JP2018072029A - Learning model structuring device, trouble forecasting system, learning model structuring method, and learning model structuring program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To forecast troubles of components with the situation of their use being taken into consideration.SOLUTION: A learning model structuring device is equipped with characteristic quantity extracting means that extracts from information on vibration provided regarding prescribed components characteristic quantities representing characteristics of the vibration, and learning means that receives as teacher data a combination of the characteristic quantities and a level, the latter being the length of time from the start of use of the prescribed components until they run into trouble, and performs mechanical learning the use of the received teacher data thereby to structure a learning model for forecasting the length of time from the start of use of the prescribed components until they run into trouble.SELECTED DRAWING: Figure 2

Description

  The present invention relates to a learning model construction apparatus, a learning model construction method and a learning model construction program for constructing a learning model for failure prediction, and a failure for performing failure prediction using the constructed learning model for failure prediction. It relates to the prediction system.

In general, electronic components are joined to a printed circuit board by soldering. In this case, it is necessary to check the quality as to whether the soldering is properly performed. Conventionally, such quality confirmation has been performed by human visual inspection.
However, each person's ability to perform a visual inspection is different. Moreover, even if they are the same person, their ability is not constant due to fatigue or the like. As a result, it has been difficult to check the quality with high precision by human visual inspection.

In view of such a situation, for example, Patent Document 1 and Patent Document 2 describe a technique for performing high-quality quality confirmation using a computer.
Specifically, Patent Document 1 discloses a technique of optically measuring a solder brick on a circuit board pad and determining the quality state of the solder based on the measured value.
In Patent Document 2, an input waveform is statistically processed to determine and collect an ideal waveform, and an abnormal waveform is determined based on the ideal waveform and a waveform of a circuit to be determined. Describes a waveform evaluation apparatus for detecting the presence or absence of the occurrence of. For example, based on the output waveform of the capacitor, it is detected that an abnormality that the capacitor is not of an appropriate type or an abnormality that the output characteristic is changed due to deterioration of the capacitor is detected.

Japanese Patent Application Laid-Open No. 09-026802 Japanese Unexamined Patent Publication No. 07-013983

  As described above, the current quality state of soldering on the printed circuit board can be determined by using the techniques disclosed in Patent Document 1 and Patent Document 2.

In this regard, even if electronic parts soldered to a printed circuit board are properly soldered at the time of manufacture, if they are used in an environment where vibration is applied, the solder joints will fatigue due to continuous vibration. Then, it may break without warning.
However, the techniques disclosed in Patent Literature 1 and Patent Literature 2 are merely for determining the current quality state. Therefore, even if these techniques are used, it is impossible to predict how much time will elapse in the future to cause a failure such as a break as described above. Also, it is not considered in what kind of environment the printed circuit board or the like is actually used.

  Accordingly, an object of the present invention is to provide a learning model construction device, a failure prediction system, a learning model construction method, and a learning model construction program for performing failure prediction of a part in consideration of an environment in which the part is used. And

  A learning model construction apparatus (for example, a learning model construction apparatus 200 described later) of the present invention extracts a feature quantity representing a feature of the vibration from information representing a vibration given to a predetermined part (for example, a feature quantity extraction unit). , A feature amount extraction unit 210 described later, the feature amount, and a label (for example, a label acquired by a label acquisition unit 220 described later) that is a length of time from the start of use of the predetermined component until failure. ) And as a teacher data, and by performing machine learning using the received teacher data, a learning model for predicting the length of time from the start of use of the predetermined part to the failure is constructed. A learning model construction device comprising learning means (for example, a learning unit 230 described later).

  In the learning model construction apparatus according to the present invention, the failure may be a failure that occurs when the predetermined component is damaged by the vibration.

  In the learning model construction apparatus according to the present invention, the predetermined part (for example, a BGA part described later) has a joining portion by soldering, and the damage of the predetermined part is the joining by soldering. It may be a broken part.

  In the learning model construction apparatus according to the present invention, the learning unit receives teacher data including temperature information around the predetermined part in addition to the feature amount and the label, and the teacher includes the received temperature information. The learning model may be constructed by performing machine learning using data.

  In the learning model construction device of the present invention, the feature amount may be data obtained by sampling waveform data obtained by measuring the vibration with a measuring device at a predetermined sampling period. .

  In the learning model construction apparatus of the present invention, the vibration may be a vibration generated in connection with use of a machine tool (for example, a machine tool 150 described later).

  A failure prediction system of the present invention (for example, failure prediction system 1000 described later) is a failure prediction system provided with the learning model construction device of the present invention and a failure prediction device (for example, failure prediction device 300 described later). The failure prediction device is based on the feature quantity of vibration given to the predetermined component that is the target of failure prediction and the learning model constructed by the learning model construction device. A failure prediction system comprising prediction means (for example, a failure prediction unit 310 described later) for predicting the length of time until the predetermined component fails.

  In the failure prediction system of the present invention described above, a plurality of failure prediction devices (for example, failure prediction devices 300-1 to 300-m described later) are provided, and each of the plurality of failure prediction devices is provided. A common learning model constructed by one learning model construction device is acquired, and a feature amount of vibration given to each of the predetermined parts to be predicted by each of the prediction means of the plurality of failure prediction devices And based on the acquired common learning model, the length of time until the predetermined part targeted for the failure prediction fails may be predicted.

  In the learning model construction method of the present invention, a feature amount extraction step (for example, processing by a feature amount extraction unit 210 described later) that extracts a feature amount that represents the feature of the vibration from information that represents vibration given to a predetermined component. And a combination of the feature quantity and a label (for example, a label acquired by a label acquisition unit 220 described later) that is a length of time from the start of use of the predetermined part to failure. A learning step for constructing a learning model for predicting the length of time from the start of use of the predetermined part to failure by performing machine learning using the received teacher data (for example, learning described later) Processing by the unit 230).

  The learning model construction program of the present invention is a learning model construction program for causing a computer to function as a learning model construction device (for example, a learning model construction device 200 described later), and represents a vibration applied to a predetermined component. Feature amount extraction means (for example, a feature amount extraction unit 210 to be described later) that extracts a feature amount that represents the feature of the vibration from the information, the feature amount, and a time from the start of use of the predetermined component until failure The predetermined component is started to be used by receiving a pair of labels (for example, a label acquired by a label acquiring unit 220 described later) as teacher data and performing machine learning using the received teacher data. Learning means for constructing a learning model for predicting the length of time from failure to failure (for example, a learning unit 230 described later). Is a learning model building program for causing the computer to function as that learning model building device.

  According to the present invention, it is possible to predict a failure of a part in consideration of the environment in which the part is used.

It is a block diagram showing the basic composition of the whole embodiment of the present invention. It is a block diagram showing the functional block contained in each apparatus in embodiment of this invention. It is an image figure shown about the failure prediction based on the machine learning in embodiment of this invention. It is a flowchart shown about the operation | movement at the time of the machine learning in embodiment of this invention. It is a figure which shows an example of the waveform data input in embodiment of this invention. It is a figure which shows an example of the method for extracting the feature-value from the waveform data input in embodiment of this invention. It is an image figure shown about the outline | summary of the learning model constructed | assembled in embodiment of this invention. It is a flowchart shown about the operation | movement at the time of the failure determination in embodiment of this invention.

  First, an outline of an embodiment of the present invention will be described. In the present embodiment, failure prediction of a predetermined component (for example, a BGA (Ball grid array) component) used in a numerical control device (CNC: Computer Numerical Control) for controlling a machine tool is performed.

A BGA component used in a numerical control device is soldered to a printed circuit board, but this solder joint may break due to fatigue due to continuous vibration. In this respect, if a strong vibration is applied for a long period of time, it will break, but there are various cases in the correlation between this breaking phenomenon and the vibration waveform.
Therefore, it is not easy to predict whether the solder joint will break based on only one case. For example, based on only one case, it is not easy to predict whether or not a BGA part will break within the product life of the numerical controller.

Therefore, in this embodiment, machine learning is performed using vibration waveforms of a large number of cases, such as a vibration waveform of a device that has broken and a vibration waveform of a device that has not broken and has not failed. Then, based on the learning model constructed by such machine learning and the vibration waveform of the device that is the target of failure determination, it is accurately predicted how long a failure such as a break will occur after a lapse of time. .
The above is the outline of the embodiment of the present invention.

  Next, the configuration of the failure prediction system 1000 according to the present embodiment will be described with reference to FIG. As shown in FIG. 1, the failure prediction system 1000 includes N numerical control devices 100, N machine tools 150, a learning model construction device 200, M failure prediction devices 300, and a network 400. N and M are arbitrary natural numbers.

  Connection between these devices will be described. The numerical control device 100 and the machine tool 150 are connected in a communicable manner. For example, a plurality of sets of the numerical control device 100 and the machine tool 150 may be installed in the same factory, or may be installed in different factories.

  The machine tool 150, the learning model construction device 200, and the failure prediction device 300 are each connected to the network 400 and can communicate with each other via the network 400. The network 400 is, for example, a local area network (LAN) built in a factory, the Internet, a public telephone network, or a combination thereof. There are no particular limitations on the specific communication method in the network 400, or whether it is a wired connection or a wireless connection.

  Next, functions of these devices included in the failure prediction system 1000 will be described with reference to FIG. Here, FIG. 2 is a block diagram showing functional blocks included in each device. Since each numerical control device 100 has an equivalent function, only one unit is shown in FIG. Similarly, since each machine tool 150 and each failure prediction apparatus 300 have the same function, only one unit is illustrated in FIG. Also, the network 400 existing between the devices is not shown.

The machine tool 150 is a general machine tool that performs processing such as cutting, polishing, and bending on a workpiece. The machine tool 150 performs machining by operating a machining tool by driving a servo motor or a linear motor based on control by the numerical control device 100.
Although vibration is generated with such processing, the vibration is also propagated to the numerical control apparatus 100 installed on the machine tool 150 or installed in the vicinity of the machine tool 150. The vibration propagated to the numerical control device 100 causes damage to the BGA components and the like inside the numerical control device 100 as described above.

  The numerical control apparatus 100 includes a measurement unit 110 for measuring vibration propagated from the machine tool 150. In addition, the numerical control apparatus 100 includes general functional blocks such as a functional block for controlling the machine tool 150, a functional block for performing communication, and a functional block for receiving an operation from the user. . However, since these general functional blocks are well known to those skilled in the art, illustration and description thereof are omitted. Similarly, in the learning model construction apparatus 200 and the failure prediction apparatus 300, which will be described later, general function blocks such as a function block for performing communication and a function block for receiving an operation from the user are illustrated and described. Omitted.

  The measurement unit 110 is a vibration sensor for measuring vibration propagated from the machine tool 150. The measurement unit 110 can be realized by, for example, a three-axis acceleration sensor that can measure accelerations in the three-axis directions of the X axis, the Y axis, and the Z axis that are orthogonal to each other. The acceleration in the three-axis directions of the X axis, the Y axis, and the Z axis measured by the measurement unit 110 is output as “input data” to the feature amount extraction unit 210 described later, as shown in the figure. More specifically, data obtained by digitizing an analog vibration waveform signal corresponding to the acceleration of each axis acquired by the triaxial acceleration sensor by analog-digital conversion is output as input data to the feature amount extraction unit 210 described later. Is done.

  The measurement unit 110 may be attached to the numerical controller 100 for the present embodiment. However, depending on the model of the numerical controller, such a triaxial acceleration sensor may be incorporated for other purposes. In such a case, the measurement value of the triaxial acceleration sensor incorporated in advance may be used also in this embodiment. The measurement unit 110 may be realized by a combination of acceleration sensors that measure acceleration in a uniaxial direction.

  In addition, the numerical controller 100 receives an input of a label from the user. Here, the label is a correct output that should correspond to an input in machine learning. In the present embodiment, the label is the length of time from the start of use of a predetermined component (for example, a certain type of BGA component) provided in common to each numerical control device 100 as a measurement target until failure. It is. More specifically, there are information such as “failed in 5 years from the start of use”, “failed in 7 years from the start of use”, and information such as “20 years have passed since the start of use but no failure”. The received label is output as “label” to the label acquisition unit 220 described later, as shown in the figure.

  The learning model construction device 200 is a device that performs machine learning using vibration waveforms of a number of cases such as a vibration waveform of the numerical control device 100 in which a failure has occurred and a vibration waveform of the numerical control device 100 in which no failure has occurred. is there. Then, the learning model construction device 200 constructs a learning model for performing failure prediction through such machine learning.

  For this purpose, the learning model construction apparatus 200 includes a feature amount extraction unit 210, a label acquisition unit 220, a learning unit 230, and a learning model storage unit 240.

  The feature quantity extraction unit 210 is a part that extracts a feature quantity from the input data input from the measurement unit 110 of the numerical control device 100. Specific contents of the feature amount extraction will be described later with reference to FIGS.

  The label acquisition unit 220 is a part that acquires a label from the numerical controller 100 and outputs the acquired label to the learning unit 230. Instead of acquiring a label from the numerical control device 100, an operation receiving unit (not shown) of the learning model construction device 200 receives a user's label input operation, thereby acquiring the label. Also good.

  In this way, the feature quantity of the numerical control device 100 as the measurement target and the label of the numerical control device 100 as the measurement target form a set and are input to the learning unit 230. This set of feature quantity and label corresponds to teacher data in machine learning.

Here, it is desirable to prepare a lot of teacher data for performing machine learning. Therefore, for example, for each of the numerical control devices 100 equipped with a three-axis acceleration sensor used at a customer site, a feature amount is acquired on any day during operation. For example, the characteristic amount is acquired by measuring the vibration on the day when the maintenance of the numerical control device 100 is attended. Then, if a predetermined part of any numerical control device 100 fails at a later date, new teacher data is created by combining the length of time until the failure and the feature value acquired for the numerical control device 100. It is recommended to create. In this way, a large amount of teacher data can be created by the numerical control device 100 that is normally used by customers or the like.
For example, teacher data may be created in a laboratory or the like. For example, in the laboratory, the numerical control apparatus 100 is vibrated at a frequency three times the normal frequency by intentionally applying vibration. If a predetermined part of the numerical control device 100 fails in one year, teacher data may be created by combining a label indicating failure in three years.

  The learning unit 230 constructs a learning model by performing machine learning based on the teacher data input in this way. Details of the machine learning by the learning unit 230 will be described later with reference to FIG. The learning model constructed by the learning unit 230 is output to the learning model storage unit 240 and a failure prediction unit 310 described later.

  The learning model storage unit 240 is a storage unit that stores the learning model constructed by the learning unit 230 and output to the learning model storage unit 240.

  The failure prediction device 300 is a device that performs failure prediction in the present embodiment. The failure prediction apparatus 300 includes a failure prediction unit 310 and a failure prediction result output unit 320.

  The failure prediction unit 310 performs failure prediction using the learning model input from the learning unit 230 and the determination data input from the outside. Then, the failure prediction unit 310 outputs a result of failure prediction performed using these data as a “failure prediction result” to the failure prediction result output unit 320 as shown in the figure.

Here, the learning model input from the learning unit 230 to the failure prediction unit 310 is constructed by the learning unit 230 as described above.
The determination data input to the failure prediction unit 310 is data equivalent to the feature amount input to the learning unit 230, and the analog waveform signal acquired by the measurement unit 110 of the numerical control device 100 that is the target of failure prediction. Is the feature amount extracted from the digitized data.
The determination data may be input from the feature amount extraction unit 210 of the learning model construction device 200. In addition, a functional block equivalent to the feature quantity extraction unit 210 is provided in the failure prediction apparatus 300, and a functional block equivalent to the feature quantity extraction unit 210 is characterized from input data output by the numerical controller 100. The amount may be extracted.

  The failure prediction result output unit 320 is a part that outputs the failure prediction result input from the failure prediction unit 310. The output may be, for example, display of the failure prediction result on the display unit, transmission of the failure prediction result to another device, or printing of the failure prediction result on a paper medium. Also good.

  By referring to the failure prediction result, the user can know the length of time until failure predicted for a predetermined component included in the numerical controller 100 that is the target of failure prediction this time. As a result, it is possible to determine the part replacement time for replacing parts prior to failure. In addition, it is possible to know that there is a high possibility that parts will not fail during the product life (for example, 20 years). A specific example of the failure prediction result will be described later with reference to FIG.

The functional blocks of the numerical control device 100, the learning model construction device 200, and the failure prediction device 300 have been described above. Next, a method for realizing these functional blocks will be described.
In order to realize these functional blocks, each of the numerical control device 100, the learning model construction device 200, and the failure prediction device 300 includes an arithmetic processing device such as a CPU (Central Processing Unit). In addition, in each of the numerical control device 100, the learning model construction device 200, and the failure prediction device 300, an auxiliary storage device such as an HDD (Hard Disk Drive) that stores various control programs, or an arithmetic processing device executes the program. A main storage device such as a RAM (Random Access Memory) for storing data temporarily required above is provided.

  In each device, the arithmetic processing device reads an application or OS from the auxiliary storage device, and performs arithmetic processing based on the application or OS while developing the read application or OS in the main storage device. In addition, based on the calculation result, various hardware included in each device is controlled. Thereby, the functional block of this embodiment is implement | achieved. That is, this embodiment can be realized by cooperation of hardware and software.

  As a specific example, the numerical control device 100 can be realized by a general numerical control device including the measurement unit 110. Further, each of the learning model construction device 200 and the failure prediction device 300 can be realized by a personal computer.

  However, since the learning model construction apparatus 200 has a large amount of computation accompanying machine learning, for example, a personal computer is equipped with GPU (Graphics Processing Units), and a technology called GPGPU (General-Purpose computing on Graphics Processing Units) If the GPU is used for arithmetic processing accompanying machine learning, high-speed processing can be performed. Furthermore, in order to perform higher-speed processing, a computer cluster is constructed by using a plurality of computers equipped with such GPUs, and parallel processing is performed by a plurality of computers included in the computer cluster. May be.

  Next, a basic concept regarding machine learning in the present embodiment will be described with reference to FIG. The upper part of FIG. 3 shows an image for machine learning. Further, the lower part of FIG. 3 shows an image of failure prediction. FIG. 3 is a diagram for illustrating the concept, and is a very schematic diagram for easy understanding.

A plurality of numerical control devices 100 included in this embodiment are connected to different machine tools 150. Since the machine tools 150 perform different processes, they vibrate differently. Therefore, as shown in the upper part of FIG. 3, the vibrations measured by the measurement unit 110 of each numerical control device 100 have different waveforms. Also, the length of time until the failure that becomes the label is 5 years, 7 years, or normal for 10 years or more.
Then, as described above, machine learning is performed using a combination of a feature amount extracted from a waveform as input data and a label as teacher data, and a learning model is constructed using a plurality of teacher data.

When the learning model is constructed in this way, as shown in the lower part of FIG. 3, the constructed learning is performed by using the feature amount extracted from the waveform measured by the measurement unit 110 of the numerical control device 100 as a failure prediction target as determination data. Failure prediction using a model can be performed The above is the basic idea regarding machine learning in this embodiment.

  Next, the operation of the learning model construction device 200 during machine learning will be described with reference to the flowchart of FIG.

  First, the learning model construction device 200 acquires input data and a label from any numerical control device 100 (step S11). The specific contents of these data are as described above.

Next, the learning model construction device 200 extracts a feature amount from the input data acquired in step S11 (step S12). This feature amount extraction will be described with reference to FIGS.
First, input data which is a premise will be described with reference to FIG. FIG. 5 shows an example of an acceleration waveform that changes along the time series measured by the measurement unit 110 of the numerical controller 100 as input data. The waveform used as the input data is measured for a certain period, but in this description, the certain period is 9:00 to 21:00 as an example. The numerical control device 100 is assumed to be continuously used in the same manner every day. For example, it is assumed that the numerical controller 100 repeats that the power is turned on at the same time every morning, the predetermined processing is performed at a predetermined cycle, and the power is turned off at the same time every evening. To do.
If it is repeated in the same way every day, for example, the first processing method is used in the morning, and the second processing method is used in the afternoon. It may be how.

  FIG. 5 shows the acceleration in the X-axis direction for a certain period (from 9:00 to 21:00 as an example), but the actual input data also includes the acceleration in the Y-axis direction for a certain period and a certain constant. The acceleration in the Z-axis direction of the period is included.

  Here, it is conceivable to make the fixed period significantly shorter than this, or to use the acceleration of one axis instead of using the acceleration of three axes. Although this embodiment functions in this way, this is not done this time from the viewpoint described below.

As described above, parts such as BGA parts break due to vibrations, but it is difficult for the user to determine what kind of vibrations cause the breaks. For example, the vibration caused by the opening / closing of the door of the machine tool 150 by the user when the workpiece is set on the machine tool 150 is not the vibration at the time of machining by the numerical control device 100, causing the breakage. It may be. Therefore, instead of setting a specific short time period from the start to the end of a certain process as a fixed period, machine learning is performed with a long period such as 12 hours as a fixed period.
In other words, in this example, based on the idea that it is preferable to perform machine learning using as much input data as possible without selecting the input data based on judgment by the user, a long time such as 12 hours is set as a certain period. I am trying to learn.

Similarly, it is difficult for the user to determine which shaft vibration causes breakage. Also, the combination of vibrations of multiple axes may be the cause of breakage, but if only the vibration of one axis is considered, the combination of vibrations of multiple axes is the cause of breakage. The user cannot judge.
Therefore, in the same way as the above-described method for determining the length of a certain period of time, the user does not select which axis vibration is important, but uses machine vibrations using all three axis vibrations. Is going to do. Thereby, it can be expected that a more appropriate learning model can be constructed.

Next, with reference to FIG. 6, the extraction of the feature amount from the input data shown in FIG. 5 will be described.
In this embodiment, as shown in FIG. 6 as an example, feature amounts are extracted by sampling input data at a resolution of 16 bits and a sampling period of 1 ms.
The amount of data extracted in this case is a fixed time of 12 hours, a sampling period of 1 ms, and 16 bits = 2 bytes. Therefore, as shown in the following [Equation 1], approximately 82 Mbytes is equivalent to one axis. The amount of data.

[Formula 1]
1000 [ms] × 60 [sec] × 60 [min] × 12 [hour] × 2 [byte] ≈82 [Mbyte]

  Note that feature values are extracted not only for the X axis but also for the Y axis and the Z axis.

  When the feature amount is extracted in this manner, a set of the extracted feature amount and label is used as teacher data and is output to the learning unit 230 (step S13).

  Then, the learning unit 230 performs machine learning using the input teacher data (step S14). Machine learning using the teacher data will be described with reference to FIG.

As shown in FIG. 7, a learning model 250 that is a learning model in the middle of construction includes a neural network 251 and a weighting value 252.
In this embodiment, machine learning is performed by a neural network configured by combining perceptrons. Specifically, a set of feature values and labels included in the teacher data is given to the neural network, and learning is repeated while changing the weights for each perceptron so that the output of the neural network is the same as the label. In this manner, the learning model for learning the characteristics of the teacher data and estimating the result from the input is obtained inductively.

  More specifically, as shown in FIG. 7, three layers of perceptrons are combined. When a plurality of inputs X are input from the left side of the neural network diagram, a plurality of outputs Y are output from the right side of the diagram.

In the following description, each perceptron included in the neural network 251 is indicated by “P with two subscripts attached”. In FIG. 7, each perceptron included in the neural network 251 is indicated by “P of a circled character with two subscripts”. The first letter of the P subscript represents the number of dimensions of the perceptron, and the second letter of the subscript represents the number of layers of the perceptron. For example, P ₂₁ represents the second-dimensional and first-layer perceptron.

  Here, the number of dimensions of the leftmost layer of the neural network 251 (hereinafter referred to as “input layer”) is “i”. The value of i is an arbitrary natural number. However, since the fixed time is 12 hours and the sampling period is 1 ms as described above for the description of the feature amount extraction, the number of sampling data is as shown in [Equation 2] below. It becomes 432 million. Therefore, the value of i is also 43200000. It is assumed that the first sampling is performed at t1, the second sampling is performed at t2, and the i-th sampling is performed at ti.

[Formula 2]
1000 [ms] × 60 [sec] × 60 [min] × 12 [hour] = 43200000 [pieces]

  The number of dimensions of the central layer of the neural network 251 (hereinafter referred to as “hidden layer”) is “j”. The value of j is an arbitrary natural number. Further, the value of j may be changed during the machine learning process.

  Furthermore, the number of dimensions of the rightmost layer of the neural network 251 (hereinafter referred to as “output layer”) is “k”. k is an arbitrary natural number, but this time, the value of k is 21. The reason will be explained. In this embodiment, as an example, the product life of the machine tool 150 is assumed to be 20 years. Then, it is predicted whether a failure will occur in a certain number of years within 20 years, or a failure after 20 years or more. Therefore, a predetermined part (for example, a certain type of BGA part) provided with a label in common with each numerical control device 100 fails in 1 year, fails in 2 years, ..., fails in 20 years, It is created in units of one year, such as failure after 20 years. Therefore, the label type is 21. Therefore, the value of k is set to 21 as described above.

In the neural network 251, the output of each perceptron is output to all the perceptrons in the next layer. For example, the output of P _{11 in} the output layer is output to each of P ₂₁ ... P _j2 which are all perceptrons in the hidden layer. Similarly, each of the outputs of the _P 21 ··· _{P n1} of the output layer, is output to each of the _P 21 ··· _{P j2} are all perceptron of hidden layer. However, in the figure, in consideration of the visibility of the figure, illustration of some perceptrons and outputs to the some perceptrons are omitted.

The weighting value 252 is a weighting value used in machine learning performed by the learning unit 230. The learning unit 230 multiplies the output value of each perceptron included in the neural network 251 by a weighting value corresponding to the output perceptron, and inputs the result to all perceptrons in the next layer.
That is, the output of each perceptron included in the hidden layer or the output layer is the sum of values obtained by multiplying the output of each perceptron in the previous layer by the weighting value corresponding to each perceptron.

  Although not shown, a bias may be input to each perceptron. The bias is a value obtained by multiplying the value “1” by a weight value corrected in the course of machine learning.

In the following description and drawings, the weighting value is represented by “W with two subscripts added”. Further, the input of the perceptron in the input layer is represented by “X with two subscripts added”, and the output of the perceptron in the output layer is represented by “Y with two subscripts added”.
Here, these two-character subscripts mean values corresponding to perceptrons to which the same two-character subscripts are attached. For example, W ₁₁ represents a weighting value corresponding to P ₁₁ . Further, for example, _{X 11 represents} that the input corresponding to _{P 11.} Further, for example, Y ₁₃ represents an input corresponding to P ₁₃ .

  Next, a method for executing machine learning will be described. First, i feature quantities and labels are input to the learning unit 230 as teacher data. As an example, this time, it is assumed that a label of failure has been input in 20 years or more.

  Then, i feature value values are respectively input to the i perceptrons of the input layer. Then, a value obtained by multiplying the input feature value by a weight value corresponding to each perceptron in the input layer is input to each perceptron in the hidden layer.

  Next, each perceptron in the hidden layer outputs the sum of the values input to itself, and the value obtained by multiplying this output value by the weight value corresponding to each perceptron in the hidden layer is each output layer. Input to the perceptron.

Furthermore, each perceptron in the output layer outputs the sum of the values input to itself. This output corresponds to Y ₁₃ , Y ₂₃ ... Y _k3 in the figure. These outputs correspond to the classification of failure in 1 year, failure in 2 years, ... failure in 20 years, failure in 20 years or more. In the example shown in the figure, the value of Y ₁₃ (corresponding to the classification of failure in one year) is 0.03, and the value of Y ₂₃ (corresponding to the classification of failure in two years) is 0.05. On the other hand, the value of Y _k3 (corresponding to the classification of failure in 20 years or more) is 0.70, which is a very high value.
And since this label is a label of a failure in 20 years or more, it turns out that the appropriate output is made.

The above operation is called forward propagation. After performing forward propagation in this way, the weighting value is adjusted so as to reduce the error in the output of each perceptron by a technique called back-propagation (also called back propagation).
Specifically, the error between each output Y and the label is calculated, and the weighting value between the hidden layer and the output layer is corrected so as to reduce the calculated error. Further, the weighting value between the hidden layer and the output layer is corrected based on the amount of correction of the weighting value between the hidden layer and the output layer.

  Thus, when forward propagation and back propagation are performed, the learning unit 230 determines whether to end the machine learning or repeat the machine learning (step S15). Here, the condition for terminating the machine learning can be arbitrarily determined. For example, the machine learning may be terminated when the error value between each output Y and the label becomes a predetermined value or less. In addition, machine learning may be terminated when machine learning is repeated a predetermined number of times.

  In any case, if the condition is not satisfied and the machine learning is not yet finished (No in step S15), the process returns to step S11, and the new teacher data or the same teacher data is targeted. Based on each weighting value, it repeats performing forward propagation and back propagation again.

  If the conditions for ending machine learning are satisfied in the process of repeating machine learning in this way (Yes in step S15), machine learning is ended and a learning model constructed by machine learning up to that point is transmitted. (Step S16). For example, the learning model is transmitted to each failure prediction apparatus 300 via the network 400.

  In addition, the learning model is output to the learning model storage unit 240 and stored therein. If the learning model storage unit 240 stores the learning model in this way, the learning model can be transmitted when a learning model is requested from the newly installed failure prediction apparatus 300. Further, when new teacher data is acquired, further machine learning can be performed on the learning model.

  The construction of the learning model by the learning unit 230 has been described above. Next, failure prediction using the learning model constructed in this way will be described with reference to the flowchart of FIG.

  The failure prediction unit 310 of the failure prediction device 300 acquires the learning model by receiving the learning model transmitted from the learning model construction device 200 (step S21). Also, determination data is acquired from the feature quantity extraction unit 210 and the numerical control device 100 (step S22). The details of the determination data acquisition method are as described above when the failure prediction unit 310 is described with reference to FIG.

  Next, the failure prediction unit 310 performs failure prediction based on the acquired learning model and the acquired determination data (step S23). Specifically, i feature value values included in the acquired determination data are input to the i perceptrons of the acquired learning model one by one. This is the same as the input in the machine learning described with reference to FIG. 4 and FIG.

Thereafter, similarly to machine learning, the weighting values corrected by machine learning are multiplied and output to the hidden layer and the output layer, and finally output Y (Y ₁₃ , Y ₂₃ ... From each output layer. Y _k3 ) is made. Such output Y is a failure prediction result.

Next, the failure prediction unit 310 outputs the failure prediction result to the failure prediction result output unit 320. Then, the failure prediction result output unit 320 outputs the input failure prediction result (step S24). The output method is as described above when describing the failure prediction result output unit 320 with reference to FIG. 2, and is performed by, for example, displaying on a screen. At this time, all of the outputs Y (Y ₁₃ , Y ₂₃ ... Y _k3 ) as failure prediction results may be output. However, _these, Y _13, Y 23 may output some of · · · _{Y k3.} For example, if _{Y 13, Y} 23 ··· _{Y k3} as shown in Figure 7 is outputted, it is sufficient to output the highest value is greater _{Y k3} as failure prediction result. In this case, a message such as “failure in 20 years or more” corresponding to Y _k3 may be output.

  In the present embodiment described above, the user refers to the failure prediction result output in this way, and thereby a predetermined component (for example, a certain type of BGA) included in the numerical control device 100 targeted for failure prediction. There is an effect that it is possible to know as a prediction result the length of time from the start of use of the component to the failure.

  In addition, since the prediction result can be obtained from the vibration measured in a state where the numerical control device 100 is connected to the machine tool 150, an environment in which predetermined parts provided in the numerical control device 100 are actually used is taken into consideration. There is also an effect that the prediction result can be known.

  In addition, there is an effect that the prediction result can be known only by measuring the vibration for a certain period (for example, 9: 00 to 21:00).

<Modification>
Although the above-described embodiment is a preferred embodiment of the present invention, the scope of the present invention is not limited only to the above-described embodiment, and various modifications are made without departing from the gist of the present invention. Can be implemented.

  For example, in the above-described embodiment, the feature amount is extracted from the vibration waveform measured by the numerical controller 100. Then, teacher data is created using the vibration feature amount and the label.

  In addition to this, another measurement value that causes a failure of a predetermined component (for example, a certain type of BGA component) included in the numerical controller 100 may be included in the teacher data. For example, a temperature sensor is installed inside the numerical controller 100. Then, the temperature of a predetermined component provided in the numerical controller 100 measured by the temperature sensor or the temperature around the predetermined component may be included in the teacher data as one of the feature amounts.

  In this respect, the temperature does not fluctuate greatly in a very short time like vibration. Therefore, instead of setting the sampling period to 1 ms, for example, the value of the sampled temperature is set as the feature amount as the sampling period 1 hour in a certain period (for example, 9: 00 to 21:00). To do. Further, an average value of temperatures sampled at a sampling period of 1 hour is defined as a feature amount.

  Then, the number of perceptrons in the input layer is increased by the number of feature quantities based on temperature, and the feature quantity based on temperature is input to the increased perceptron in the input layer. Thereby, it is possible to perform failure prediction in consideration of temperature.

  Moreover, you may make it further include other measured values other than temperature in a feature-value. For example, the measured humidity value may be further included in the feature amount.

  As another modification, the neural network described with reference to FIG. 7 has three layers. However, the number of layers may be further increased. That is, learning and failure prediction may be performed by so-called deep learning (also called deep learning). As another modification, a self encoder may be used.

  Still another modification will be described. As described above, as described above, data obtained by digitizing an analog vibration waveform signal corresponding to the acceleration of each axis obtained by the triaxial acceleration sensor by analog-to-digital conversion was sampled as shown in FIG. I used something. However, other methods may be used. For example, Fourier transform may be performed on the sampled data as shown in FIG. 6, and the frequency spectrum obtained by the Fourier transform may be used as the feature amount.

As yet another modification, the learning model construction device 200 and the failure prediction device 300 may be realized as an integrated device.
Alternatively, the learning model construction device 200 and the numerical control device 100 may be realized as an integrated device.

  Each device included in the failure prediction system can be realized by hardware, software, or a combination thereof. Moreover, the failure prediction method performed by each device included in the failure prediction system can also be realized by hardware, software, or a combination thereof. Here, “realized by software” means realized by a computer reading and executing a program.

  The program may be stored using various types of non-transitory computer readable media and supplied to the computer. Non-transitory computer readable media include various types of tangible storage media. Examples of non-transitory computer readable media include magnetic recording media (for example, flexible disks, magnetic tapes, hard disk drives), magneto-optical recording media (for example, magneto-optical disks), CD-ROMs (Read Only Memory), CD- R, CD-R / W, and semiconductor memory (for example, mask ROM, PROM (Programmable ROM), EPROM (Erasable PROM), flash ROM, RAM (random access memory)). The program may also be supplied to the computer by various types of transitory computer readable media. Examples of transitory computer readable media include electrical signals, optical signals, and electromagnetic waves. The temporary computer-readable medium can supply the program to the computer via a wired communication path such as an electric wire and an optical fiber, or a wireless communication path.

DESCRIPTION OF SYMBOLS 100 Numerical control apparatus 110 Measurement part 150 Machine tool 200 Learning model construction apparatus 210 Feature-value extraction part 220 Label acquisition part 230 Learning part 240 Learning model memory | storage part 250 Learning model 251 Neural network 252 Weight value 300 Failure prediction apparatus 310 Failure prediction part 320 Failure prediction result output unit 1000 Failure prediction system

Claims (10)

A feature amount extracting means for extracting a feature amount representing the feature of the vibration from information representing the vibration given to a predetermined part;
A set of the feature amount and a label that is a length of time from when the predetermined part is used until it breaks down is received as teacher data, and machine learning is performed using the received teacher data, whereby the predetermined amount is obtained. A learning means for constructing a learning model for predicting the length of time from the start of use of a part to the time of failure;
A learning model construction device comprising:   The learning model construction apparatus according to claim 1, wherein the failure is a failure that occurs when the predetermined component is damaged by the vibration.   The learning model construction apparatus according to claim 2, wherein the predetermined component has a joint portion by soldering, and the damage of the predetermined component is a break of the joint portion by the soldering.   The learning means receives teacher data including temperature information around the predetermined component in addition to the feature amount and the label, and performs machine learning using the teacher data including the received temperature information to obtain the learning model. The learning model construction device according to claim 1, wherein the learning model construction device is constructed.   The learning model according to any one of claims 1 to 4, wherein the feature amount is data obtained by sampling waveform data obtained by measuring the vibration with a measuring device at a predetermined sampling period. Construction device.   The learning model construction apparatus according to any one of claims 1 to 5, wherein the vibration is vibration generated in association with use of a machine tool. A failure prediction system comprising the learning model construction device according to any one of claims 1 to 6 and a failure prediction device,
The failure prediction apparatus is
Based on the feature amount of vibration given to the predetermined part to be subject to failure prediction and the learning model constructed by the learning model construction device, the predetermined part to be subject to failure prediction is a failure. A prediction means for predicting the length of time until
A failure prediction system comprising: A plurality of the failure prediction devices,
Each of the plurality of failure prediction devices acquires a common learning model constructed by one learning model construction device,
Each of the prediction means of the plurality of failure prediction apparatuses is based on the feature quantity of vibration given to the predetermined component that is the target of failure prediction, and the failure prediction target based on the acquired common learning model The failure prediction system according to claim 7, wherein a length of time until the predetermined component fails is predicted. A feature amount extraction step for extracting a feature amount representing the feature of the vibration from information representing the vibration applied to a predetermined part;
A set of the feature amount and a label that is a length of time from when the predetermined part is used until it breaks down is received as teacher data, and machine learning is performed using the received teacher data, whereby the predetermined amount is obtained. A learning step for building a learning model for predicting the length of time from the start of use of a part to the time of failure;
A learning model construction method comprising: A learning model construction program for causing a computer to function as a learning model construction device,
A feature amount extracting means for extracting a feature amount representing the feature of the vibration from information representing the vibration given to a predetermined part;
A set of the feature amount and a label that is a length of time from when the predetermined part is used until it breaks down is received as teacher data, and machine learning is performed using the received teacher data, whereby the predetermined amount is obtained. A learning means for constructing a learning model for predicting the length of time from the start of use of a part to the time of failure;
A learning model construction program for causing the computer to function as a learning model construction device.

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