effort when measuring the temperature of the surface of the semiconductor element 18.

If the measurement unit 22 has not performed the measurement by the time the period elapses, the temperature of the semiconductor module 15 mounted on the inverter 11 is equivalent to the environmental temperature, and the car 6 is not loaded, it is set in advance. The elevator may be operated only for the specified period. After that, the temperature of the surface of the semiconductor element 18 may be measured when a preset time elapses. In this case as well, the prediction accuracy of the life curve A can be improved.

Further, the elevator may be operated when the elevator is not operating by the lapse of a preset time or when the temperature of the semiconductor module 15 reaches the preset temperature. In this case as well, the prediction accuracy of the life curve A can be improved.

In the first embodiment or the second embodiment, the function of the determination device 13 may be provided in a device different from the control device 8. In this case as well, the accuracy of predicting the remaining life of the elevator can be improved.

As described above, the elevator remaining life prediction device according to the present invention can be used in an elevator system.

1 power supply, 2 hoist, 3 motor, 4 rope wheel, 5 rope, 6 basket, 7 counter weight, 8 controller, 9 converter, 10 capacitor, 11 inverter, 12 controller, 13 judge, 14 alarm, 15 Semiconductor module, 16 Metal base, 17 Insulated substrate, 18 Semiconductor element, 19 Heat sink, 20a solder, 20b solder, 21a metal pattern, 21b metal pattern, 22 Measuring unit, 23 Storage unit, 24 Analysis unit, 25 Prediction unit, 26 Timing controller, 100a processor, 100b memory, 200 hardware

Claims (5)

An inverter equipped with a semiconductor module having a semiconductor element,
The motor driven by the inverter and
The basket driven by the motor,
For elevators equipped with
An analysis unit that creates a future driving amount prediction model based on the elevator driving history,
A measuring unit that measures the temperature of the semiconductor element,
A prediction unit that predicts the remaining life of the semiconductor module based on the operation amount prediction model created by the analysis unit and the temperature measured by the measurement unit.
Equipped with
The analysis unit is an elevator remaining life prediction device that creates a future operation amount prediction model that considers at least one of seasonal fluctuation, trend fluctuation, periodic fluctuation, and irregular fluctuation . The remaining life prediction device for an elevator according to claim 1, wherein the analysis unit performs time-series analysis on the operation history of the elevator to create a future operation amount prediction model. The remaining life prediction device for an elevator according to claim 1, wherein the analysis unit performs machine learning on the operation history of the elevator to create a future operation amount prediction model. A timing control unit that operates the elevator when the elevator is not operating by the lapse of a preset time.
Equipped with
The remaining life prediction device for an elevator according to any one of claims 1 to 3 , wherein the measuring unit measures the temperature of the semiconductor element after the timing control unit operates the elevator. A timing control unit that operates the elevator when the temperature of the semiconductor module reaches a preset temperature.
Equipped with
The remaining life prediction device for an elevator according to any one of claims 1 to 3 , wherein the measuring unit measures the temperature of the semiconductor element after the timing control unit operates the elevator.

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