Patent Description:
Conventionally, a technique is known to detect a press abnormality based on a load waveform of a press load when pressing a work in a press system (see, for example, Patent Document <NUM>).

Further, a technique is known to control a motor by using a probability density function depending on the speed of a servomotor (see, for example, Patent Document <NUM>).

The technique described in Patent Document <NUM> or Patent Document <NUM> determines whether an abnormality occurs according to whether a measured value of the device in a machining operation for machining the work is within the standard deviation.

<CIT> relates to a diagnostic method of diagnosing a press for the presence of any abnormality that deteriorates a quality of a product manufactured by the press, wherein physical value such as a load generated at a selected portion of the press during operation of the press is detected, and the press is diagnosed for any abnormality, on the basis of the detected physical value, and according to a predetermined reference.

<CIT>relates to a diagnostic device and method for monitoring the operation of a slave or ratio control loop in a meshed control structure of an automation system.

In addition, in a target device using a servomotor as a power source, the prediction curve of the press load or the motor speed during the machining operation may sometimes have a steep part during the machining operation. In a case where whether an abnormality occurs is determined based on whether the measured value of the device in the machining operation is within the standard deviation as in the related art described above, when the prediction curve is steep, it is likely to determine that there is an abnormality even though no abnormality occurs.

One aspect of the present invention has been made in view of the above circumstances, and an objective of the present invention is to provide a technique capable of appropriately performing an abnormality determination.

The following disclosure serves a better understanding of the present invention. To solve the above problems, an abnormality detecting device according to claim <NUM> is provided.

Further, to solve the above problems, an abnormality detecting method according to claim <NUM> is provided.

Further, an abnormality detecting program according to claim <NUM> is provided.

According to an aspect of the present invention, it is possible to appropriately perform an abnormality determination of a target device.

Hereinafter, an embodiment (hereinafter also referred to as "the present embodiment") according to one aspect of the present invention will be described with reference to the drawings.

An example of a situation in which the present invention is applied will be described with reference to <FIG> is a view schematically showing a site where an abnormality detecting device <NUM> according to the present embodiment is used.

As shown in <FIG>, the abnormality detecting device <NUM> is, for example, a device used at a production site to detect an abnormality of a target device <NUM>. The abnormality detecting device <NUM> is realized by, for example, a PLC (programmable controller). In a configuration in which the abnormality detecting device <NUM> is realized by a PLC, the abnormality detecting device <NUM> may have a configuration that controls the operation of the target device <NUM>. The abnormality detecting device <NUM> is connected to one or more target devices <NUM> via a network such as a field network or a local network.

The target device <NUM> is, for example, a press machine driven by a servomotor as a power source. The press machine, which is an example of the target device <NUM>, rotates a servomotor <NUM>, converts the rotational motion of the servomotor <NUM> into a linear motion by an actuator <NUM>, and press-fits a press-fitting work 5b into a press-fitted work 5a via a press tool <NUM>.

When the press tool <NUM> of the press machine performs press-fitting repeatedly, the contact surface with the press-fitting work 5b wears. As the wearing of the press tool <NUM> continues, metal powder is generated, and at the time of press-fitting, if the metal powder is brought into a space between the press-fitting work 5b and the press-fitted work 5a, an abnormality of foreign matter biting, an abnormality in which the press tool cannot be pulled out, etc. occurs.

The abnormality detecting device <NUM> has a function of collecting, learning, and monitoring data related to the operation of the target device <NUM>. From the target device <NUM>, the abnormality detecting device <NUM> acquires, for example, information such as a torque, a speed, and a position of the servomotor, information of a load applied to the press tool <NUM> measured by a load cell <NUM>, and a sensor value (position) detected by a displacement sensor <NUM>.

Based on the data related to the operation of the target device <NUM>, the abnormality detecting device <NUM> acquires a first index value associated with a first index, e.g., a value related to a stage of the operation of the target device <NUM>, and a second index value associated with a second index, e.g., a value related to a load of the operation of the target device <NUM>, and with reference to these two index values, determines whether an abnormality occurs in the target device <NUM>.

Specifically, the abnormality detecting device <NUM> determines whether an abnormality occurs in the target device <NUM> based on a distance from a predetermined reference curve to a point indicated by the first index value and the second index value on a two-dimensional plane with the first index and the second index being axes.

Accordingly, the abnormality detecting device <NUM> can monitor from the start of the machining operation to the end of the machining operation of the target device <NUM> and detect an abnormality sign at an early stage. Further, even in a situation in which the sensor value suddenly changes in the operation process of the target device <NUM>, it is possible to appropriately determine whether an abnormality occurs. Further, by performing an abnormality determination using a value related to the stage of the operation of the target device <NUM> and a value related to the load of the operation of the target device <NUM>, it is possible to appropriately perform an abnormality determination at each stage of the operation of the target device <NUM>.

Hereinafter, an embodiment of the present invention will be described in detail.

<FIG> is a block diagram showing a main configuration of the abnormality detecting device <NUM>. As shown in <FIG>, the abnormality detecting device <NUM> includes a communication part <NUM>, a control part <NUM>, and a storage part <NUM>.

The communication part <NUM> performs communication with the target device <NUM> via a network and executes transmission/reception of data. The communication part <NUM> is realized using, for example, an integrated circuit (IC) such as a communication IC. The communication part <NUM> performs communication with the target device <NUM> by wired communication or wireless communication.

The control part <NUM> is a computation device having a function of comprehensively controlling each part of the abnormality detecting device <NUM>. The control part <NUM> may control each part of the abnormality detecting device <NUM> by, for example, executing a program stored in one or more memories (e.g., a RAM or a ROM) by one or more processors (e.g., a CPU).

The storage part <NUM> stores various data used by the control part <NUM> and various software executed by the control part <NUM>. Further, the storage part <NUM> stores data related to the operation of the target device <NUM> acquired and learned from the target device <NUM> by the control part <NUM>.

The control part <NUM> includes an acquisition part <NUM>, a reference generation part <NUM>, a scale normalization part <NUM>, and an abnormality detecting part <NUM>.

The acquisition part <NUM> acquires a first index value associated with a first index and a second index value associated with a second index in the operation of the target device <NUM> via the communication part <NUM>. The value of the first index and the value of the second index in the operation of the target device <NUM> are values related to a position, a torque, a load applied to the press tool <NUM> (a load applied to the press-fitting work 5b), etc. indicated by the servomotor <NUM>, the displacement sensor <NUM>, or the load cell <NUM> of the target device <NUM>. The load applied to the press tool <NUM> may also be estimated from the torque of the servomotor <NUM>.

The reference generation part <NUM> collects the first index value and the second index value acquired by the acquisition part <NUM> from the target device <NUM>, and generates a regression prediction model, which is a method of machine learning, for the collected first index value and second index value. From the result of machine learning, the reference generation part <NUM> sets a reference curve on a two-dimensional plane with the first index and the second index being axes. The reference curve represents a relationship between the first index value and the second index value in a normal state of the target device <NUM>. The abnormality detecting device <NUM> may also acquire information indicating the reference curve in advance from outside.

The first index value associated with the first index is a value related to the stage of the operation of the target device <NUM>, and when the target device <NUM> is a servo press machine, the first index value is, for example, a value indicating a degree of progress in a series of press operation (a value indicating a percentage at which the process has progressed from the start to the end of a series of press operation, e.g., the position of the servomotor <NUM> or the position of the press tool <NUM>), or an elapsed time from the start of a series of press operation. The second index value associated with the second index is a value related to a load of the target device <NUM>.

The first index value and the second index value are not limited to a value indicating a degree of progress in a series of press operation and a value related to a load (a load of the load cell) of the target device <NUM>, but values such as the position, torque, and speed of the servomotor, and measured values associated with the target device <NUM> may be appropriately selected and used.

The scale normalization part <NUM> individually normalizes each of the scale of the first index and the scale of the second index on the two-dimensional plane with the first index and the second index being axes. Based on the result of machine learning of the first index value and the second index value by the reference generation part <NUM>, the scale normalization part <NUM> normalizes the scale of the first index and the scale of the second index. A reference curve may be set for the two-dimensional plane with the normalized scales. In this manner, by appropriately normalizing each index value and performing the abnormality determination, it is possible to appropriately perform the abnormality determination.

<FIG> is a graph showing the first index value and the second index value in the press operation performed multiple times collected from the target device <NUM> which is a servo press machine, in which the first index (position) is the horizontal axis, and the second index (load) is the vertical axis.

<FIG> a graph showing a reference curve set by the reference generation part <NUM> using the scales normalized by the scale normalization part <NUM> using the collected data shown in <FIG>. As shown in <FIG>, the reference curve set according to machine learning by the reference generation part <NUM> is, for example, a line graph. In the reference curve, the horizontal axis shows a value (e.g., position) related to the stage of the operation of the target device <NUM>, which is the first index value associated with the first index normalized by the scale normalization part <NUM>, and the vertical axis shows a value related to the load, which is the second index value associated with the second index normalized by the scale normalization part <NUM>. The reference curve may be, for example, a correlation diagram between the position of the servomotor and the load applied to the press tool, or a correlation diagram between the speed of the servomotor and the torque of the servomotor.

The reference curve is, for example, a line graph which is a regression prediction model f(x) represented by (Formula <NUM>) below. yideal indicates the second index value on the reference curve. xnorm indicates the normalized first index value. <NUM>] <MAT>.

The abnormality detecting part <NUM> detects an abnormality of the target device <NUM> based on a distance from the reference curve to a point indicated by the first index value and the second index value on the two-dimensional plane with the first index and the second index being axes.

<FIG> is a view showing parameters related to the first index value associated with the first index and the second index value associated with the second index on the reference curve of the regression prediction model f(x). The horizontal axis is the first index (x) indicating the stage of the operation, and the vertical axis is the second index (y).

The scale normalization part <NUM> first obtains xnorm and ynorm obtained by normalizing a measured value xact of the first index value and a measured value yact of the second index value acquired by the acquisition part <NUM>, using (Formula <NUM>) and (Formula <NUM>) below. <NUM>] <MAT>
[Math. <NUM>] <MAT>.

Herein, xmin and ymin may respectively be minimum values of the measured values xact and yact of the first index value and the second index value stored in the storage part <NUM>, or minimum values of the first index value and the second index value on the reference curve. Further, xmax and ymax may respectively be maximum values of the measured values xact and yact of the first index value and the second index value stored in the storage part <NUM>, or maximum values of the first index value and the second index value on the reference curve. Normalization is performed to match the scale of the first index and the scale of the second index.

Subsequently, the abnormality detecting part <NUM> obtains yideal for the normalized first index value xnorm using (Formula <NUM>). The abnormality detecting part <NUM> obtains xideal for the normalized second index value ynorm using (Formula <NUM>) below. xideal indicates the first index value on the reference curve. g(y) is the inverse function of f(x). yideal indicates the second index value (ideal value of the second index) when the first index value is xnorm in the case where the target device <NUM> is normal. xideal indicates the first index value (ideal value of the first index) when the second index value is ynorm in the case where the target device <NUM> is normal. <NUM>] <MAT>.

Next, the abnormality detecting part <NUM> obtains Δx and Δy which are deviations of the normalized first index value xnorm and second index value ynorm from their respective ideal values xideal and yideal using (Formula <NUM>) and (Formula <NUM>) below. <NUM>] <MAT>
[Math. <NUM>] <MAT>.

The abnormality detecting part <NUM> calculates a distance Δh from the reference curve to the point indicated by the first index value and the second index value according to (Formula <NUM>) below using the deviations Δx and Δy of the first index value and the second index value deviating from the ideal values xideal and yideal. <NUM>] <MAT>.

In addition, for example, when Δy is a predetermined value ε or less, or when |Δy| is a predetermined value ε or less, Δh = <NUM> and the error from the reference curve may be ignored.

In this manner, the abnormality detecting part <NUM> detects an abnormality of the target device <NUM> based on the distance Δh from the predetermined reference curve to the point indicated by the first index value and the second index value on the two-dimensional plane with the first index and the second index being axes. Accordingly, even at a process position where the slope changes sharply on the reference curve, the abnormality detecting device <NUM> can appropriately perform abnormality detection, and can suppress determination that there is an abnormality even though an abnormality does not occur.

<FIG> is a graph showing a reference curve based on the inverse regression prediction model g(y). The vertical axis is the first index (x) indicating the stage of the operation, and the horizontal axis is the second index (y). As shown in <FIG>, the reference curve is not limited to the configuration indicated by the regression prediction model f(x), but may also have a configuration indicated by the inverse regression prediction model g(y). Even when the reference curve is indicated by the inverse regression prediction model g(y), the abnormality detecting part <NUM> can calculate the distance Δh from the reference curve to the point indicated by the first index value and the second index value according to the above method using (Formula <NUM>) to (Formula <NUM>).

<FIG> is a view showing a time change of the distance Δh. The horizontal axis is the time (first index), and the vertical axis is the distance Δh. As shown in <FIG>, the abnormality detecting part <NUM> assigns a positive sign to the distance Δh when the second index value (load) is on the large side with respect to the reference curve. Further, the abnormality detecting part <NUM> assigns a negative sign to the distance Δh when the second index value is on the small side with respect to the reference curve. In this manner, the abnormality detecting part <NUM> switches the sign (positive or negative) assigned to the distance Δh according to whether the second index value is on the large side or the small side with respect to the reference curve. Based on whether the distance Δh assigned with the positive/negative sign is within a predetermined normal range (normal range), the abnormality detecting part <NUM> determines whether an abnormality occurs in the target device <NUM>. The abnormality detecting part <NUM> may also perform a determination on an abnormality based on an absolute value of the distance Δh without distinguishing between the positive and negative.

In addition, depending on the type of the target device <NUM>, for example, when the second index value (load) is on the large side with respect to the reference curve, it may be considered that the possibility of abnormality is higher than when the second index value is on the small side with respect to the reference curve. Therefore, according to the sign (positive or negative) assigned to the distance Δh, the abnormality detecting part <NUM> may have a different threshold value of the distance Δh used for determining whether an abnormality occurs in the target device <NUM>. In other words, the threshold value on the positive side and the absolute value of the threshold value on the negative side that indicate the boundary of the normal range associated with Δh may be different.

In this manner, instead of the absolute value of the distance Δh from the reference curve to the point indicated by the first index value and the second index value, the abnormality detecting part <NUM> may select an appropriate threshold value according to the distance Δh assigned with the positive/negative sign according to whether the second index value is on the side with a larger load or on the side with a smaller load with respect to the reference curve, to determine whether an abnormality occurs in the target device <NUM>. Therefore, according to the configuration of the abnormality detecting part <NUM>, it is possible to suppress an erroneous determination that an abnormality occurs even though an abnormality does not occur in the target device <NUM>, and it is possible to appropriately perform an abnormality determination.

<FIG> is a flowchart showing a flow of a reference curve generation process by the abnormality detecting device <NUM>.

In the reference curve generation process, the control part <NUM> of the abnormality detecting device <NUM> first acquires, by the function of the acquisition part <NUM>, a first index value associated with a first index and a second index value associated with a second index from the target device <NUM> in the normal state via the communication part <NUM> (step S1).

The control part <NUM> stores the first index value associated with the first index and the second index value associated with the second index acquired by the function of the acquisition part <NUM> to the storage part <NUM> (step S2).

The control part <NUM> determines whether a series of machining operation such as a press-fitting process and a caulking process performed by the target device <NUM> has been completed (step S3). When the control part <NUM> determines that the series of machining operation performed by the target device <NUM> has been completed (YES in step S3), the process proceeds to step S4. When it is determined that the series of machining operation performed by the target device <NUM> has not been completed (NO in step S3), returning to step S1, the control part <NUM> continues the collection of the first index value associated with the first index and the second index value associated with the second index.

By the function of the reference generation part <NUM>, the control part <NUM> performs machine learning to generate a regression model for the first index value associated with the first index and the second index value associated with the second index related to the series of machining operation performed by the target device <NUM> that are stored in the storage part <NUM> (step S4).

With reference to the result of machine learning by the reference generation part <NUM>, the control part <NUM> normalizes, by the function of the scale normalization part <NUM>, the scales of the first index and the second index. Based on the scales normalized by the scale normalization part <NUM> and the result of machine learning, the reference generation part <NUM> sets a reference curve (normalized reference curve) associated with a series (one stroke from the start of operation to the completion of operation) of machining operation performed by the target device <NUM> (step S5).

The control part <NUM> stores the set reference curve to the storage part <NUM>. Further, the control part <NUM> presets a normal range associated with Δh and stores it to the storage part <NUM>. The normal range may also be inputted by a user.

With reference to the reference curve set in step S5, the abnormality detecting device <NUM> monitors, by the function of the abnormality detecting part <NUM>, the machining operation performed by the target device <NUM>, and detects when an abnormality occurs in the machining operation performed by the target device <NUM>.

<FIG> is a flowchart showing a flow of an abnormality detecting process by the abnormality detecting device <NUM>.

The abnormality detecting device <NUM> initializes an abnormality detection flag (Flag) (Flag = <NUM>) at a start timing of a series of operation in the machining operation of the target device <NUM> (step S11).

During the operation of the target device <NUM>, the control part <NUM> of the abnormality detecting device <NUM> acquires, by the function of the acquisition part <NUM>, a first index value associated with the first index and a second index value associated with the second index from the target device <NUM> via the communication part <NUM> (step S12).

The control part <NUM> normalizes, by the function of the scale normalization part <NUM>, each of the first index value and the second index value acquired by the acquisition part <NUM> using (Formula <NUM>) and (Formula <NUM>) above (step S13). Herein, the scale normalization part <NUM> of the control part <NUM> normalizes each of the first index value and the second index value by using the maximum value and the minimum value on the reference curve before normalization.

The control part <NUM> calculates, by the function of the abnormality detecting part <NUM>, a deviation Δx of the normalized index value xnorm of the first index deviating from an ideal value xideal, and a deviation Δy of the normalized index value ynorm of the second index deviating from an ideal value yideal, using (Formula <NUM>) and (Formula <NUM>) above (step S14).

The abnormality detecting part <NUM> calculates a distance Δh from the reference curve to a point indicated by the first index value and the second index value on the two-dimensional plane according to (Formula <NUM>) above using the deviation Δx of the first index value and the deviation Δy of the second index value (step S15).

The control part <NUM> stores the distance Δh to the storage part <NUM>.

Subsequently, the control part <NUM> determines, by the function of the abnormality detecting part <NUM>, whether the distance Δh is within a predetermined normal range corresponding to the sign (positive or negative) (step S16). When the control part <NUM> determines by the abnormality detecting part <NUM> that the distance Δh is within the normal range (YES in step S16), the process proceeds to step S18. When the control part <NUM> determines by the abnormality detecting part <NUM> that the distance Δh is outside the normal range (NO in step S16), the process proceeds to step S17.

When the distance Δh is outside the normal range, the control part <NUM> detects that an abnormality occurs in the target device <NUM> and counts up the abnormality detection flag (Flag) (Flag = Flag + <NUM>), and the process proceeds to step S18 (step S17). "Flag" represents an integration period (integration section) in which the distance Δh is outside the normal range.

When it is determined in step S16 that the distance Δh is within the normal range, the control part <NUM> determines whether the entirety of the series of operation performed by the target device <NUM> has been completed (step S18). The control part <NUM> may also determine whether the entirety of the series of operation performed by the target device <NUM> has been completed with reference to, for example, the index value of the first index, the index value of the second index, and the reference curve. Further, the control part <NUM> may also acquire information as to whether the entirety of the series of operation has been completed from, for example, the target device <NUM> via the acquisition part <NUM>.

When the control part <NUM> determines that the entirety of the series of operation performed by the target device <NUM> has been completed (YES in step S18), the process proceeds to step S19. When the control part <NUM> determines that the entirety of the series of operation performed by the target device <NUM> has not been completed (NO in step S18), returning to step S12, the process continues. Accordingly, during the series of operation of the target device <NUM>, for example, at a predetermined time interval, the control part <NUM> continues the acquisition of the first index value associated with the first index and the second index value associated with the second index from the target device <NUM> via the communication part <NUM>.

When the entirety of the series of operation performed by the target device <NUM> is completed, the control part <NUM> calculates, by the function of the abnormality detecting part <NUM>, a feature amount of Δh in the entirety of the series of operation (step S19). The abnormality detecting part <NUM> may calculate a mean, a variance, or a standard deviation of the distance Δh calculated during the series of operation performed by the target device <NUM> as the feature amount. Further, the abnormality detecting part <NUM> may also calculate a feature amount in a frequency distribution of the distance Δh calculated during the series of machining operation performed by the target device <NUM>. Herein, the frequency distribution of the distance Δh during the machining operation of the target device <NUM> may be a histogram with the distance Δh during the machining operation taken as a bin, and the abnormality detecting part <NUM> may also calculate a kurtosis or a skewness in the histogram as the feature amount in the frequency distribution of the distance Δh during the machining operation.

Based on the feature amount of the distance Δh during the series of operation performed by the target device <NUM>, the control part <NUM> determines, by the function of the abnormality detecting part <NUM>, whether or not an abnormality occurs in the entirety of the series of operation of the target device <NUM> (step S20).

For example, if the kurtosis in the histogram with the distance Δh taken as a bin is smaller than a predetermined threshold value, the abnormality detecting part <NUM> may determine that an abnormality occurs in the entirety of the series of operation of the target device <NUM>. Further, if the skewness of the histogram with the distance Δh taken as a bin is larger than a predetermined threshold value, the abnormality detecting part <NUM> may determine that an abnormality occurs in the entirety of the series of operation of the target device <NUM>. Further, if the mean, the variance, or the standard deviation of the distance Δh is larger than a predetermined threshold value, the abnormality detecting part <NUM> may determine that an abnormality occurs in the entirety of the series of operation of the target device <NUM>.

Further, based on the feature amount of the distance Δh and the count amount of the abnormality detection flag, the abnormality detecting part <NUM> may determine whether or not an abnormality occurs in the entirety of the series of operation of the target device <NUM>. Even in the case where an abnormality of the target device <NUM> is suspected from the feature amount of the distance Δh, the abnormality detecting part <NUM> may determine that an abnormality does not occur in the series of operation of the target device <NUM> if the count amount (Flag) of the abnormality detection flag remains at the initial value (<NUM>). Further, even in the case where it is not determined that an abnormality occurs in the target device <NUM> from the feature amount of the distance Δh, the abnormality detecting part <NUM> may determine that an abnormality occurs in the series of operation of the target device <NUM> if the count amount (Flag) of the abnormality detection flag is larger than a predetermined value.

<FIG> are views showing graphs corresponding to the first index value associated with the first index and the second index value associated with the second index collected when a series of operation is performed multiple times in the target device <NUM>.

Graphs 61a, 62a, and 63a of <FIG> are motion profiles showing the process of the operation of the target device <NUM> by the first index value (horizontal axis) and the second index value (vertical axis). Each of graphs 61b, 62b, and 63b of <FIG> is a graph obtained by respectively normalizing the first index value and the second index value of the graphs 61a, 62a, and 63a. Each of graphs 61c, 62c, and 63c of <FIG> and <FIG> is a graph (the horizontal axis is the first index) showing a distribution of the distance Δh from the reference curve to the point indicated by the normalized first index value and second index value respectively shown in the graphs 61b, 62b and 63b. Each of graphs 61d, 62d, and 63d of <FIG> is a histogram (the vertical axis is the distance Δh, and the horizontal axis is the frequency) in which the distance Δh respectively shown in the graphs 61c, 62c, and 63c is taken as a bin.

The example shown in the graphs 61a, 61b, 61c, and 61d of <FIG> and <FIG> shows a case where it is determined that there is no abnormality as a result of performing an abnormality determination based on the distance Δh as well as performing an abnormality determination based on the feature amount in the frequency distribution of the distance Δh for the operation of the target device <NUM>. As shown in the graphs 61c and 61d, when the standard deviation in the frequency distribution of the distance Δh is within a predetermined range, and when the kurtosis and the skewness of the histogram with the distance Δh taken as a bin are within predetermined ranges, the abnormality detecting part <NUM> does not detect an abnormality in the operation performed by the target device <NUM> (determining that there is no abnormality).

The example shown in the graphs 62a, 62b, 62c, and 62d of <FIG> and <FIG> shows a case where the distance Δh sometimes falls outside the range of the threshold value on the negative side in the process of the operation of the target device <NUM>. As shown in the graphs 62c and 62d, even if the distance Δh sometimes falls outside the range of the threshold value on the negative side, the standard deviation in the frequency distribution of the distance Δh may be within the predetermined range. Even in such a case, the abnormality detecting part <NUM> can detect an abnormality in the operation performed by the target device <NUM> based on whether the kurtosis or the skewness of the histogram with the distance Δh taken as a bin is within the predetermined range.

The abnormality detecting part <NUM> may also be configured to detect an abnormality of the target device <NUM> at a time point when detecting that the distance Δh falls outside the range of the threshold value on the negative side, or may also be configured to detect an abnormality of the target device <NUM> based on the standard deviation in the frequency distribution of the distance Δh in the entirety of the series of operation and the kurtosis or the skewness of the histogram with the distance Δh taken as a bin.

The example shown in the graphs 63a, 63b, 63c, and 63d of <FIG> and <FIG> show a case where the distance Δh sometimes falls outside the range of the threshold value on the positive side and the negative side in the process of the operation of the target device <NUM>. As shown in the graphs 63c and 63d, even if the distance Δh falls outside the range of the threshold value on the positive side and the negative side, the standard deviation in the frequency distribution of the distance Δh may be within the predetermined range. Even in such a case, the abnormality detecting part <NUM> can detect an abnormality in the operation performed by the target device <NUM> based on whether the kurtosis or the skewness of the histogram with the distance Δh taken as a bin is within the predetermined range.

<FIG> shows an example of a histogram (the vertical axis is the distance Δh, and the horizontal axis is the frequency) of the distance Δh in the target device <NUM> in which an abnormality occurs. In the example shown in <FIG>, the mean in the frequency distribution of the distance Δh is well below <NUM> (the mean is less than the threshold value on the negative side). The abnormality detecting part <NUM> may detect an abnormality of the target device <NUM> according to whether a statistical value such as the mean in the frequency distribution of the distance Δh is within a normal range.

<FIG> shows another example of a histogram (the vertical axis is the distance Δh, and the horizontal axis is the frequency) of the distance Δh in the target device <NUM> in which an abnormality occurs. In the example shown in <FIG>, the standard deviation in the frequency distribution of the distance Δh is larger than the threshold value. The abnormality detecting part <NUM> may detect an abnormality of the target device <NUM> according to whether a statistical value such as the standard deviation in the frequency distribution of the distance Δh is within a normal range.

In the above description, the case where the target device <NUM> is a press machine driven by a servomotor as a power source has been taken as an example. However, the target device <NUM> is not limited to a press machine, but may be any device driven by a servomotor as a power source, and it is also possible to appropriately detect an abnormality by the abnormality detecting device <NUM>. Further, the abnormality detecting process by the abnormality detecting device <NUM> is applicable not only to a servomotor but also to a stepping motor and other devices driven by a simple motor as a power source. Further, the abnormality detecting process by the abnormality detecting device <NUM> is applicable not only to a motor but also to a device driven by a general actuator such as a hydraulic or pneumatic actuator as a power source.

<FIG> is a view showing a first index value associated with a first index, a second index value associated with a second index, and a reference curve in a case where the target device <NUM> is a device other than a press machine. The upper graph of <FIG> is a view showing measured values xact and yact of the rotation speed of the servomotor which is the first index value associated with the first index and the torque of the servomotor which is the second index value associated with the second index in a series of operation of the target device <NUM>, with the horizontal axis being time. The lower graph of <FIG> a graph showing normalized first index value xnorm and second index value ynorm, and the reference curve on a two-dimensional plane with the first index being the x-axis and the second index being the y-axis.

In this manner, with the target device <NUM> being any device driven by a servomotor as a power source, it is possible to detect an abnormality based on the distance Δh between the reference curve and the point indicated by the normalized first index value xnorm and second index value ynorm on the two-dimensional plane with the first index being the x-axis and the second index being the y-axis.

The control block (specifically, the acquisition part <NUM>, the reference generation part <NUM>, the scale normalization part <NUM>, and the abnormality detecting part <NUM>) of the abnormality detecting device <NUM> may be realized by a logic circuit (hardware) formed in an integrated circuit (IC chip) or the like, or may be realized by software.

In the latter case, the abnormality detecting device <NUM> includes a computer executing commands of a program which is software realizing each function. The computer includes, for example, one or more processors and a computer-readable recording medium that stores the program. Then, in the computer, the objective of the present invention is achieved by the processor reading the program from the recording medium and executing the program. As the processor, for example, a CPU (central processing unit) may be used. As the recording medium, a "non-transitory tangible medium" such as a ROM (read only memory) and the like, a tape, a disk, a card, a semiconductor memory, a programmable logic circuit, etc. may be used. Further, a RAM (random access memory) or the like for developing the program may be further provided. Further, the program may be supplied to the computer via an arbitrary transmission medium (communication network, broadcast wave, etc.) capable of transmitting the program. One aspect of the present invention may also be realized in the form of a data signal embedded in a carrier wave, in which the program is embodied by electronic transmission.

To solve the above problems, an abnormality detecting device according to an aspect of the present invention is an abnormality detecting device detecting an abnormality of a target device, and includes an acquisition part and an abnormality detecting part. The acquisition part acquires a first index value associated with a first index and a second index value associated with a second index in an operation of the target device. The abnormality detecting part detects an abnormality of the target device based on a distance from a predetermined reference curve to a point indicated by the first index value and the second index value on a two-dimensional plane with the first index and the second index being axes.

Further, to solve the above problems, an abnormality detecting method according to an aspect of the present invention is an abnormality detecting method executed in an abnormality detecting device detecting an abnormality of a target device, and includes: an index value acquisition step of acquiring a first index value associated with a first index and a second index value associated with a second index in an operation of the target device; and an abnormality detecting step of detecting an abnormality of the target device based on a distance from a predetermined reference curve to a point indicated by the first index value and the second index value on a two-dimensional plane with the first index and the second index being axes.

According to the above configuration, it is possible to appropriately perform an abnormality determination in the operation process of the target device.

Further, in the abnormality detecting device according to an aspect of the present invention, the first index value is a value related to a stage of the operation of the target device, and the second index value is a value related to a load of the operation of the target device.

According to the above configuration, it is possible to appropriately perform an abnormality determination at each stage of the operation of the target device.

Further, the abnormality detecting device according to an aspect of the present invention includes a scale normalization part that individually normalizes each of a scale of the first index value on the two-dimensional plane and a scale of the second index value on the two-dimensional plane.

According to the above configuration, since each index value is appropriately normalized to perform the abnormality determination, it is possible to appropriately perform the abnormality determination.

Further, in the abnormality detecting device according to an aspect of the present invention, the abnormality detecting part switches a sign of positive and negative assigned to the distance according to whether the second index value is on a side with a large load or a side with a small load with respect to the reference curve, and detects an abnormality of the target device based on the distance assigned with the sign.

According to the above configuration, it is also possible to appropriately perform an abnormality determination in a case where there is no abnormality even if the index value is smaller than the reference curve to some extent, but there is a high possibility of abnormality when the index value is higher than the reference curve.

Further, in the abnormality detecting device according to an aspect of the present invention, the abnormality detecting part changes a threshold value of the distance from the reference curve, which is a threshold value used for determination on presence or absence of an abnormality of the target device, according to the sign assigned to the distance.

According to the above configuration, it is possible to appropriately perform an abnormality determination even if the determination on presence or absence of an abnormality differs depending on the sign (positive or negative) of the distance.

Further, in the abnormality detecting device according to an aspect of the present invention, the abnormality detecting part detects an abnormality of the target device based on a standard deviation of the distance during a machining operation.

Further, in the abnormality detecting device according to an aspect of the present invention, the abnormality detecting part detects an abnormality of the target device based on a feature amount in a frequency distribution of the distance during a machining operation.

Further, in the abnormality detecting device according to an aspect of the present invention, the feature amount in the frequency distribution is a kurtosis in a histogram in which the distance during the machining operation is taken as a bin.

Further, in the abnormality detecting device according to an aspect of the present invention, the feature amount in the frequency distribution is a skewness in a histogram in which the distance during the machining operation is taken as a bin.

Claim 1:
An abnormality detecting device (<NUM>) configured to detect an abnormality of a target device, the abnormality detecting device (<NUM>) comprising:
an acquisition part (<NUM>) configured to acquire a first index value associated with a first index and a second index value associated with a second index in an operation of the target device; and
an abnormality detecting part (<NUM>) configured to acquire a predetermined reference curve representing a relationship between the first index value and the second index value in a normal state of the target device on a two-dimensional plane with the first index and the second index being axes, to acquire a first ideal value as the value on the first index of the predetermined reference curve for the second index value on the second index, to acquire a second ideal value as the value on the second index of the predetermined reference curve for the first index value on the first index, to calculate a distance from the predetermined reference curve to a point indicated by the first index value and the second index value based on the first index value, the second index value, the first ideal value, and the second ideal value, and to detect an abnormality of the target device based on the distance.