WO2019187138A1 - Dispositif de prédiction de durée de vie restante et machine-outil - Google Patents

Dispositif de prédiction de durée de vie restante et machine-outil Download PDF

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Publication number
WO2019187138A1
WO2019187138A1 PCT/JP2018/013941 JP2018013941W WO2019187138A1 WO 2019187138 A1 WO2019187138 A1 WO 2019187138A1 JP 2018013941 W JP2018013941 W JP 2018013941W WO 2019187138 A1 WO2019187138 A1 WO 2019187138A1
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Prior art keywords
inspection
remaining life
time
inspection data
data
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PCT/JP2018/013941
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English (en)
Japanese (ja)
Inventor
友英 那須
左京 松下
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株式会社牧野フライス製作所
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Priority to PCT/JP2018/013941 priority Critical patent/WO2019187138A1/fr
Publication of WO2019187138A1 publication Critical patent/WO2019187138A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M99/00—Subject matter not provided for in other groups of this subclass

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  • the present application relates to a remaining life prediction apparatus and a machine tool including a remaining life prediction unit.
  • Patent Document 1 discloses a rolling bearing failure diagnosis device that outputs a failure state of a rolling bearing.
  • This apparatus includes a neural network that receives a Fourier spectrum based on the detected vibration data of the rolling bearing as an input, and outputs whether or not the rolling bearing has failed and the state of the failure.
  • an object of the present invention is to provide a remaining life prediction apparatus and a machine tool that can indicate the remaining life of an apparatus.
  • One aspect of the present disclosure inputs inspection data obtained from a predetermined inspection operation in an apparatus and an inspection time that is a use time of the apparatus at the time when the inspection operation is performed in the apparatus for predicting the remaining life of the apparatus.
  • the apparatus is configured to calculate a predicted remaining life of the device using the machine learning unit configured to perform the inspection, the inspection data input in the first input unit, and the algorithm obtained from the machine learning.
  • the remaining life prediction unit and the remaining life Comprising an output unit for outputting the predicted residual life obtained by the life predicting portion, a residual life predicting device of the apparatus.
  • the device remaining life prediction apparatus In the device remaining life prediction apparatus according to an aspect of the present disclosure, machine learning is performed in which inspection data obtained from a predetermined inspection operation is input and the remaining life at each inspection time is output. And the estimated remaining lifetime of an apparatus is calculated using the algorithm obtained from machine learning, and the newly input test
  • Machine learning includes (i) a convolutional neural network that uses the result of wavelet transform from inspection data, (ii) a neural network based on long- and short-term memory that uses feature values preprocessed from inspection data, and (iii) inspection data May be included in at least one of neural networks based on long-term and short-term memory.
  • the machine learning may include a convolutional neural network and a neural network based on long- and short-term memory using preprocessed feature values. In this case, highly accurate prediction based on machine learning including two methods can be realized.
  • the preprocessed feature amount includes the peak amplitude obtained by fast Fourier transform of the envelope-processed inspection data, the peak amplitude obtained by direct fast Fourier transform of the inspection data, and the inspection data
  • the mean square value and kurtosis obtained from the inspection data may be included. In this case, highly accurate prediction based on machine learning including the plurality of feature amounts can be realized.
  • the inspection operation may be included in the warm-up operation. In this case, it is not necessary to operate the device only for the inspection operation.
  • the usage time is the length of time from the time when the use of the device is started to the time when the use time is confirmed, or the time when the use of the device is started and the use time is confirmed. Two pieces of time data may be used.
  • another aspect of the present disclosure is a sensor for obtaining inspection data related to equipment included in a machine tool in a machine tool, wherein the inspection data is obtained from a predetermined inspection operation in the equipment, and the sensor
  • a remaining life prediction unit configured to calculate a predicted remaining life of a device using inspection data measured at a predetermined frequency and sampled at a predetermined frequency and an algorithm obtained from machine learning, and a remaining life prediction unit
  • a display device for displaying the predicted remaining life obtained in step 4
  • the algorithm is determined by performing machine learning with inspection data as input and remaining life as output for multiple devices that have become unusable
  • the remaining lifetime is based on the lifetime that is the usage time of the device when the device becomes unusable and the inspection time that is the usage time of the device when the inspection operation is performed. Te is calculated for each of the past inspection time, a machine tool.
  • Such a machine tool can also indicate the remaining life of the device in the same manner as the remaining life prediction apparatus.
  • An example of inspection data is shown.
  • inspection data is shown.
  • inspection data is shown.
  • inspection data is shown.
  • An example of the image wavelet-transformed from vibration data is shown.
  • FIG. 1 is a schematic block diagram showing a system including a remaining life prediction apparatus according to an embodiment.
  • the remaining lifetime of the equipment constituting the machine tool 10 is predicted.
  • the system 100 includes a machine tool 10 and a remaining life prediction device 50.
  • the machine tool 10 can be, for example, various NC (Numerical Control) machine tools such as a machining center.
  • NC Genetic Control
  • the machine tool 10 includes, for example, a main shaft 11, a front bearing 12 and a rear bearing 13, a bearing case 14, and a sensor 15.
  • the machine tool 10 may include other components.
  • the spindle 11 grips the tool T or the workpiece according to the type or application of the machine tool.
  • the bearings 12 and 13 rotatably support the main shaft 11.
  • the bearings 12 and 13 can be various bearings such as a rolling bearing such as a ball bearing or a roller bearing.
  • the inner rings of the bearings 12 and 13 can be fixed to the main shaft 11, and the outer rings of the bearings 12 and 13 can be fixed to the bearing case 14.
  • the bearing case 14 accommodates the bearings 12 and 13.
  • the sensor 15 is used to obtain inspection data relating to a device (in this embodiment, the main spindle 11) that is a target of calculation of the predicted remaining life.
  • a device in this embodiment, the main spindle 11
  • Such equipment may be, for example, a component of a machine tool used to move or transport a tool T or a workpiece (not shown). From another point of view, the equipment may be a moving element of a machine tool that includes two or more parts that are in rolling contact or sliding contact.
  • the sensor 15 can be used to obtain inspection data regarding the bearings 12, 13 that support the main shaft 11.
  • the inspection data obtained from the measurement value detected by the sensor 15 is used to calculate the predicted remaining life.
  • Such inspection data can be, for example, data representing a relationship between time and a physical quantity that varies according to the operation of the device.
  • the inspection data can change over time depending on various factors, such as equipment damage and / or equipment usage time.
  • the sensor 15 can be an acceleration sensor, and in this case, the inspection data is vibration data representing a relationship between time and acceleration.
  • the sensor 15 may be a displacement sensor, and in this case, the inspection data is vibration data representing a relationship between time and displacement.
  • the sensor 15 can be attached, for example, in the vicinity of the main shaft 11. In the present embodiment, the sensor 15 is attached to the outer surface of the bearing case 14 (for example, screwed). The sensor 15 may be attached to another component in the vicinity of the main shaft 11.
  • the inspection data may be data obtained from a servo motor (not shown) for rotating the spindle 11 instead of the vibration data as described above.
  • the sensor 15 may be incorporated in a servo motor, for example.
  • the sensor 15 may be used to obtain inspection data relating to a feed mechanism (not shown) for moving a workpiece or a tool, instead of the spindle 11.
  • the device for which the expected remaining life is calculated is, for example, a ball screw or a linear motion rolling guide included in a linear motion feed mechanism (for example, a table or a saddle), or a bearing included in a rotary table. Also good.
  • the sensor 15 may be used to obtain inspection data relating to an automatic tool changer (ATC) for moving a tool or an automatic pallet changer (APC) for moving a workpiece via a pallet.
  • ATC automatic tool changer
  • APC automatic pallet changer
  • a device such as a bearing included in ATC or APC can be a target for calculating the predicted remaining life.
  • the sensor 15 can be mounted, for example, in the vicinity of the feed mechanism, ATC or APC, or can be incorporated in a servo motor for driving the feed mechanism, ATC or APC.
  • FIG. 2 shows an example of inspection data, and shows an example of vibration data obtained by the sensor 15 when the sensor 15 is an acceleration sensor. As shown in FIG. 2, the vibration data represents the relationship between time and acceleration.
  • FIGS. 3 to 6 show other examples of inspection data, showing examples of inspection data obtained by a servo motor.
  • the inspection data may be data representing a relationship between time and the position of the device, as shown in FIG.
  • the inspection data may be data representing a relationship between time and a position error representing a deviation from a desired position.
  • the inspection data may be data representing a relationship between time and a torque command value of the servo motor, as shown in FIG.
  • the inspection data may be data representing a relationship between time and a reading value of an encoder of a servo motor.
  • Inspection data is obtained from a predetermined inspection operation on the equipment.
  • the inspection operation can be performed, for example, once every several hours, once every day to every few days, once every week to every few weeks, or once every month to every few months.
  • the spindle 11, the feed mechanism, the ATC, or the APC or the like to which the predetermined tool T or the workpiece is attached or not attached is processed at a predetermined rotational speed without machining the workpiece. It can be operated at a predetermined speed and / or a predetermined distance.
  • the inspection operation may be performed for a relatively short time, for example, may be performed for 1 second to several tens of seconds or 1 minute to several minutes, or may be performed for a time shorter or longer than these times.
  • the inspection operation can be, for example, a warm-up operation of the device or can be part of a warm-up operation.
  • the machine tool 10 further includes a control device 20 and a data collection device 30.
  • the control device 20 may be, for example, an NC device for controlling the spindle 11 and the feed mechanism of the machine tool 10 and / or other equipment (for example, ATC and / or APC) of the machine tool 10. It may be a machine control device for controlling.
  • the control device 20 includes a display unit 21 such as a touch panel or a liquid crystal display.
  • the control device 20 includes a processor such as a CPU (Central Processing Unit), a memory such as a hard disk drive, a ROM (read only memory), a RAM (random access memory), and / or a mouse and / or Other components such as an input device such as a keyboard can be included.
  • a CPU Central Processing Unit
  • a memory such as a hard disk drive
  • ROM read only memory
  • RAM random access memory
  • Other components such as an input device such as a keyboard can be included.
  • the data collection device 30 is connected to the sensor 15 in a wired or wireless manner, and receives a measurement value detected by the sensor 15. Further, the data collection device 30 is connected to the control device 20 so as to be communicable by wire or wirelessly.
  • the data collection device 30 has a storage unit 31.
  • the storage unit 31 can be, for example, a hard disk drive.
  • the storage unit 31 stores a plurality of sets of inspection data based on the measurement value received from the sensor 15 and inspection time when the corresponding inspection operation is performed.
  • the measurement value detected by the sensor 15 is sampled at a predetermined frequency (for example, 18 kHz to 120 kHz) and stored in the storage unit 31 as inspection data.
  • “inspection time” may mean the usage time of the device at the time when the inspection operation is performed. Further, in the present disclosure, the “use time” may mean the total time that the device actually operates from the time when the device is installed or from a predetermined time after the device is installed. . Alternatively, for example, the “use time” may be the length of time from the time when use of the device is started to the time when the use time is confirmed. Furthermore, two time data of the time when the use of the device is started and the time when the use time is confirmed are stored as the use time, and in the following machine learning and calculation of the predicted remaining life, etc. The remaining time may be calculated as the usage time in the remaining life prediction apparatus 50.
  • the data collection device 30 and / or the control device 20 may include a counter for counting the usage time of the device.
  • the storage unit 31 may further store the “life” of the device (details will be described later).
  • the “life” may be input to the data collection device 30 by the user of the machine tool 10 when the device becomes unusable, for example.
  • the data collection device 30 may calculate the “remaining life” (details will be described later) of the device at each past inspection time based on the “inspection time” and the “lifetime”. The recorded “remaining life” may be stored.
  • the data collection device 30 may be provided separately from the control device 20 or may be incorporated in the control device 20. When the data collection device 30 is incorporated in the control device 20, each component of the data collection device 30 may be realized by a corresponding component in the control device 20, or what is a component in the control device 20? It may be provided separately.
  • the data collection device 30 may include other components such as a processor such as a CPU, a ROM, a RAM, and / or an input / output device such as a touch panel, a mouse, and / or a keyboard. .
  • the remaining life prediction device 50 can communicate with the control device 20 and the data collection device 30 via a network.
  • the remaining life prediction apparatus 50 can be provided on the manufacturer side of the machine tool 10, for example, and can be, for example, a computer, a server, and / or a mobile terminal such as a smartphone and / or a tablet.
  • the network is, for example, a communication system based on a communication standard defined by 3GPP (Third Generation Partnership Project) such as the Internet, WiFi (registered trademark), Bluetooth (registered trademark), 3G and 4G, or other communication methods, or Any combination of these can be used, wired or wireless, or a combination thereof.
  • 3GPP Third Generation Partnership Project
  • the remaining life prediction apparatus 50 can include a first input unit 51, a second input unit 52, a storage unit 53, a processor 54, and an output unit 55. These elements are connected via a bus or the like. Are connected to each other.
  • the remaining life prediction apparatus 50 may further include other components.
  • the first input unit 51 receives input of inspection data and inspection time.
  • the first input unit 51 can be, for example, an I / O port.
  • the first input unit 51 can receive the inspection data and the inspection time transmitted from the data collection device 30.
  • the first input unit 51 may be configured to read inspection data and inspection time stored in a storage medium.
  • the inspection data and the inspection time input from the first input unit 51 are transmitted to the storage unit 53.
  • the storage device 53 is used to indicate which device of the machine tool 10 the inspection data and the inspection time input from the first input unit 51 are related to. Identification information indicating which region of the inspection data and inspection time should be stored may be input to the first input unit 51.
  • Identification information indicating whether inspection data and inspection time should be stored in the area may be input to the first input unit.
  • the second input unit 52 receives an input of the life of the device when the device becomes unusable (for example, when it fails or is damaged).
  • the “lifetime” may mean the usage time of the device when the device becomes unusable.
  • the 2nd input part 52 can receive the input of failure mode, when an apparatus becomes unusable.
  • the “failure mode” may mean a failure type indicating how the device has become unusable. For example, if the device is a bearing 12 or 13 of the spindle 11, the failure modes can include outer ring breakage, inner ring breakage, and cage breakage. Failure modes may also include, for example, bearing indentation, poor lubrication and / or preload loss, or ball screw wear, indentation, misalignment and / or poor lubrication.
  • the second input unit 52 can be, for example, an input device such as a mouse, a keyboard, and / or a touch panel.
  • the lifetime and failure mode can be input by the second input unit 52.
  • the second input unit 52 can be, for example, an I / O port or the like.
  • the lifetime and failure mode of the device transmitted from the data collection device 30 can be received by the second input unit 52.
  • the second input unit 52 may be realized by an I / O port for the first input unit 51, or may be provided separately from the I / O port for the first input unit 51.
  • the lifetime and failure mode input to the second input unit 52 are transmitted to the storage unit 53. As described above, identification information indicating in which area of the storage device 53 the life should be stored may be input to the second input unit 52.
  • the storage unit 53 stores the inspection data and the inspection time transmitted from the first input unit 51, and the life and failure mode transmitted from the second input unit 52.
  • the storage unit 53 can be, for example, a hard disk drive.
  • the storage unit 53 may store other programs and / or data such as a program executed by the processor 54 described below.
  • the processor 54 can be, for example, one or more CPUs.
  • the processor 54 includes a machine learning unit 54 a and a remaining life prediction unit 54 b, and these can be realized by a program stored in the storage unit 53, for example.
  • the machine learning unit 54a performs machine learning to obtain an algorithm for predicting the remaining life of the device.
  • a neural network can be used, and an algorithm in which the weight in each neuron is determined is obtained by machine learning.
  • the machine learning unit 54a obtains “remaining life” in each past inspection time based on the life and inspection time stored in the storage unit 53 for each of the plurality of devices that have become unusable. Specifically, the “remaining life” at each inspection time can be calculated by subtracting each inspection time from the life. Note that, as described above, when the remaining life is calculated by the data collection device 30 on the user side of the machine tool 10, the machine learning unit 54a does not need to calculate the remaining life. I want.
  • the machine learning unit 54a performs machine learning with the inspection data as an input and the remaining life as an output, using data of a plurality of devices that have become unusable.
  • the machine learning unit 54a can further perform machine learning to obtain an algorithm for predicting the failure mode of the device.
  • the machine learning unit 54a performs machine learning using test data as input and failure mode as output using data of a plurality of devices that have become unusable.
  • FIG. 7 is a schematic diagram illustrating an example of machine learning according to the embodiment.
  • the machine learning unit 54a can perform machine learning based on various methods.
  • the machine learning performed by the machine learning unit 54a may be a convolutional neural network (CNN (Convolutional Neural Network)) that uses preprocessed inspection data.
  • CNN Convolutional Neural Network
  • wavelet transformation can be used as the preprocessing, and a wavelet transformed image is obtained by the preprocessing.
  • FIG. 8 shows an example of an image obtained as a result of wavelet transform from inspection data.
  • the image may include information indicating a comparison between the inspection data at the first inspection time and the inspection data at a certain inspection time after the second inspection.
  • the image can include a red portion (R), a green portion (G), and a blue portion (B).
  • R red portion
  • G green portion
  • B blue portion
  • a green part shows the area
  • the red part indicates a region where there is a change from the first inspection data.
  • the blue part indicates an area where there is no data.
  • the preprocessed image as described above can be used as an input to the CNN, and the remaining life (RUL (Remaining Useful Life)) and the failure mode are output.
  • RUL Remaining Useful Life
  • the machine learning may be a neural network based on long-short-term memory (LSTM (Long Short-Term Memory)) using feature values preprocessed from inspection data.
  • the feature amount may include, for example, the peak amplitude obtained by performing fast Fourier transform on the inspection data subjected to envelope processing.
  • the inspection data is subjected to, for example, a band pass filter, MED (Minimum Entropy Deconvolution), and envelope processing (for example, Hilbert transform), and then to a fast Fourier transform (FFT (Fast Fourier Transform)). May be.
  • MED Minimum Entropy Deconvolution
  • envelope processing for example, Hilbert transform
  • FFT Fast Fourier transform
  • the feature amount may include, for example, the peak amplitude obtained by directly performing fast Fourier transform on the inspection data, the root mean square value of the inspection data, and the kurtosis obtained from the inspection data.
  • the kurtosis can be obtained, for example, from inspection data passed through a bandpass filter and MED.
  • These preprocessed feature quantities can be used as inputs to the LSTM, with RUL and failure modes being the outputs. It should be noted that the feature amount may include a larger number of preprocessed numerical values.
  • the machine learning may be a neural network based on LSTM that uses inspection data as it is.
  • unprocessed inspection data can be used as input to the LSTM, and RUL and failure mode are output.
  • FIG. 9 is a schematic diagram illustrating another example of machine learning according to the embodiment.
  • the machine learning performed by the machine learning unit 54 a includes CNN that uses the wavelet-transformed image in FIG. 7, a neural network that is based on LSTM that uses feature values preprocessed from inspection data, and A combination of these may be used.
  • Machine learning may further include other neural networks.
  • the inspection data is passed through a filter that uses the results obtained by the CNN. Specifically, in a wavelet-transformed image, if the red portion occupies a predetermined ratio or more of the entire image, the corresponding inspection data is determined as “abnormal”; otherwise, the corresponding inspection data is “normal”. It is determined.
  • Only the inspection data determined to be “abnormal” can pass through the filter. As shown in FIG. 9, only the inspection data that has passed through the filter is preprocessed, and the feature value obtained from the preprocess is used in the neural network based on LSTM, and the RUL and failure mode are output.
  • the remaining life prediction unit 54 b uses the new inspection data input by the first input unit 51 and the algorithm obtained from the above machine learning, and the predicted remaining life of the device and Calculate the failure mode.
  • the “predicted remaining life” is an operation that is performed when it is assumed that a device that has been used for a certain period of use will continue to perform machining that is subject to a load similar to that before the machine tool 10 performs the prediction. It can mean the remaining time expected to be able to.
  • the remaining life prediction unit 54b can be updated.
  • the inspection data used in the machine learning unit 54a and the remaining life prediction unit 54b may be obtained by, for example, an acceleration test.
  • an acceleration test In this case, operations such as algorithm acquisition and system verification can be performed earlier.
  • the acceleration test can be performed by providing an indentation on a device such as a bearing 12, 13 or a ball screw.
  • the output unit 55 outputs the predicted remaining life and the failure mode obtained by the remaining life prediction unit 54b to the control device 20.
  • the output unit 55 can be, for example, an I / O port.
  • an inspection operation is executed on the spindle 11 every time the engine is warmed up, and a measured value is obtained by the sensor 15.
  • the measured value is sampled at a predetermined frequency and stored in the storage unit 31 as inspection data.
  • the storage unit 31 also stores the inspection time.
  • the data stored in the storage unit 31 is transmitted to the remaining life prediction apparatus 50.
  • the data stored in the storage unit 31 is the remaining life prediction device. 50 may be transmitted.
  • Data input through the first input unit 51 is stored in the storage unit 53.
  • the operator of the manufacturer inputs the life of the corresponding bearing through the second input unit.
  • the data input at the second input unit is stored in the storage unit 53.
  • the machine learning unit 54a When the data of the unusable bearings 12 or 13 exceeding the predetermined number is stored in the storage unit 53, the machine learning unit 54a performs machine learning using the inspection data as an input and the remaining life as an output. Further, the machine learning unit 54a performs machine learning using the inspection data as an input and the failure mode as an output. Then, the remaining life prediction unit 54 b calculates the predicted remaining life and failure mode of the device using the algorithm obtained from the machine learning and the new inspection data input by the first input unit 51. The calculated predicted remaining life and failure mode can be output from the output unit 55 to the control device 20 and displayed on the display unit 21 of the control device 20.
  • FIG. 10 is a graph showing the actual remaining life and the predicted remaining life.
  • the horizontal axis of FIG. 10 shows the usage time (or inspection time) of the bearing, and the vertical axis shows the ratio between the remaining life and the life of the bearing at each usage time.
  • the “actual remaining life” (Actual RUL) in FIG. 10 is calculated by subtracting each inspection time from the life of a bearing that has actually become unusable.
  • the “predicted remaining life” (Predicted10RUL) in FIG. 10 is calculated using the same inspection data of the bearing that has actually become unusable and the algorithm obtained from machine learning. As shown in FIG. 10, the predicted remaining life is in good agreement with the actual remaining life.
  • the algorithm used to calculate the “predicted remaining life” in FIG. 10 uses the inspection data and remaining life of a plurality of other bearings that have actually become unusable, and the two neural networks shown in FIG. It is derived from machine learning based on combinations.
  • the inspection operation the main shaft 11 was rotated at 1800 rpm (30 Hz).
  • the inspection data is vibration data representing the relationship between time and acceleration.
  • 41 feature quantities preprocessed from vibration data were used. Specifically, the 41 feature values are the inner ring, the outer ring, the rolling element, and the holding for each of the front bearing 12 and the rear bearing 13 ( ⁇ 2) obtained by direct Fourier transform of vibration data.
  • the rotational frequency (30 Hz) of the spindle 11 obtained by fast Fourier transform of the vibration data subjected to the envelope processing and a frequency (60 Hz) ( ⁇ ) twice that of the spindle 11 are obtained.
  • the remaining life prediction apparatus 50 As described above, in the remaining life prediction apparatus 50 according to the embodiment, machine learning is performed in which the inspection data obtained from a predetermined inspection operation is input and the remaining life at each inspection time is output. Then, the predicted remaining life of the bearing 12 or 13 is calculated using an algorithm obtained from machine learning and newly input inspection data. Therefore, the remaining life prediction device 50 can indicate the remaining life of the bearing 12 or 13.
  • machine learning is performed by (i) a convolutional neural network using an image wavelet transformed from inspection data, and (ii) a neural network based on long- and short-term memory using feature values preprocessed from the inspection data. It may include at least one of a network and (iii) a neural network based on long and short-term memory using the inspection data as it is.
  • the machine learning can also include a convolutional neural network and a neural network based on long- and short-term memory using preprocessed feature values. In this case, highly accurate prediction based on machine learning including two methods can be realized.
  • the preprocessed feature amount is obtained by performing fast Fourier transform directly on the amplitude of the peak obtained by performing fast Fourier transform on the inspection data subjected to envelope processing.
  • the peak amplitude, the root mean square value of the inspection data, and the kurtosis obtained from the inspection data are included. Therefore, highly accurate prediction based on machine learning including the plurality of feature amounts can be realized.
  • the inspection operation is included in the warm-up operation. Therefore, it is not necessary to operate the machine tool 10 only for the inspection operation.
  • FIG. 11 is a schematic block diagram illustrating a system including the machine tool according to the embodiment.
  • the remaining life prediction unit 32a is provided in the data collection device 30 on the user side of the machine tool 40, and the device 60 on the manufacturer side does not have the remaining life prediction unit 54b.
  • the processor 32 of the data collection device 30 has a remaining life prediction unit 32a.
  • the system 200 may be similar to the system 100.
  • the processor 32 can be, for example, one or more CPUs.
  • an algorithm for predicting the remaining life and failure mode of the device is obtained in the machine learning unit 54a of the device 60 on the manufacturer side in the same manner as the system 100 described above.
  • the obtained algorithm is copied to the data collection device 30 as the remaining life prediction unit 32a.
  • the remaining life prediction unit 32a can be realized by a program stored in the storage unit 31, for example. When new data of an unusable device is added to the storage unit 53 and the algorithm is updated in the machine learning unit 54a, the remaining life prediction unit 32a can be updated through the network.
  • the remaining life prediction unit 32a uses the inspection data measured by the sensor 15 and sampled at a predetermined frequency, and the algorithm obtained from the machine learning in the machine learning unit 54a, and the predicted remaining life and failure mode of the device. Is calculated. The calculated predicted remaining life and failure mode can be output to the control device 20 and displayed on the display unit 21.
  • the machine tool 40 as described above can indicate the remaining life of the device, similarly to the remaining life prediction apparatus 50 described above.

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Abstract

L'invention concerne un dispositif de prédiction de durée de vie restante (50) comprenant : une première unité d'entrée (51) pour recevoir des données d'inspection et des temps d'inspection pour des machines (12, 13) ; une unité de stockage (53) pour stocker les données d'inspection et les temps d'inspection ; une seconde unité d'entrée (52) pour entrer les durées de vie des machines (12, 13) lorsque les machines (12, 13) sont devenues inutilisables ; une unité d'apprentissage automatique (54b) configurée de façon à réaliser, pour une pluralité de machines (12, 13) qui sont devenues inutilisables, un apprentissage automatique ayant les données d'inspection en tant qu'entrée et ayant, en tant que sortie, des durées de vie restantes calculées pour chaque temps d'inspection passé sur la base des durées de vie et des temps d'inspection ; une unité de prédiction de durée de vie restante (54b) configurée de façon à calculer des durées de vie restante prédites pour des machines (12, 13) à l'aide des données d'inspection et d'un algorithme obtenu à partir de l'apprentissage automatique ; et une unité de sortie (55) pour délivrer les durées de vie restante prédites.
PCT/JP2018/013941 2018-03-30 2018-03-30 Dispositif de prédiction de durée de vie restante et machine-outil WO2019187138A1 (fr)

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Publication number Priority date Publication date Assignee Title
CN110554657A (zh) * 2019-10-16 2019-12-10 河北工业大学 一种数控机床运行状态健康诊断系统及诊断方法
WO2021090842A1 (fr) * 2019-11-08 2021-05-14 Dmg森精機株式会社 Machine-outil et dispositif d'affichage
WO2022064769A1 (fr) * 2020-09-24 2022-03-31 国立大学法人大阪大学 Système et procédé de prédiction d'état de dégradation
CN114186500A (zh) * 2022-02-16 2022-03-15 华中科技大学 一种基于迁移学习和多时窗的船用轴承剩余寿命预测方法
CN114186500B (zh) * 2022-02-16 2022-04-29 华中科技大学 一种基于迁移学习和多时窗的船用轴承剩余寿命预测方法
WO2024014070A1 (fr) * 2022-07-11 2024-01-18 株式会社日本製鋼所 Procédé d'inférence, dispositif d'inférence et programme informatique
WO2024048065A1 (fr) * 2022-08-30 2024-03-07 三菱重工業株式会社 Dispositif de traitement d'informations, procédé de traitement d'informations, et programme

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