WO2023189107A1 - Estimation device and estimation method - Google Patents

Estimation device and estimation method Download PDF

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Publication number
WO2023189107A1
WO2023189107A1 PCT/JP2023/007264 JP2023007264W WO2023189107A1 WO 2023189107 A1 WO2023189107 A1 WO 2023189107A1 JP 2023007264 W JP2023007264 W JP 2023007264W WO 2023189107 A1 WO2023189107 A1 WO 2023189107A1
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state
feature amount
degree
contribution
target
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光樹 佃
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オムロン株式会社
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    • 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
    • B23Q17/00—Arrangements for observing, indicating or measuring on machine tools
    • B23Q17/09—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring cutting pressure or for determining cutting-tool condition, e.g. cutting ability, load on tool
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M13/00—Testing of machine parts
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M99/00—Subject matter not provided for in other groups of this subclass

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  • the present disclosure relates to an estimation device and an estimation method that are capable of estimating factors of a change in the state of a target.
  • Patent Document 1 discloses a cultivation device that estimates the amount of water contained in plants with high accuracy.
  • a method for capturing changes in the state of an object there is a method of setting a threshold value for the feature amount obtained from the object. With such a method, it is only possible to know whether the feature value exceeds a threshold value, and it may be difficult to estimate the cause of a change in the state of the object.
  • An object of the present disclosure is to provide an estimation device and an estimation method capable of estimating factors of a change in the state of a target.
  • An estimation device includes: an acquisition unit that acquires the feature amount obtained from the object in a first state of the object and the feature amount in a second state of the object different from the first state; a testing unit that tests a significant difference between the acquired feature amount in the first state and the acquired feature amount in the second state; a generation unit that generates a degree of contribution of the feature amount to a state change between the first state and the second state of the object from the tested significant difference; and an estimation unit that quantitatively estimates a factor of a state change between the first state and the second state of the target based on the generated contribution degree.
  • An estimation method includes: obtaining the feature amount obtained from the object in a first state of the object and the feature amount in a second state of the object different from the first state; testing a significant difference between the acquired feature amount in the first state and the acquired feature amount in the second state; Generating a degree of contribution of the feature amount to a change of the object from the first state to the second state from the tested significant difference; Based on the generated contribution degree, a factor of a state change between the first state and the second state of the target is quantitatively estimated.
  • estimation device and estimation method of the above aspect it is possible to quantitatively estimate the cause of a change in the state of the object.
  • FIG. 1 is a block diagram showing an estimation device according to an embodiment of the present disclosure.
  • FIG. 2 is a diagram showing an example of a feature amount acquired by an acquisition unit of the estimation device of FIG. 1 and an example of a test value indicating a significant difference tested by a test unit.
  • 2 is a diagram showing an example of a contribution degree generated by a generation unit of the estimation device of FIG. 1 and an example of a standard contribution degree acquired by an acquisition unit.
  • FIG. 2 is a flowchart for explaining an estimation method using the estimation device of FIG. 1.
  • an estimation device 1 includes an acquisition unit 10, a verification unit 20, a generation unit 30, and an estimation unit 40, and quantitatively evaluates the factors of a state change of a target. is configured to estimate.
  • Targets include machine tools that create workpieces into desired shapes, and equipment such as processing machines that use machine tools to process materials into desired shapes.
  • the target states include a normal state in which the device operates normally, and an abnormal state in which the device does not operate normally. For example, if the equipment is a machine tool that performs end mill processing, abnormal conditions include a state in which the end mill is worn and the machine tool cannot perform normal processing, or a state in which the machine tool is unable to perform processing normally due to lack of lubrication due to lack of cutting oil. Including the state where there is no.
  • the estimation device 1 includes, for example, a processor 2, a storage device 3, and a communication device 4.
  • Each of the acquisition unit 10, the verification unit 20, the generation unit 30, and the estimation unit 40 is realized, for example, by the processor 2 executing a predetermined program.
  • the processor 2 includes a CPU, MPU, GPU, DSP, FPGA, ASIC, etc.
  • the storage device 3 is composed of, for example, an internal recording medium or an external recording medium.
  • the internal recording medium includes nonvolatile memory and the like. External recording media include hard disks (HDD), solid state drives (SSD), optical disk devices, and the like.
  • the communication device 4 includes, for example, a communication circuit or a communication module for transmitting and receiving data to and from an external device such as a server.
  • the acquisition unit 10 is configured to acquire, for example via the communication device 4, a feature amount of the target in a first state and a feature amount of the target in a second state different from the first state.
  • the feature amount is a numerical value that quantitatively indicates the feature of the object.
  • the feature amount includes an effective value, an average value, a peak value, a harmonic content rate, etc. calculated from analog values of voltage and current of the target.
  • the feature amount may be calculated by an external device such as a server, or may be calculated by the estimation device 1.
  • the acquisition unit 10 is configured to acquire the standard contribution degree.
  • the standard contribution includes a contribution corresponding to each factor of a plurality of state changes that may occur in the target.
  • the reference degree of contribution includes a degree of contribution corresponding to a factor of a state change of the object from state X1 to state Y2, and a degree of contribution corresponding to a factor of a state change of object from state X1 to state Z2.
  • the standard contribution degree may be acquired from an external device such as a server, or may be acquired from the storage device 3 of the estimation device 1 in which the standard contribution degree is stored in advance.
  • the test unit 20 is configured to statistically test the significant difference between the acquired feature amount in the first state and the acquired feature amount in the second state.
  • the type of test is determined by the characteristics of the subject. For example, when the target is a device such as a processing machine, "Welch's t-test" is used for the following reasons. - Since the state of the device changes depending on the state change factor, it is not possible to obtain features from objects in the same state. - Feature amounts obtained from objects in two different states (for example, a normal state and an abnormal state) do not correspond to each other. ⁇ Features obtained from objects in two different states have different populations, and it cannot be assumed that the population variances are equal.
  • State X1 is an example of a first state
  • state X2 is an example of a second state.
  • the testing unit 20 tests the significant difference between the feature amount A in the obtained state X1 and the feature amount A in the obtained state A significant difference between the feature amount B and the feature amount C is tested, and a significant difference between the feature amount C in the acquired state X1 and the feature amount C in the acquired state X2 is tested.
  • the result of testing the significance of each feature amount is calculated as a test value.
  • the feature amount A is determined to be the feature amount that most easily detects the state change between the target states X1 and X2.
  • the generation unit 30 is configured to generate the degree of contribution of the feature amount to the state change between the first state and the second state of the object (hereinafter referred to as contribution degree) from the tested significant difference.
  • the degree of contribution is the degree to which the feature amount contributes to the discrimination between one state of the target and another state.
  • the generation unit 30 generates a degree of contribution from the test result of the test unit 20 using a plurality of feature quantities in the same order as the acquired standard contribution degree.
  • the generation section 30 generates the degree of contribution from the test values of the feature amounts A, B, and C.
  • the generated degree of contribution is represented, for example, by a straight line L1 (indicated by a broken line) connecting the test values of the feature amounts A, B, and C, as shown in FIG.
  • the estimation unit 40 is configured to quantitatively estimate the cause of the state change between the first state and the second state of the target based on the generated degree of contribution.
  • the estimation unit 40 is configured to quantitatively estimate the cause of the state change between the first state and the second state of the target from the generated contribution degree and the acquired reference contribution degree. has been done.
  • the standard contribution is the contribution corresponding to the cause of the state change from the target state X1 to the state Y2 (hereinafter referred to as standard contribution 1), and the contribution corresponding to the cause of the state change from the target state X1 to the state Z2. (hereinafter referred to as standard contribution 2).
  • the standard contribution degree 1 is represented by a straight line L2 (indicated by a dotted line) connecting the test values of the feature quantities A, B, and C
  • the standard contribution degree 2 is Suppose that it is represented by a straight line L3 (indicated by a solid line) connecting the test values of .
  • the estimating unit 40 compares the straight line L1 with the straight lines L2 and L3, and determines whether the factor corresponding to the standard contribution degree 2 represented by the straight line L3 having substantially the same slope as the straight line L1 is in the target state. It is estimated that this is the cause of the state change between X1 and X2.
  • the estimation unit 40 is configured to estimate the degree of transition for the entire state change from the ratio of the generated contribution degree to the acquired standard contribution degree. For example, if the generated contribution test values are (t A , t B , t C ), and the obtained standard contribution degree 2 test values are ( TA , T B , T C ), The transition degree for the entire state change is given by (t A +t B +t C )/(T A +T B +T C ).
  • t A and T A are test values obtained by testing the feature amount A
  • t B and T B are test values obtained by testing the feature amount B
  • t C and T C are the test values obtained by testing the feature amount B. This is the test value obtained by testing C.
  • the estimation method is implemented, for example, by the processor 2 executing a predetermined program.
  • the acquisition unit 10 acquires the feature amount of the first state of the target and the feature amount of the second state of the target from the target (step S1).
  • the testing unit 20 determines the significant difference between the acquired feature amount of the first state and the feature amount of the second state. Verify (step S2).
  • the generation unit 30 calculates the difference between the first state and the second state of the object based on the tested significant difference.
  • the degree of contribution of the feature amount to the state change is generated (step S3).
  • the estimation unit 40 compares the generated degree of contribution and the standard contribution and determines the first state and the second state of the target.
  • the factor of the state change between the second states is quantitatively estimated (step S4), and the estimation method ends.
  • the estimation device 1 can exhibit the following effects.
  • the estimation device 1 includes an acquisition section 10, a verification section 20, a generation section 30, and an estimation section 40.
  • the acquisition unit 10 acquires the feature amount obtained from the object in the first state of the object and the feature amount in the second state of the object different from the first state.
  • the testing unit 20 tests the significant difference between the acquired feature amount in the first state and the acquired feature amount in the second state.
  • the generation unit 30 generates the degree of contribution of the feature amount to the state change between the first state and the second state of the object from the tested significant difference.
  • the estimating unit 40 quantitatively estimates the cause of the state change between the first state and the second state of the target based on the generated degree of contribution. With such a configuration, it is possible to quantitatively estimate the cause of a change in the state of the object.
  • the target is a machine tool that performs end mill processing
  • the machine tool changes from a normal state to an abnormal state
  • whether the cause of this state change is wear of the end mill or lack of lubrication due to lack of cutting oil. It is possible to estimate whether
  • the acquisition unit 10 acquires a standard contribution degree that includes a contribution degree corresponding to each factor of a plurality of state changes that may occur in the target.
  • the estimation unit 40 compares the generated degree of contribution with the acquired reference degree of contribution, and quantitatively estimates the cause of the state change between the first state and the second state of the object. With such a configuration, it is possible to quantitatively estimate the cause of a change in the state of the object.
  • the estimation unit 40 estimates the degree of transition for the entire state change from the ratio of the generated contribution degree to the acquired standard contribution degree. With such a configuration, the degree of transition of the state change of the object can be quantified.
  • the estimation method of the present disclosure can exhibit the following effects.
  • the estimation method includes the following steps. With such a configuration, it is possible to quantitatively estimate the cause of a change in the state of the object. - Obtain the feature amount obtained from the object in the first state of the object and the feature amount in the second state different from the first state of the object. - Testing the significant difference between the acquired feature amount in the first state and the acquired feature amount in the second state. - Generate the degree of contribution of the feature amount to the change from the first state to the second state of the object from the tested significant difference. - Quantitatively estimate the cause of the state change between the first state and the second state of the target based on the generated contribution degree.
  • the estimation device 1 can also be configured as follows.
  • the type of feature amount to be acquired may or may not be set in advance. If the types of feature amounts to be acquired are not set in advance, for example, the acquisition unit 10 acquires all types of feature amounts that can be acquired, and based on the test results of the test unit 20, selects the features to be used for generating the degree of contribution. The type of amount may also be determined.
  • the estimation unit 40 quantitatively estimates the cause of the state change between the first state and the second state of the target based on the generated contribution, not only when using the standard contribution but also using other methods. It can be configured as possible.
  • the estimation device 1 includes: an acquisition unit 10 configured to acquire the feature amount obtained from the object in a first state of the object and the feature amount in a second state of the object different from the first state; a testing unit 20 configured to test a significant difference between the acquired feature amount in the first state and the acquired feature amount in the second state; a generation unit 30 configured to generate a degree of contribution of the feature amount to a state change between the first state and the second state of the object from the tested significant difference; The estimation unit 40 is configured to quantitatively estimate a factor of a state change between the first state and the second state of the target based on the generated contribution degree.
  • the estimation device 1 includes: The acquisition unit 10 is configured to acquire a standard contribution degree including the contribution degree corresponding to each factor of a plurality of state changes that may occur in the object, The estimation unit 40 compares the generated degree of contribution with the acquired standard contribution to quantitatively estimate the cause of the state change between the first state and the second state of the target. It is configured as follows.
  • the estimation device 1 includes: The estimation unit 40 is configured to estimate the degree of transition for the entire state change from the ratio of the generated contribution degree to the acquired reference contribution degree.
  • the estimation method includes: obtaining the feature amount obtained from the object in a first state of the object and the feature amount in a second state of the object different from the first state; testing a significant difference between the acquired feature amount in the first state and the acquired feature amount in the second state; Generating a degree of contribution of the feature amount to a change of the object from the first state to the second state from the tested significant difference; Based on the generated contribution degree, a factor of a state change between the first state and the second state of the target is quantitatively estimated.
  • the estimation device and estimation method of the present disclosure can be applied, for example, when the state of a device such as a machine tool changes from a normal state to an abnormal state, to estimate the factors that caused the state change.
  • Estimation device 1 Estimation device 2 Processor 3 Storage device 4 Communication device 10 Acquisition unit 20 Verification unit 30 Generation unit 40 Estimation unit

Abstract

This estimation device comprises an acquisition unit, a testing unit, a generation unit, and an estimating unit. The acquisition unit acquires a feature quantity obtained from a target in a first state of the target, and a feature quantity in a second state of the target different from the first state. The testing unit tests a significant difference between the acquired feature quantity in the first state and the acquired feature quantity in the second state. The generation unit generates a contribution degree of the feature quantity to the state change between the first state and the second state of the target, from the tested significant difference. The estimating unit quantitatively estimates a factor of state change between the first state and the second state of the target on the basis of the generated contribution degree.

Description

推定装置および推定方法Estimation device and estimation method
　本開示は、対象の状態変化の要因を推定可能な推定装置および推定方法に関する。 The present disclosure relates to an estimation device and an estimation method that are capable of estimating factors of a change in the state of a target.
　特許文献１には、植物に含まれる水分量を高確度で推定する栽培装置が開示されている。 Patent Document 1 discloses a cultivation device that estimates the amount of water contained in plants with high accuracy.
特開２０１９－０４５４８４号公報JP2019-045484A
　一般的に、対象の状態変化を捉える方法としては、対象から得られる特徴量に対して閾値を設定する方法がある。このような方法では、特徴量が閾値を超えたか否かしか分からず、対象の状態変化の要因を推定することが難しい場合がある。 Generally, as a method for capturing changes in the state of an object, there is a method of setting a threshold value for the feature amount obtained from the object. With such a method, it is only possible to know whether the feature value exceeds a threshold value, and it may be difficult to estimate the cause of a change in the state of the object.
　本開示は、対象の状態変化の要因を推定可能な推定装置および推定方法を提供することにある。 An object of the present disclosure is to provide an estimation device and an estimation method capable of estimating factors of a change in the state of a target.
　本開示の一態様の推定装置は、
　対象の第１状態における前記対象から得られる特徴量と、前記対象の前記第１状態とは異なる第２状態における前記特徴量とを取得する取得部と、
　取得された前記第１状態における前記特徴量と、取得された前記第２状態における前記特徴量との間の有意差を検定する検定部と、
　検定された前記有意差から、前記対象の前記第１状態および前記第２状態間の状態変化に対する前記特徴量の貢献度を生成する生成部と、
　生成された前記貢献度に基づいて、前記対象の前記第１状態および前記第２状態間の状態変化の要因を定量的に推定する推定部と
を備える。 An estimation device according to one aspect of the present disclosure includes:
an acquisition unit that acquires the feature amount obtained from the object in a first state of the object and the feature amount in a second state of the object different from the first state;
a testing unit that tests a significant difference between the acquired feature amount in the first state and the acquired feature amount in the second state;
a generation unit that generates a degree of contribution of the feature amount to a state change between the first state and the second state of the object from the tested significant difference;
and an estimation unit that quantitatively estimates a factor of a state change between the first state and the second state of the target based on the generated contribution degree.
　本開示の一態様の推定方法は、
　対象の第１状態における前記対象から得られる特徴量と、前記対象の前記第１状態とは異なる第２状態における前記特徴量とを取得し、
　取得された前記第１状態における前記特徴量と、取得された前記第２状態における前記特徴量との間の有意差を検定し、
　検定された前記有意差から、前記対象の前記第１状態から前記第２状態への変化に対する前記特徴量の貢献度を生成し、
　生成された前記貢献度に基づいて、前記対象の前記第１状態および前記第２状態間の状態変化の要因を定量的に推定する。 An estimation method according to one aspect of the present disclosure includes:
obtaining the feature amount obtained from the object in a first state of the object and the feature amount in a second state of the object different from the first state;
testing a significant difference between the acquired feature amount in the first state and the acquired feature amount in the second state;
Generating a degree of contribution of the feature amount to a change of the object from the first state to the second state from the tested significant difference;
Based on the generated contribution degree, a factor of a state change between the first state and the second state of the target is quantitatively estimated.
　前記態様の推定装置および推定方法によれば、対象の状態変化の要因を定量的に推定できる。 According to the estimation device and estimation method of the above aspect, it is possible to quantitatively estimate the cause of a change in the state of the object.
本開示の一実施形態の推定装置を示すブロック図。FIG. 1 is a block diagram showing an estimation device according to an embodiment of the present disclosure. 図１の推定装置の取得部で取得される特徴量の一例と、検定部で検定された有意差を示す検定値との一例を示す図。FIG. 2 is a diagram showing an example of a feature amount acquired by an acquisition unit of the estimation device of FIG. 1 and an example of a test value indicating a significant difference tested by a test unit. 図１の推定装置の生成部で生成される貢献度の一例と、取得部で取得される基準貢献度の一例と示す図。2 is a diagram showing an example of a contribution degree generated by a generation unit of the estimation device of FIG. 1 and an example of a standard contribution degree acquired by an acquisition unit. FIG. 図１の推定装置を用いた推定方法を説明するためのフローチャート。2 is a flowchart for explaining an estimation method using the estimation device of FIG. 1. FIG.
　以下、本開示の一例を添付図面に従って説明する。以下の説明は、本質的に例示に過ぎず、本開示、その適用物、あるいは、その用途を制限することを意図するものではない。図面は模式的なものであり、各寸法の比率等は現実のものとは必ずしも合致していない。 An example of the present disclosure will be described below with reference to the accompanying drawings. The following description is merely exemplary in nature and is not intended to limit the present disclosure, its applications, or its uses. The drawings are schematic, and the ratio of each dimension does not necessarily match the reality.
　本開示の一実施形態の推定装置１は、図１に示すように、取得部１０と、検定部２０と、生成部３０と、推定部４０とを備え、対象の状態変化の要因を定量的に推定するように構成されている。対象は、工作物を所要の形状に作り上げる工作機械、工作機械等を用いて材料を目的の形状に加工する加工機械等の装置を含む。対象の状態は、装置が正常に動作する正常状態と、装置が正常に動作しない異常状態とを含む。例えば、装置がエンドミル加工を行う工作機械である場合、異常状態は、エンドミルが摩耗して工作機械が正常に加工を行えない状態、切削油切れによる潤滑性不足で工作機械が正常に加工を行えない状態を含む。 As shown in FIG. 1, an estimation device 1 according to an embodiment of the present disclosure includes an acquisition unit 10, a verification unit 20, a generation unit 30, and an estimation unit 40, and quantitatively evaluates the factors of a state change of a target. is configured to estimate. Targets include machine tools that create workpieces into desired shapes, and equipment such as processing machines that use machine tools to process materials into desired shapes. The target states include a normal state in which the device operates normally, and an abnormal state in which the device does not operate normally. For example, if the equipment is a machine tool that performs end mill processing, abnormal conditions include a state in which the end mill is worn and the machine tool cannot perform normal processing, or a state in which the machine tool is unable to perform processing normally due to lack of lubrication due to lack of cutting oil. Including the state where there is no.
　推定装置１は、一例として、プロセッサ２、記憶装置３および通信装置４を備えている。取得部１０、検定部２０、生成部３０および推定部４０の各々は、例えば、プロセッサ２が所定のプログラムを実行することにより実現される。プロセッサ２は、ＣＰＵ、ＭＰＵ、ＧＰＵ、ＤＳＰ、ＦＰＧＡ、ＡＳＩＣ等を含む。記憶装置３は、例えば、内部記録媒体または外部記録媒体で構成されている。内部記録媒体は、不揮発メモリ等を含む。外部記録媒体は、ハードディスク（ＨＤＤ）、ソリッドステートドライブ（ＳＳＤ）、光ディスク装置等を含む。通信装置４は、例えば、サーバ等の外部装置との間でデータの送受信を行うための通信回路または通信モジュールで構成されている。 The estimation device 1 includes, for example, a processor 2, a storage device 3, and a communication device 4. Each of the acquisition unit 10, the verification unit 20, the generation unit 30, and the estimation unit 40 is realized, for example, by the processor 2 executing a predetermined program. The processor 2 includes a CPU, MPU, GPU, DSP, FPGA, ASIC, etc. The storage device 3 is composed of, for example, an internal recording medium or an external recording medium. The internal recording medium includes nonvolatile memory and the like. External recording media include hard disks (HDD), solid state drives (SSD), optical disk devices, and the like. The communication device 4 includes, for example, a communication circuit or a communication module for transmitting and receiving data to and from an external device such as a server.
　取得部１０は、例えば通信装置４を介して、対象の第１状態における特徴量と、前記対象の第１状態とは異なる第２状態における特徴量とを取得するように構成されている。特徴量は、対象の特徴を定量的に示した数値である。例えば、対象が加工機械等の装置である場合、特徴量は、対象の電圧および電流のアナログ値から算出される実効値、平均値、ピーク値、高調波含有率等を含む。特徴量は、サーバ等の外部装置で算出されてもよいし、推定装置１で算出されてもよい。 The acquisition unit 10 is configured to acquire, for example via the communication device 4, a feature amount of the target in a first state and a feature amount of the target in a second state different from the first state. The feature amount is a numerical value that quantitatively indicates the feature of the object. For example, when the target is a device such as a processing machine, the feature amount includes an effective value, an average value, a peak value, a harmonic content rate, etc. calculated from analog values of voltage and current of the target. The feature amount may be calculated by an external device such as a server, or may be calculated by the estimation device 1.
　本実施形態では、取得部１０は、基準貢献度を取得するように構成されている。基準貢献度は、対象に起こり得る複数の状態変化の各々の要因に対応する貢献度を含む。例えば、基準貢献度は、対象の状態Ｘ１から状態Ｙ２への状態変化の要因に対応する貢献度と、対象の状態Ｘ１から状態Ｚ２への状態変化の要因に対応する貢献度とを含む。基準貢献度は、例えば、サーバ等の外部装置から取得されてもよいし、基準貢献度が予め記憶された推定装置１の記憶装置３から取得されてもよい。 In this embodiment, the acquisition unit 10 is configured to acquire the standard contribution degree. The standard contribution includes a contribution corresponding to each factor of a plurality of state changes that may occur in the target. For example, the reference degree of contribution includes a degree of contribution corresponding to a factor of a state change of the object from state X1 to state Y2, and a degree of contribution corresponding to a factor of a state change of object from state X1 to state Z2. The standard contribution degree may be acquired from an external device such as a server, or may be acquired from the storage device 3 of the estimation device 1 in which the standard contribution degree is stored in advance.
　検定部２０は、取得された第１状態における特徴量と、取得された第２状態における特徴量との間の有意差を統計学的に検定するように構成されている。検定の種類は、対象の特性等により決定される。例えば、対象が加工機械等の装置である場合、次に示す理由により「Ｗｅｌｃｈのｔ検定」が用いられる。
　　・状態変化要因によって装置の状態が変わるため、同じ状態の対象から特徴量を取得することができない。
　　・２つの異なる状態（例えば、正常状態よび異常状態）の対象から得られる特徴量は、相互に対応していない。
　　・２つの異なる状態の対象から得られる特徴量は、母集団が異なり、母分散が等しいと仮定できない。 The test unit 20 is configured to statistically test the significant difference between the acquired feature amount in the first state and the acquired feature amount in the second state. The type of test is determined by the characteristics of the subject. For example, when the target is a device such as a processing machine, "Welch's t-test" is used for the following reasons.
- Since the state of the device changes depending on the state change factor, it is not possible to obtain features from objects in the same state.
- Feature amounts obtained from objects in two different states (for example, a normal state and an abnormal state) do not correspond to each other.
・Features obtained from objects in two different states have different populations, and it cannot be assumed that the population variances are equal.
　例えば、図２に示すように、２つの異なる状態Ｘ１、Ｘ２に対して、それぞれ３種類の特徴量Ａ、Ｂ、Ｃが複数取得されたとする。状態Ｘ１は第１状態の一例であり、状態Ｘ２は第２状態の一例である。検定部２０は、取得された状態Ｘ１における特徴量Ａと取得された状態Ｘ２における特徴量Ａとの間の有意差を検定し、取得された状態Ｘ１における特徴量Ｂと取得された状態Ｘ２における特徴量Ｂとの間の有意差を検定し、取得された状態Ｘ１における特徴量Ｃと取得された状態Ｘ２における特徴量Ｃとの間の有意差を検定する。各特徴量の有意差を検定した結果は、検定値として算出される。検定値が大きいほど、２つの状態Ｘ１、Ｘ２間の状態変化を検出し易い特徴量であると判断される。図２では、特徴量Ａが、対象の状態Ｘ１、Ｘ２間の状態変化を最も検出し易い特徴量であると判断される。 For example, as shown in FIG. 2, assume that a plurality of three types of feature amounts A, B, and C are obtained for two different states X1 and X2, respectively. State X1 is an example of a first state, and state X2 is an example of a second state. The testing unit 20 tests the significant difference between the feature amount A in the obtained state X1 and the feature amount A in the obtained state A significant difference between the feature amount B and the feature amount C is tested, and a significant difference between the feature amount C in the acquired state X1 and the feature amount C in the acquired state X2 is tested. The result of testing the significance of each feature amount is calculated as a test value. It is determined that the larger the test value is, the easier it is to detect a state change between the two states X1 and X2. In FIG. 2, the feature amount A is determined to be the feature amount that most easily detects the state change between the target states X1 and X2.
　生成部３０は、検定された有意差から、対象の第１状態および第２状態間の状態変化に対する特徴量の貢献度（以下、貢献度という。）を生成するように構成されている。貢献度は、対象のある状態と別の状態との識別に対して特徴量が貢献する度合いである。 The generation unit 30 is configured to generate the degree of contribution of the feature amount to the state change between the first state and the second state of the object (hereinafter referred to as contribution degree) from the tested significant difference. The degree of contribution is the degree to which the feature amount contributes to the discrimination between one state of the target and another state.
　例えば、生成部３０は、検定部２０の検定結果から、取得された基準貢献度と同じ順に複数の特徴量を用いて貢献度を生成する。一例として、図２に示す検定結果が検定部２０で得られたとすると、生成部３０は、特徴量Ａ、Ｂ、Ｃの検定値から貢献度を生成する。生成された貢献度は、例えば、図３に示すように、特徴量Ａ、Ｂ、Ｃの検定値を結んだ直線Ｌ１（破線で示す）で表される。 For example, the generation unit 30 generates a degree of contribution from the test result of the test unit 20 using a plurality of feature quantities in the same order as the acquired standard contribution degree. As an example, assuming that the test result shown in FIG. 2 is obtained by the test section 20, the generation section 30 generates the degree of contribution from the test values of the feature amounts A, B, and C. The generated degree of contribution is represented, for example, by a straight line L1 (indicated by a broken line) connecting the test values of the feature amounts A, B, and C, as shown in FIG.
　推定部４０は、生成された貢献度に基づいて、対象の第１状態および第２状態間の状態変化の要因を定量的に推定するように構成されている。本実施形態では、推定部４０は、生成された貢献度と、取得された基準貢献度とから、対象の第１状態および第２状態間の状態変化の要因を定量的に推定するように構成されている。 The estimation unit 40 is configured to quantitatively estimate the cause of the state change between the first state and the second state of the target based on the generated degree of contribution. In the present embodiment, the estimation unit 40 is configured to quantitatively estimate the cause of the state change between the first state and the second state of the target from the generated contribution degree and the acquired reference contribution degree. has been done.
　例えば、基準貢献度が、対象の状態Ｘ１から状態Ｙ２への状態変化の要因に対応する貢献度（以下、基準貢献度１という。）と、対象の状態Ｘ１から状態Ｚ２への状態変化の要因に対応する貢献度（以下、基準貢献度２という。）とを含むとする。図３に示すように、基準貢献度１が、特徴量Ａ、Ｂ、Ｃの検定値を結んだ直線Ｌ２（点線で示す）で表され、基準貢献度２が、特徴量Ａ、Ｂ、Ｃの検定値を結んだ直線Ｌ３（実線で示す）で表されるとする。この場合、推定部４０は、直線Ｌ１と、直線Ｌ２および直線Ｌ３とを比較して、直線Ｌ１と略同じ傾きを有する直線Ｌ３で表される基準貢献度２に対応する要因が、対象の状態Ｘ１、Ｘ２間の状態変化の要因であると推定する。 For example, the standard contribution is the contribution corresponding to the cause of the state change from the target state X1 to the state Y2 (hereinafter referred to as standard contribution 1), and the contribution corresponding to the cause of the state change from the target state X1 to the state Z2. (hereinafter referred to as standard contribution 2). As shown in FIG. 3, the standard contribution degree 1 is represented by a straight line L2 (indicated by a dotted line) connecting the test values of the feature quantities A, B, and C, and the standard contribution degree 2 is Suppose that it is represented by a straight line L3 (indicated by a solid line) connecting the test values of . In this case, the estimating unit 40 compares the straight line L1 with the straight lines L2 and L3, and determines whether the factor corresponding to the standard contribution degree 2 represented by the straight line L3 having substantially the same slope as the straight line L1 is in the target state. It is estimated that this is the cause of the state change between X1 and X2.
　本実施形態では、推定部４０は、取得した基準貢献度に対する生成された貢献度の比から、状態変化全体に対する遷移度合いを推定するように構成されている。例えば、生成された貢献度の検定値が（ｔＡ，ｔＢ，ｔＣ）であり、取得された基準貢献度２の検定値が（ＴＡ，ＴＢ，ＴＣ）であるとすると、状態変化全体に対する遷移度合いは、（ｔＡ＋ｔＢ＋ｔＣ）／（ＴＡ＋ＴＢ＋ＴＣ）により出される。ｔＡおよびＴＡは特徴量Ａを検定することで得られる検定値であり、ｔＢおよびＴＢは特徴量Ｂを検定することで得られる検定値であり、ｔＣおよびＴＣは特徴量Ｃを検定することで得られる検定値である。 In this embodiment, the estimation unit 40 is configured to estimate the degree of transition for the entire state change from the ratio of the generated contribution degree to the acquired standard contribution degree. For example, if the generated contribution test values are (t A , t B , t C ), and the obtained standard contribution degree 2 test values are ( TA , T B , T C ), The transition degree for the entire state change is given by (t A +t B +t C )/(T A +T B +T C ). t A and T A are test values obtained by testing the feature amount A, t B and T B are test values obtained by testing the feature amount B, and t C and T C are the test values obtained by testing the feature amount B. This is the test value obtained by testing C.
　図４を参照して、推定装置１を用いた推定方法の一例を説明する。推定方法は、例えば、プロセッサ２が所定のプログラムを実行することで実施される。 An example of an estimation method using the estimation device 1 will be explained with reference to FIG. The estimation method is implemented, for example, by the processor 2 executing a predetermined program.
　図４に示すように、推定方法が開始されると、取得部１０が、対象から、対象の第１状態の特徴量および対象の第２状態の特徴量を取得する（ステップＳ１）。 As shown in FIG. 4, when the estimation method is started, the acquisition unit 10 acquires the feature amount of the first state of the target and the feature amount of the second state of the target from the target (step S1).
　対象の第１状態の特徴量および対象の第２状態の特徴量が取得されると、検定部２０が、取得された第１状態の特徴量および第２状態の特徴量の間の有意差を検定する（ステップＳ２）。 When the feature amount of the first state of the target and the feature amount of the second state of the target are acquired, the testing unit 20 determines the significant difference between the acquired feature amount of the first state and the feature amount of the second state. Verify (step S2).
　取得された第１状態の特徴量および第２状態の特徴量の間の有意差が検定されると、生成部３０が、検定された有意差から、対象の第１状態および第２状態間の状態変化に対する特徴量の貢献度を生成する（ステップＳ３）。 When the significant difference between the acquired feature amount of the first state and the feature amount of the second state is tested, the generation unit 30 calculates the difference between the first state and the second state of the object based on the tested significant difference. The degree of contribution of the feature amount to the state change is generated (step S3).
　対象の第１状態および第２状態間の状態変化に対する特徴量の貢献度が生成されると、推定部４０が、生成された貢献度および基準貢献度を比較して、対象の第１状態および第２状態間の状態変化の要因を定量的に推定し（ステップＳ４）、推定方法が終了する。 When the degree of contribution of the feature amount to the state change between the first state and the second state of the target is generated, the estimation unit 40 compares the generated degree of contribution and the standard contribution and determines the first state and the second state of the target. The factor of the state change between the second states is quantitatively estimated (step S4), and the estimation method ends.
　推定装置１は、次のような効果を発揮できる。 The estimation device 1 can exhibit the following effects.
　推定装置１が、取得部１０と、検定部２０と、生成部３０と、推定部４０とを備える。取得部１０は、対象の第１状態における対象から得られる特徴量と、対象の第１状態とは異なる第２状態における特徴量とを取得する。検定部２０は、取得された第１状態における特徴量と取得された第２状態における特徴量との間の有意差を検定する。生成部３０は、検定された有意差から、対象の第１状態および第２状態間の状態変化に対する特徴量の貢献度を生成する。推定部４０は、生成された貢献度に基づいて、対象の第１状態および第２状態間の状態変化の要因を定量的に推定する。このような構成により、対象の状態変化の要因を定量的に推定できる。より具体的には、対象がエンドミル加工を行う工作機械であり、工作機械が正常状態から異常状態に状態変化した場合、この状態変化の要因がエンドミルの摩耗なのか、切削油切れによる潤滑性不足なのかを推定できる。 The estimation device 1 includes an acquisition section 10, a verification section 20, a generation section 30, and an estimation section 40. The acquisition unit 10 acquires the feature amount obtained from the object in the first state of the object and the feature amount in the second state of the object different from the first state. The testing unit 20 tests the significant difference between the acquired feature amount in the first state and the acquired feature amount in the second state. The generation unit 30 generates the degree of contribution of the feature amount to the state change between the first state and the second state of the object from the tested significant difference. The estimating unit 40 quantitatively estimates the cause of the state change between the first state and the second state of the target based on the generated degree of contribution. With such a configuration, it is possible to quantitatively estimate the cause of a change in the state of the object. More specifically, if the target is a machine tool that performs end mill processing, and the machine tool changes from a normal state to an abnormal state, whether the cause of this state change is wear of the end mill or lack of lubrication due to lack of cutting oil. It is possible to estimate whether
　取得部１０が、対象に起こり得る複数の状態変化の各々の要因に対応する貢献度を含む基準貢献度を取得する。推定部４０は、生成された貢献度と取得した基準貢献度とを比較して、対象の第１状態および第２状態間の状態変化の要因を定量的に推定する。このような構成により、対象の状態変化の要因を定量的に推定できる。 The acquisition unit 10 acquires a standard contribution degree that includes a contribution degree corresponding to each factor of a plurality of state changes that may occur in the target. The estimation unit 40 compares the generated degree of contribution with the acquired reference degree of contribution, and quantitatively estimates the cause of the state change between the first state and the second state of the object. With such a configuration, it is possible to quantitatively estimate the cause of a change in the state of the object.
　推定部４０は、取得した基準貢献度に対する生成された貢献度の比から、状態変化全体に対する遷移度合いを推定する。このような構成により、対象の状態変化の遷移度合いを定量化できる。 The estimation unit 40 estimates the degree of transition for the entire state change from the ratio of the generated contribution degree to the acquired standard contribution degree. With such a configuration, the degree of transition of the state change of the object can be quantified.
　本開示の推定方法は、次のような効果を発揮できる。 The estimation method of the present disclosure can exhibit the following effects.
　推定方法が、次に示すステップを備える。このような構成により、対象の状態変化の要因を定量的に推定できる。
　　・対象の第１状態における対象から得られる特徴量と、対象の第１状態とは異なる第２状態における特徴量とを取得する。
　　・取得された第１状態における特徴量と、取得された第２状態における特徴量との間の有意差を検定する。
　　・検定された有意差から、対象の第１状態から第２状態への変化に対する特徴量の貢献度を生成する。
　　・生成された貢献度に基づいて、対象の第１状態および第２状態間の状態変化の要因を定量的に推定する。 The estimation method includes the following steps. With such a configuration, it is possible to quantitatively estimate the cause of a change in the state of the object.
- Obtain the feature amount obtained from the object in the first state of the object and the feature amount in the second state different from the first state of the object.
- Testing the significant difference between the acquired feature amount in the first state and the acquired feature amount in the second state.
- Generate the degree of contribution of the feature amount to the change from the first state to the second state of the object from the tested significant difference.
- Quantitatively estimate the cause of the state change between the first state and the second state of the target based on the generated contribution degree.
　推定装置１は、次のように構成することもできる。 The estimation device 1 can also be configured as follows.
　取得される特徴量の種類は、予め設定されていてもよいし、予め設定されていなくてもよい。取得される特徴量の種類が予め設定されていない場合、例えば、取得部１０は、取得可能な全ての種類の特徴量を取得し、検定部２０の検定結果により、貢献度の生成に用いる特徴量の種類を決定してもよい。 The type of feature amount to be acquired may or may not be set in advance. If the types of feature amounts to be acquired are not set in advance, for example, the acquisition unit 10 acquires all types of feature amounts that can be acquired, and based on the test results of the test unit 20, selects the features to be used for generating the degree of contribution. The type of amount may also be determined.
　推定部４０は、基準貢献度を用いる場合に限らず、他の方法を用いて、生成された貢献度に基づいて対象の第１状態および第２状態間の状態変化の要因を定量的に推定可能に構成することができる。 The estimation unit 40 quantitatively estimates the cause of the state change between the first state and the second state of the target based on the generated contribution, not only when using the standard contribution but also using other methods. It can be configured as possible.
　以上、図面を参照して本開示における種々の実施形態を詳細に説明したが、最後に、本開示の種々の態様について説明する。なお、以下の説明では、一例として、参照符号も添えて記載する。 Various embodiments of the present disclosure have been described above in detail with reference to the drawings, and finally, various aspects of the present disclosure will be described. Note that in the following description, reference numerals are also included as an example.
　本開示の第１態様の推定装置１は、
　対象の第１状態における前記対象から得られる特徴量と、前記対象の前記第１状態とは異なる第２状態における前記特徴量とを取得するように構成されている取得部１０と、
　取得された前記第１状態における前記特徴量と、取得された前記第２状態における前記特徴量との間の有意差を検定するように構成されている検定部２０と、
　検定された前記有意差から、前記対象の前記第１状態および前記第２状態間の状態変化に対する前記特徴量の貢献度を生成するように構成されている生成部３０と、
　生成された前記貢献度に基づいて、前記対象の前記第１状態および前記第２状態間の状態変化の要因を定量的に推定するように構成されている推定部４０と
を備える。 The estimation device 1 according to the first aspect of the present disclosure includes:
an acquisition unit 10 configured to acquire the feature amount obtained from the object in a first state of the object and the feature amount in a second state of the object different from the first state;
a testing unit 20 configured to test a significant difference between the acquired feature amount in the first state and the acquired feature amount in the second state;
a generation unit 30 configured to generate a degree of contribution of the feature amount to a state change between the first state and the second state of the object from the tested significant difference;
The estimation unit 40 is configured to quantitatively estimate a factor of a state change between the first state and the second state of the target based on the generated contribution degree.
　本開示の第２態様の推定装置１は、
　前記取得部１０が、前記対象に起こり得る複数の状態変化の各々の要因に対応する前記貢献度を含む基準貢献度を取得するように構成され、
　前記推定部４０は、生成された前記貢献度と取得された前記基準貢献度とを比較して、前記対象の前記第１状態および前記第２状態間の状態変化の要因を定量的に推定するように構成されている。 The estimation device 1 according to the second aspect of the present disclosure includes:
The acquisition unit 10 is configured to acquire a standard contribution degree including the contribution degree corresponding to each factor of a plurality of state changes that may occur in the object,
The estimation unit 40 compares the generated degree of contribution with the acquired standard contribution to quantitatively estimate the cause of the state change between the first state and the second state of the target. It is configured as follows.
　本開示の第３態様の推定装置１は、
　前記推定部４０は、取得した前記基準貢献度に対する生成された前記貢献度の比から、状態変化全体に対する遷移度合いを推定するように構成されている。 The estimation device 1 according to the third aspect of the present disclosure includes:
The estimation unit 40 is configured to estimate the degree of transition for the entire state change from the ratio of the generated contribution degree to the acquired reference contribution degree.
　本開示の第４態様の推定方法は、
　対象の第１状態における前記対象から得られる特徴量と、前記対象の前記第１状態とは異なる第２状態における前記特徴量とを取得し、
　取得された前記第１状態における前記特徴量と、取得された前記第２状態における前記特徴量との間の有意差を検定し、
　検定された前記有意差から、前記対象の前記第１状態から前記第２状態への変化に対する前記特徴量の貢献度を生成し、
　生成された前記貢献度に基づいて、前記対象の前記第１状態および前記第２状態間の状態変化の要因を定量的に推定する。 The estimation method according to the fourth aspect of the present disclosure includes:
obtaining the feature amount obtained from the object in a first state of the object and the feature amount in a second state of the object different from the first state;
testing a significant difference between the acquired feature amount in the first state and the acquired feature amount in the second state;
Generating a degree of contribution of the feature amount to a change of the object from the first state to the second state from the tested significant difference;
Based on the generated contribution degree, a factor of a state change between the first state and the second state of the target is quantitatively estimated.
　前記様々な実施形態または変形例のうちの任意の実施形態または変形例を適宜組み合わせることにより、それぞれの有する効果を奏するようにすることができる。また、実施形態同士の組み合わせまたは実施例同士の組み合わせまたは実施形態と実施例との組み合わせが可能であると共に、異なる実施形態または実施例の中の特徴同士の組み合わせも可能である。 By appropriately combining any of the various embodiments or modifications described above, the effects of each can be achieved. In addition, combinations of embodiments, combinations of examples, or combinations of embodiments and examples are possible, and combinations of features in different embodiments or examples are also possible.
　本開示は、添付図面を参照しながら好ましい実施形態に関連して充分に記載されているが、この技術の熟練した人々にとっては種々の変形や修正は明白である。そのような変形や修正は、添付した請求の範囲による本開示の範囲から外れない限りにおいて、その中に含まれると理解されるべきである。 Although this disclosure has been fully described with reference to preferred embodiments and with reference to the accompanying drawings, various variations and modifications will be apparent to those skilled in the art. It is to be understood that such variations and modifications are included insofar as they do not depart from the scope of the disclosure as defined by the appended claims.
　本開示の推定装置および推定方法は、例えば、工作機械等の装置が正常状態から異常状態に状態変化した場合に、その状態変化をもたらした要因の推定に適用できる。 The estimation device and estimation method of the present disclosure can be applied, for example, when the state of a device such as a machine tool changes from a normal state to an abnormal state, to estimate the factors that caused the state change.
１　推定装置
２　プロセッサ
３　記憶装置
４　通信装置
１０　取得部
２０　検定部
３０　生成部
４０　推定部 1 Estimation device 2 Processor 3 Storage device 4 Communication device 10 Acquisition unit 20 Verification unit 30 Generation unit 40 Estimation unit

Claims (4)

  1. 　対象の第１状態における前記対象から得られる特徴量と、前記対象の前記第１状態とは異なる第２状態における前記特徴量とを取得する取得部と、
    　取得された前記第１状態における前記特徴量と、取得された前記第２状態における前記特徴量との間の有意差を検定する検定部と、
    　検定された前記有意差から、前記対象の前記第１状態および前記第２状態間の状態変化に対する前記特徴量の貢献度を生成する生成部と、
    　生成された前記貢献度に基づいて、前記対象の前記第１状態および前記第２状態間の状態変化の要因を定量的に推定する推定部と
    を備える、推定装置。     an acquisition unit that acquires the feature amount obtained from the object in a first state of the object and the feature amount in a second state of the object different from the first state;
    a testing unit that tests a significant difference between the acquired feature amount in the first state and the acquired feature amount in the second state;
    a generation unit that generates a degree of contribution of the feature amount to a state change between the first state and the second state of the object from the tested significant difference;
    An estimation device comprising: an estimation unit that quantitatively estimates a factor of a state change between the first state and the second state of the target based on the generated contribution degree.
  2. 　前記取得部が、前記対象に起こり得る複数の状態変化の各々の要因に対応する前記貢献度を含む基準貢献度を取得し、
    　前記推定部は、生成された前記貢献度と取得した前記基準貢献度とを比較して、前記対象の前記第１状態および前記第２状態間の状態変化の要因を定量的に推定する、請求項１に記載の推定装置。     The acquisition unit acquires a standard contribution degree including the contribution degree corresponding to each factor of a plurality of state changes that may occur in the target,
    The estimation unit quantitatively estimates a factor of a state change between the first state and the second state of the target by comparing the generated contribution degree and the acquired reference contribution degree. The estimation device according to item 1.
  3. 　前記推定部は、取得した前記基準貢献度に対する生成された前記貢献度の比から、状態変化全体に対する遷移度合いを推定する、請求項２に記載の推定装置。 The estimating device according to claim 2, wherein the estimating unit estimates the degree of transition for the entire state change from a ratio of the generated contribution degree to the acquired reference contribution degree.
  4. 　対象の第１状態における前記対象から得られる特徴量と、前記対象の前記第１状態とは異なる第２状態における前記特徴量とを取得し、
    　取得された前記第１状態における前記特徴量と、取得された前記第２状態における前記特徴量との間の有意差を検定し、
    　検定された前記有意差から、前記対象の前記第１状態から前記第２状態への変化に対する前記特徴量の貢献度を生成し、
    　生成された前記貢献度に基づいて、前記対象の前記第１状態および前記第２状態間の状態変化の要因を定量的に推定する、推定方法。     obtaining the feature amount obtained from the object in a first state of the object and the feature amount in a second state of the object different from the first state;
    testing a significant difference between the acquired feature amount in the first state and the acquired feature amount in the second state;
    Generating a degree of contribution of the feature amount to a change of the object from the first state to the second state from the tested significant difference;
    An estimation method that quantitatively estimates a factor of a state change between the first state and the second state of the target based on the generated contribution degree.
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JP2012189319A (en) * 2011-02-21 2012-10-04 Sumitomo Heavy Ind Ltd Evaluation method and evaluation apparatus for change gear
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