WO2023188493A1 - 誤差解析方法、誤差解析装置およびプログラム - Google Patents

誤差解析方法、誤差解析装置およびプログラム Download PDF

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Publication number
WO2023188493A1
WO2023188493A1 PCT/JP2022/039832 JP2022039832W WO2023188493A1 WO 2023188493 A1 WO2023188493 A1 WO 2023188493A1 JP 2022039832 W JP2022039832 W JP 2022039832W WO 2023188493 A1 WO2023188493 A1 WO 2023188493A1
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Prior art keywords
industrial equipment
thermal image
model
error
error analysis
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PCT/JP2022/039832
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English (en)
French (fr)
Japanese (ja)
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幸嗣 小畑
サヒム 山浦
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Panasonic Intellectual Property Management Co Ltd
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Panasonic Intellectual Property Management Co Ltd
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Priority to CN202280093603.3A priority Critical patent/CN118872041A/zh
Priority to JP2024511186A priority patent/JPWO2023188493A1/ja
Priority to US18/848,423 priority patent/US20250208613A1/en
Publication of WO2023188493A1 publication Critical patent/WO2023188493A1/ja
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
    • G01J5/48—Thermography; Techniques using wholly visual means
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
    • B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
    • B23Q15/00—Automatic control or regulation of feed movement, cutting velocity or position of tool or work
    • B23Q15/007—Automatic control or regulation of feed movement, cutting velocity or position of tool or work while the tool acts upon the workpiece
    • B23Q15/18—Compensation of tool-deflection due to temperature or force
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Program-control systems
    • G05B19/02—Program-control systems electric
    • G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of program data in numerical form
    • G05B19/404—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of program data in numerical form characterised by control arrangements for compensation, e.g. for backlash, overshoot, tool offset, tool wear, temperature, machine construction errors, load, inertia
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Program-control systems
    • G05B19/02—Program-control systems electric
    • G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of program data in numerical form
    • G05B19/4155—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of program data in numerical form characterised by program execution, i.e. part program or machine function execution, e.g. selection of a program
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0221—Preprocessing measurements, e.g. data collection rate adjustment; Standardization of measurements; Time series or signal analysis, e.g. frequency analysis or wavelets; Trustworthiness of measurements; Indexes therefor; Measurements using easily measured parameters to estimate parameters difficult to measure; Virtual sensor creation; De-noising; Sensor fusion; Unconventional preprocessing inherently present in specific fault detection methods like PCA-based methods
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
    • G05B23/024—Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
    • H—ELECTRICITY
    • H10—SEMICONDUCTOR DEVICES; ELECTRIC SOLID-STATE DEVICES NOT OTHERWISE PROVIDED FOR
    • H10P—GENERIC PROCESSES OR APPARATUS FOR THE MANUFACTURE OR TREATMENT OF DEVICES COVERED BY CLASS H10
    • H10P72/00—Handling or holding of wafers, substrates or devices during manufacture or treatment thereof
    • H10P72/50—Handling or holding of wafers, substrates or devices during manufacture or treatment thereof for positioning, orientation or alignment
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
    • G01J2005/0077—Imaging

Definitions

  • the present disclosure relates to an error analysis method, an error analysis device, and a program.
  • Patent Document 1 Even if the technology proposed in Patent Document 1 is used, it is not known which part of industrial equipment generates heat or thermal deformation greatly contributes to accuracy. Therefore, it may not be possible to measure the temperature of mechanical elements, which greatly contributes to accuracy. In other words, there is a problem in that accuracy cannot be improved because a correction formula cannot be calculated from the temperature of a mechanical element that greatly contributes to accuracy.
  • the present disclosure has been made in view of the above-mentioned circumstances, and aims to provide an error analysis method, an error analysis device, and a program that can more accurately determine the location of heat generation that affects errors and improve correction accuracy. do.
  • an error analysis method includes an acquisition step of acquiring a thermal image and an error during operation of industrial equipment, and a model using the thermal image and the error. Perform machine learning to estimate a correction amount of the industrial equipment from the thermal image, and use the degree of contribution identified by a predetermined method to determine a part of the industrial equipment that appears in the thermal image that affects accuracy. and a determining step, and in the acquiring step, the temperature of the part determined in the determining step is acquired in order to calculate a correction amount of the industrial equipment.
  • FIG. 1 is a block diagram showing an example of the configuration of an error analysis device according to an embodiment.
  • FIG. 2 is a diagram conceptually showing how the industrial equipment in operation is photographed by a thermal camera according to the embodiment.
  • FIG. 3 is a diagram showing an example of time-series thermal images in the embodiment.
  • FIG. 4 is a block diagram showing an example of a detailed configuration of the determining section shown in FIG. 1.
  • FIG. 5 is a diagram conceptually showing a model subjected to machine learning by the learning processing unit in the embodiment.
  • FIG. 6A is a diagram showing feature amounts extracted by CNN of the model shown in FIG. 5.
  • FIG. 6B is a diagram for explaining the degree of contribution identified by Grad-CAM.
  • FIG. 1 is a block diagram showing an example of the configuration of an error analysis device according to an embodiment.
  • FIG. 2 is a diagram conceptually showing how the industrial equipment in operation is photographed by a thermal camera according to the embodiment.
  • FIG. 3 is a diagram showing an example of time-series
  • FIG. 6C is a diagram for explaining that a portion that has a large influence on the error has been identified.
  • FIG. 7 is a diagram illustrating an example in which parts that affect accuracy in industrial equipment are displayed in chronological order by the display unit according to the embodiment.
  • FIG. 8 is a flowchart showing error analysis processing of the error analysis device in the embodiment.
  • FIG. 9A is a diagram illustrating an example of a saliency map representing the degree of contribution identified using backpropagation.
  • FIG. 9B is a diagram showing an example of a region specified using the saliency map of FIG. 9A.
  • FIG. 10A is a diagram conceptually showing deconvolution network processing.
  • FIG. 10B is a diagram illustrating an example of a reconstructed image representing the degree of contribution identified using the deconvolution network.
  • FIG. 10C is a diagram illustrating an example of a region identified using the reconstructed image of FIG. 10B.
  • FIG. 11 is a diagram showing an example of Feature Importance calculated using
  • FIG. 1 is a block diagram showing an example of the configuration of an error analysis device 10 in this embodiment.
  • the error analysis device 10 is realized by a computer or the like using a model subjected to machine learning, and includes an acquisition unit 11 and a determination unit 12 as shown in FIG.
  • the error analysis device 10 analyzes parts that affect error (accuracy) in industrial equipment.
  • the error analysis device 10 will be described as further including the correction amount calculation section 13, but the present invention is not limited to this.
  • the error analysis device 10 does not need to include the correction amount calculation unit 13.
  • a description will be given of how the error analysis device 10 determines by analyzing a part of an industrial device that is a heat generating part that affects an error, and calculates a correction amount for the industrial device.
  • the industrial equipment is a mounting machine, and the above-mentioned accuracy may be mounting accuracy, or the industrial equipment is a machine tool, and the above-mentioned accuracy may be processing accuracy.
  • the acquisition unit 11 acquires thermal images and errors during operation of industrial equipment.
  • the acquisition unit 11 acquires a time-series thermal image during operation of the industrial equipment obtained by continuously capturing thermal images during operation of the industrial equipment for a predetermined period, and a time-series thermal image obtained during the operation of the industrial equipment. You may also obtain the error.
  • the thermal image may be a time-series thermal image or a single thermal image, as long as it shows the part that affects the error in the industrial equipment.
  • the thermal image and the error during operation of the industrial equipment 50 will be described as being stored in, for example, a storage device outside the error analysis device 10 before being acquired by the acquisition unit 11. .
  • FIG. 2 is a diagram conceptually showing how the industrial equipment 50 in operation according to the present embodiment is photographed by the thermal camera 60.
  • the thermal camera 60 may be a thermography device or any device that can photograph the heat distribution of the industrial equipment 50.
  • FIG. 3 is a diagram showing an example of a time-series thermal image in this embodiment.
  • FIG. 3 shows thermal images at times t0, t1, and t2 as an example.
  • the thermal image may be a three-dimensional thermal image, but is not limited to this, and may be a two-dimensional thermal image as long as it shows a part that affects errors in industrial equipment. Further, the three-dimensional thermal image may be composed of two-dimensional thermal images of the industrial equipment 50 taken from a plurality of viewpoints.
  • thermography or the like to obtain the state of heat generation and the final error of the target industrial equipment 50 in three dimensions and in time series, a thermal image of the industrial equipment 50 during operation can be obtained. and the error can be stored in a storage device or the like outside the error analysis device 10.
  • the acquisition unit 11 can acquire the temperature measured by the temperature sensor.
  • the determining unit 12 analyzes the relationship between the heat generating parts of the industrial equipment 50 and errors using AI (machine learning model), and determines parts of the industrial equipment 50 that affect accuracy. More specifically, the determining unit 12 first uses the thermal image acquired by the acquiring unit 11 and the error to cause the model to estimate a correction amount for minimizing the error of the industrial equipment 50 from the thermal image. Do machine learning. Subsequently, the determining unit 12 determines the portions of the industrial equipment 50 that appear in the thermal image that affect accuracy using the degree of contribution identified by a predetermined method.
  • the model may be, for example, a CNN (Convolution Neural Networks)-based neural network model including a convolution layer, or a model using a decision tree.
  • FIG. 4 is a block diagram showing an example of a detailed configuration of the determining unit 12 shown in FIG. 1.
  • the determining unit 12 includes a learning processing unit 121, a contribution specifying unit 122, an affected region determining unit 123, and a display unit 124, as shown in FIG.
  • the learning processing unit 121 performs machine learning processing on the model. More specifically, the learning processing unit 121 uses the thermal image acquired by the acquisition unit 11 and the error to perform machine learning that causes the model to estimate the correction amount of the industrial equipment 50 from the thermal image.
  • FIG. 5 is a diagram conceptually showing a model 1210 subjected to machine learning by the learning processing unit 121 in this embodiment.
  • a model 1210 shown in FIG. 5 is a CNN-based neural network model, and includes a CNN 1210a and an output layer 1210b.
  • the CNN 1210a can apply a filter such as a kernel to successfully obtain the spatial and temporal dependencies in the thermal image.
  • the output layer 1210b may be a fully connected layer, a FLAT layer, or the like as appropriate.
  • the learning processing unit 121 uses the thermal image and error acquired by the acquisition unit 11 to perform machine learning that causes the model 1210 to estimate the position correction amount of the industrial equipment 50 from the thermal image. .
  • the learning processing unit 121 causes one model 1210 to perform machine learning to estimate position correction amounts ⁇ x, ⁇ y, and ⁇ in the x direction, y direction, and ⁇ direction.
  • Models for estimating each of the x direction, y direction, and ⁇ direction may be prepared.
  • the learning processing unit 121 may perform machine learning on each of the three models.
  • the CNN 1210a undergoes machine learning to output an offset map (correction amount map) as a feature map from the thermal image acquired by the acquisition unit 11.
  • the contribution identification unit 122 uses a predetermined method to identify the contribution at the position of the thermal image acquired by the acquisition unit 11, which contributes to estimating the position correction amount.
  • a predetermined method for specifying the degree of contribution Grad-CAM (Gradient-weighted Class Activation Mapping) or the like can be used, for example.
  • the predetermined method is not limited to the method using Grad-CAM, but may also be a method using backpropagation or a method using a deconvolution network. If the model 1210 is a model using a decision tree, the predetermined method may be a method using Feature Importance.
  • the affected region determination unit 123 determines the region that affects accuracy in the industrial equipment 50 that appears in the thermal image, using the degree of contribution identified by a predetermined method. In other words, the affected part determination unit 123 uses the degree of contribution specified by the contribution degree identification unit 122 to determine the part that causes the error (displacement).
  • the parts of the industrial equipment 50 may be defined by CAD data or user specifications.
  • the affected part determining unit 123 uses the contribution specified by the contribution specifying unit 122 to identify the part that is the cause of the error (displacement) and is defined by CAD data or user specification. You can decide.
  • the affected part determining unit 123 may determine the part that causes the error (deviation) from the degree of contribution identified by the contribution identifying unit 122 using unsupervised segmentation or the like.
  • FIG. 6A is a diagram showing feature amounts (feature map) extracted by the CNN 1210a of the model 1210 shown in FIG. 5.
  • FIG. 6B is a diagram for explaining the degree of contribution identified by Grad-CAM.
  • Figure 6B (b) shows an example of a heat map representing the degree of contribution identified by Grad-CAM, and
  • Figure 6B (a) shows the industrial A device 50 is shown conceptually.
  • FIG. 6C is a diagram showing an example of a region specified using the heat map shown in FIG. 6B (b).
  • the contribution identification unit 122 uses the gradient information of the feature quantity output by the convolutional layer (CNN 1210a) of the model 1210 to determine the accuracy of the industrial equipment 50 reflected in the thermal image input to the model 1210.
  • the contribution identifying unit 122 can identify the contribution at the position of the thermal image acquired by the acquiring unit 11.
  • the affected part determination unit 123 can determine the part that affects accuracy in the industrial equipment 50 using the degree of contribution specified by the contribution degree identification unit 122.
  • the display section 124 displays the region determined by the affected region determining section 123. If the part determined by the affected part determination part 123 is a part of the industrial equipment 50 that appears in each of the time-series thermal images, the display unit 124 may display the determined parts in chronological order. However, they may be displayed one by one in chronological order. Note that the display unit 124 does not need to be included in the determining unit 12, and may be an external display or the like.
  • FIG. 7 is a diagram illustrating an example of a case where parts that affect accuracy in industrial equipment 50 are displayed in chronological order by display unit 124 in this embodiment.
  • hatched circle areas indicate areas that affect accuracy. This makes it possible to visualize temporal changes in heat generating locations and changes in heat generating locations that affect errors in the industrial equipment 50, making it possible to follow changes in locations that affect accuracy. Therefore, the temperature sensor can be accurately installed at a position where it can measure the temperature of a portion that affects the accuracy of the industrial equipment 50 and its surroundings.
  • the determining unit 12 can use AI to analyze the thermal image acquired by the acquiring unit 11 and find out which part's heat generation is affecting the accuracy.
  • correction amount calculation unit 13 obtains the temperature of the part determined by the determination unit 12 and calculates the correction amount of the industrial equipment 50.
  • a temperature sensor is installed at a position where it can measure the temperature of the region determined by the determination unit 12 and its surroundings.
  • the correction amount calculation section 13 acquires the temperature of the region from the acquisition section 11 .
  • the correction amount calculation unit 13 uses AI such as a model that has undergone machine learning to calculate a correction amount for correcting the error from the obtained temperature of the region.
  • the AI such as a model that has undergone machine learning may be a model that has been machine learned by the learning processing unit 121 described above, or may be a known model that has been trained.
  • the temperature sensor can be installed at the heat generating location (site) that affects the accuracy of the industrial equipment 50 as determined by the determining unit 12, and the temperature can be measured, so the correction amount calculating unit 13 can: A correction amount for correcting an error can be calculated with high accuracy from the measured temperature.
  • FIG. 8 is a flowchart showing error analysis processing by the error analysis device 10 in this embodiment.
  • FIG. 8 in order to simplify the explanation, an example will be described in which a single thermal image is acquired instead of a time-series thermal image and errors are analyzed.
  • the error analysis device 10 acquires a thermal image and an error during operation of the industrial equipment 50 (S1).
  • the error analysis device 10 performs machine learning of the model using the thermal image and the error acquired in step S1, and uses the degree of contribution specified by a predetermined method to analyze the industrial equipment 50 that appears in the thermal image.
  • the parts that affect accuracy are determined.
  • the model may be a CNN-based neural network model as described above, or a model using a decision tree or the like.
  • the error analysis device 10 obtains the temperature of the region determined in step S2, and calculates the correction amount for the industrial equipment 50 (S3). More specifically, by installing a temperature sensor so that the temperature of the part determined in step S2 can be measured, the error analysis device 10 can acquire the temperature of the part when the industrial equipment 50 is operating. I can do it. Thereby, the error analysis device 10 uses AI or the like to accurately calculate the correction amount of the industrial equipment 50 from the obtained temperature of the relevant part.
  • the operation in step S3 may not be an essential operation of the error analysis device 10. In that case, the error analysis device 10 may perform the acquisition step of step S1 after step S2. More specifically, the error analysis device 10 may obtain the temperature of the portion determined in step S2 in order to calculate the correction amount of the industrial equipment 50.
  • the error analysis device 10 it is possible to identify the degree of contribution that contributed to estimating the correction amount at the position of the acquired thermal image from the machine learning model.
  • the parts of the industrial equipment 50 that affect the error can be determined.
  • the temperature of the part of the industrial equipment 50 that affects the error can be measured, so by acquiring the temperature of the part, the correction amount of the industrial equipment can be calculated with high accuracy.
  • the error analysis method includes an acquisition step of acquiring a thermal image and an error during operation of the industrial equipment 50, and a model using the thermal image and the error.
  • the temperature of the part determined in the determination step is acquired in order to calculate the correction amount of the industrial equipment 50.
  • the degree of contribution to the machine-learned model it is possible to more accurately know the parts of the industrial equipment 50 that are heat-generating parts that affect errors, and to identify the parts of heat-generating parts that affect errors. Temperature can be obtained. Thereby, the correction amount of the industrial equipment 50 can be calculated with higher accuracy. In other words, it is possible to more accurately know the location of the heat that affects the error, and improve the correction accuracy.
  • the time-series thermal images during the operation of the industrial equipment 50 obtained by continuously capturing thermal images during the operation of the industrial equipment 50 and the time-series You may also obtain the error obtained in .
  • the model is a CNN-based model, and the degree of contribution identified by a predetermined method is determined by using the gradient information of the feature quantity output by the convolution layer of the model. It may be a heat map in which a portion that affects accuracy is calculated.
  • the model is a CNN-based model, and the contribution identified by a predetermined method is calculated based on the amount of gradient that each pixel receives with respect to the thermal image using backpropagation. It may also be a salience map.
  • the degree of contribution can be identified using the saliency map, so it is possible to more accurately know which part of the industrial equipment is the heat generating part that affects the error.
  • the model may be a CNN-based model
  • the predetermined method may be a method using a deconvolution network that reconstructs a thermal image that is an input image by activating an intermediate layer of the model. good.
  • the degree of contribution can be identified using a method that uses deconvolution, so it is possible to more accurately know which part of the industrial equipment is the heat generating part that affects the error.
  • the model may be a model using a decision tree
  • the predetermined method may be a method using Feature Importance calculated using the impurity of the model.
  • the industrial equipment 50 may be a mounting machine, or the industrial equipment 50 may be a machine tool.
  • the error analysis device includes an acquisition unit 11 that acquires a thermal image and an error during operation of the industrial device 50, and a model of the industrial device from the thermal image using the thermal image and the error. and a determination unit 12 that performs machine learning to estimate the correction amount of 50 and determines the part that affects accuracy in the industrial equipment 50 that appears in the thermal image using the degree of contribution identified by a predetermined method.
  • the section 12 acquires the temperature of the part determined by the determining section 12 in order to calculate the correction amount of the industrial equipment 50.
  • backpropagation is used as a predetermined method for identifying the degree of contribution.
  • FIG. 9A is a diagram showing an example of a saliency map representing the degree of contribution identified using backpropagation.
  • FIG. 9B is a diagram showing an example of a region specified using the saliency map of FIG. 9A.
  • Saliency maps are expressed as Saliency maps and are also called saliency maps.
  • the feature map extracted by CNN 1210a is obtained.
  • backpropagation is performed by setting the activation of layers other than the CNN layer that outputs the feature map to 0, and calculates the amount of gradient that each pixel of the input image receives.
  • the saliency map 1222 shown in FIG. 9A can be visualized (calculated) as a degree of contribution to the input image. From the saliency map 1222 shown in FIG. 9A, it can be determined that, for example, parts 50a and 50b of the industrial equipment 50 shown in FIG.
  • the contribution identification unit 122 applies backpropagation to the model 1210 to calculate the amount of gradient that each pixel receives with respect to the thermal image input to the model 1210. Based on the calculated amount of gradient, the contribution specifying unit 122 can calculate a saliency map 1222 representing a portion of the industrial equipment 50 that appears in the thermal image that affects accuracy as a contribution. In this way, the contribution identifying unit 122 can identify the contribution at the position of the thermal image acquired by the acquiring unit 11, and the affected area determining unit 123 can identify the contribution identified by the contribution identifying unit 122. The accuracy can be used to determine parts of the industrial equipment 50 that affect accuracy.
  • FIGS. 10A to 10C are diagrams for explaining a case where a deconvolution network is used as a predetermined method for specifying the degree of contribution.
  • FIG. 10A is a diagram conceptually showing deconvolution network processing.
  • FIG. 10B is a diagram illustrating an example of a reconstructed image representing the degree of contribution identified using the deconvolution network.
  • FIG. 10C is a diagram illustrating an example of a region identified using the reconstructed image of FIG. 10B.
  • a thermal image is given as an input image to the CNN-based model 1210 shown in FIG. 5, and the amount of correction is inferred. Get the map.
  • a deconvolution network is constructed to correspond to each layer of the CNN 1210a.
  • the input image is reconstructed by repeating processes such as deconvolution and unpooling on the feature map extracted by the CNN 1210a.
  • the hidden layers of the model 1210 are activated to reconstruct the input image in order to know which features (basically pixels) in the input image they are looking for.
  • the reconstructed image 1223 of the thermal image as shown in FIG. 10B can be visualized as a degree of contribution to the input image. Then, from the reconstructed image 1223 shown in FIG. 10B, it can be determined that, for example, parts 50a and 50b of the industrial equipment 50 shown in FIG. 10C are parts that have a large influence on errors (displacements). .
  • the contribution identification unit 122 activates the middle layer of the model 1210 and uses a deconvolution network that reconstructs the thermal image that is the input image to identify the parts that affect accuracy in the industrial equipment 50.
  • the contribution identifying unit 122 can identify the contribution at the position of the thermal image acquired by the acquiring unit 11, and the affected area determining unit 123 can identify the contribution identified by the contribution identifying unit 122.
  • the accuracy can be used to determine parts of the industrial equipment 50 that affect accuracy.
  • the model 1210 is a model using a decision tree
  • the degree of contribution can be identified by using Feature Importance.
  • the model using a decision tree is a model using RandomForest, Adaboost, Xgboost, lightGBM, etc.
  • a model using a decision tree is a model composed of a plurality of nodes in a tree structure. Each node branches data depending on a condition in a certain feature. By applying machine learning to a model using a decision tree, it is possible to classify data that most closely matches the conditions into the same set.
  • impurity is known as an index for determining whether each node in a decision tree has successfully created a conditional branch. It is also known that decision trees can visualize feature importance, which is a numerical representation of the degree of influence each explanatory variable has on the output result. Therefore, when performing machine learning on a model using a decision tree, it is possible to obtain Feature Importance, which represents the degree of contribution, by calculating how much each feature contributes to reducing weighted impurity.
  • FIG. 11 is a diagram showing an example of Feature Importance calculated using the ROI extracted from the thermal image.
  • ROI is Region of Interest (ROI). As shown in FIG. 11, it can be seen that ROI1 and ROI2 have a large influence on the error (shift).
  • the contribution specifying unit 122 calculates the contribution of Feature Importance using the impurity of the model.
  • the affected part determination unit 123 determines, for example, the parts of the industrial equipment 50 corresponding to ROI1 and ROI2 that affect the precision of the industrial equipment 50 using the Feature Importance representing the contribution identified by the contribution identification unit 122. Decide as a part.
  • the error analysis device 10 analyzes the parts of the industrial equipment 50 that are heat generating parts that affect the error, acquires the temperature of the parts determined by the analysis, and corrects the amount of the industrial equipment 50. Although the calculation has been described, the calculation is not limited to this. Errors in the industrial equipment 50 are caused not only by heat during operation but also by vibration during operation. For this reason, the error analysis device 10 may analyze parts of the industrial equipment that are subject to vibrations that affect errors, and may calculate the amount of correction of the industrial equipment for errors caused by vibration. This makes it possible to cope with deterioration in accuracy caused by vibration.
  • the error analysis method in this modification includes an acquisition step of acquiring data indicating a time-series vibration during operation of the industrial equipment 50 and an error after the time-series vibration; is used to perform machine learning that causes the model to estimate the correction amount of the industrial equipment 50 from the data, and using the degree of contribution identified by a predetermined method, determine the parts of the industrial equipment 50 in the data that affect accuracy.
  • data indicating the time-series vibration of the part determined in the determination step may be acquired in order to calculate the correction amount of the industrial equipment 50.
  • the correction amount is described as an example of the position correction amount for a portion such as an arm of the industrial equipment 50, but the present invention is not limited to this.
  • the correction amount may be a vibration frequency.
  • the error analysis device 10 may perform machine learning that causes the model to infer the vibration frequency from the vibration data.
  • the error analysis device 10 can analyze and determine the portions of the industrial equipment 50 that are affected by errors due to heat or vibration during operation.
  • correction amount in the error analysis method, etc. of the present disclosure is not limited to the vibration frequency or position correction amount, but may be RUL (Remaining Useful Life).
  • the present disclosure is not limited to the above embodiments.
  • other embodiments of the present disclosure may be implemented by arbitrarily combining the components described in this specification or by excluding some of the components.
  • the present disclosure also includes modifications obtained by making various modifications to the above-described embodiments that a person skilled in the art can think of without departing from the gist of the present disclosure, that is, the meaning indicated by the words described in the claims. It will be done.
  • the present disclosure further includes the following cases.
  • the above device is a computer system composed of a microprocessor, ROM, RAM, hard disk unit, display unit, keyboard, mouse, etc.
  • a computer program is stored in the RAM or hard disk unit.
  • Each device achieves its function by the microprocessor operating according to the computer program.
  • a computer program is configured by combining a plurality of instruction codes indicating instructions to a computer in order to achieve a predetermined function.
  • a system LSI is a super-multifunctional LSI manufactured by integrating multiple components onto a single chip, and specifically, it is a computer system that includes a microprocessor, ROM, RAM, etc. .
  • a computer program is stored in the RAM. The system LSI achieves its functions by the microprocessor operating according to the computer program.
  • Some or all of the components constituting the above device may be configured from an IC card or a single module that is removable from each device.
  • the IC card or the module is a computer system composed of a microprocessor, ROM, RAM, etc.
  • the IC card or the module may include the above-mentioned super multifunctional LSI.
  • the IC card or the module achieves its functions by the microprocessor operating according to a computer program. This IC card or this module may be tamper resistant.
  • the present disclosure may also be the method described above. Moreover, it may be a computer program that implements these methods by a computer, or it may be a digital signal composed of the computer program.
  • the present disclosure also provides a method for storing the computer program or the digital signal in a computer-readable recording medium, such as a flexible disk, hard disk, CD-ROM, MO, DVD, DVD-ROM, DVD-RAM, BD ( It may be recorded on a Blu-ray (registered trademark) Disc), a semiconductor memory, or the like.
  • the signal may be the digital signal recorded on these recording media.
  • the computer program or the digital signal may be transmitted via a telecommunication line, a wireless or wired communication line, a network typified by the Internet, data broadcasting, or the like.
  • the present disclosure also provides a computer system including a microprocessor and a memory, wherein the memory stores the computer program, and the microprocessor may operate according to the computer program.
  • the program or the digital signal may be executed by another independent computer system by recording the program or the digital signal on the recording medium and transferring the program, or by transferring the program or the digital signal via the network or the like. You may do so.
  • the present disclosure can be used for an error analysis method, an error analysis device, and a program, and particularly for error analysis of errors during operation of industrial equipment such as mounting machines or machine tools.

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PCT/JP2022/039832 2022-03-31 2022-10-26 誤差解析方法、誤差解析装置およびプログラム Ceased WO2023188493A1 (ja)

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