JP2020030111A5 - Abnormality sign detection system - Google Patents
Abnormality sign detection system Download PDFInfo
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- JP2020030111A5 JP2020030111A5 JP2018155915A JP2018155915A JP2020030111A5 JP 2020030111 A5 JP2020030111 A5 JP 2020030111A5 JP 2018155915 A JP2018155915 A JP 2018155915A JP 2018155915 A JP2018155915 A JP 2018155915A JP 2020030111 A5 JP2020030111 A5 JP 2020030111A5
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- 230000005856 abnormality Effects 0.000 title claims 13
- 238000001514 detection method Methods 0.000 title claims 6
- 238000004364 calculation method Methods 0.000 claims 3
- 238000013528 artificial neural network Methods 0.000 claims 1
- 230000006835 compression Effects 0.000 claims 1
- 238000007906 compression Methods 0.000 claims 1
- 238000011156 evaluation Methods 0.000 claims 1
Claims (4)
事前に前記対象設備の正常時に収集された前記振動波形データに基づき多次元特徴量を生成する多次元特徴量算出部と、
前記多次元特徴量を正常データとして学習することで入力を出力として再現する正常モデルを生成する正常モデル作成部と、
前記対象設備の前記振動波形データに基づき生成された多次元特徴量を診断データとし、前記診断データを前記正常モデルに入力したときの入出力の誤差分布を求める再構築誤差算出部と、
前記診断データの誤差分布が前記正常データの誤差分布を逸脱していれば、前記異常予兆を判定する異常判定部と、
前記診断データの周波数毎の誤差を算出し、算出された誤差の評価に応じて前記対象設備の異常要因を推定する異常要因推定部と、
を備えることを特徴とする異常予兆検出システム。 An abnormality sign detection system that detects anomaly signs based on the vibration waveform data of the target equipment.
A multidimensional feature calculation unit that generates multidimensional features based on the vibration waveform data collected in advance when the target equipment is normal, and a multidimensional feature calculation unit.
A normal model creation unit that generates a normal model that reproduces the input as an output by learning the multidimensional features as normal data.
A reconstruction error calculation unit that uses a multidimensional feature amount generated based on the vibration waveform data of the target equipment as diagnostic data and obtains an input / output error distribution when the diagnostic data is input to the normal model.
If the error distribution of the diagnostic data deviates from the error distribution of the normal data, the abnormality determination unit for determining the abnormality sign and the abnormality determination unit
An abnormality factor estimation unit that calculates an error for each frequency of the diagnostic data and estimates an abnormality factor of the target equipment according to the evaluation of the calculated error.
An abnormality sign detection system characterized by being equipped with.
ことを特徴とする請求項１記載の異常予兆検出システム。 The abnormality sign detection system according to claim 1, wherein the normal model creating unit creates the normal model by a dimension compression type autoencoder using a neural network.
前記診断データの誤差分布が事前設定の閾値を越えれば、前記正常データの誤差分布を逸脱していると判断する
ことを特徴とする請求項１または２記載の異常予兆検出システム。 The abnormality determination unit
The abnormality sign detection system according to claim 1 or 2, wherein if the error distribution of the diagnostic data exceeds a preset threshold value, it is determined that the error distribution of the normal data deviates from the error distribution.
前記誤差を高評価とすることを特徴とする請求項１〜３のいずれかに記載の異常予兆検出システム。 If the error of the diagnostic data exceeds the preset threshold value, the abnormality factor estimation unit will be used.
The abnormality sign detection system according to any one of claims 1 to 3, wherein the error is highly evaluated.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
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| JP2018155915A JP7056465B2 (en) | 2018-08-23 | 2018-08-23 | Abnormality sign detection system |
| JP2021098709A JP7196954B2 (en) | 2018-08-23 | 2021-06-14 | Anomaly sign detection method |
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| JP2018155915A JP7056465B2 (en) | 2018-08-23 | 2018-08-23 | Abnormality sign detection system |
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| JP2021098709A Division JP7196954B2 (en) | 2018-08-23 | 2021-06-14 | Anomaly sign detection method |
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| JP2020030111A JP2020030111A (en) | 2020-02-27 |
| JP2020030111A5 true JP2020030111A5 (en) | 2021-07-26 |
| JP7056465B2 JP7056465B2 (en) | 2022-04-19 |
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| JP2018155915A Active JP7056465B2 (en) | 2018-08-23 | 2018-08-23 | Abnormality sign detection system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| JP7438805B2 (en) * | 2020-03-24 | 2024-02-27 | 大阪瓦斯株式会社 | Abnormality analysis device and abnormality analysis method |
| JP7381390B2 (en) * | 2020-04-13 | 2023-11-15 | 株式会社日立製作所 | Abnormality diagnosis device and maintenance management system |
| JP2021197158A (en) * | 2020-06-15 | 2021-12-27 | 三菱パワー株式会社 | Predictive judgment device, Predictive judgment system, Predictive judgment method and program |
| JP7499132B2 (en) | 2020-09-18 | 2024-06-13 | Ｂｉｐｒｏｇｙ株式会社 | COMPUTER PROGRAM, ABNORMALITY DETECTION METHOD, AND ABNORMALITY DETECTION DEVICE |
| WO2022064590A1 (en) * | 2020-09-24 | 2022-03-31 | Ｓｉシナジーテクノロジー株式会社 | Trained autoencoder, trained autoencoder generation method, non-stationary vibration detection method, non-stationary vibration detection device, and computer program |
| JP7647138B2 (en) * | 2021-02-08 | 2025-03-18 | 株式会社明電舎 | Abnormality diagnosis system and abnormality diagnosis method, frequency fluctuation correction processing device and correction processing method, abnormality diagnosis program, and frequency fluctuation correction processing program |
| CN113572539B (en) * | 2021-06-24 | 2022-08-26 | 西安电子科技大学 | Storage-enhanced unsupervised spectrum anomaly detection method, system, device and medium |
| JP7580658B2 (en) | 2022-03-17 | 2024-11-11 | 三菱電機株式会社 | Equipment diagnostic device and equipment diagnostic system |
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| JPH06167385A (en) * | 1992-11-30 | 1994-06-14 | Hitachi Ltd | Acoustic diagnosis method |
| JP2000162035A (en) * | 1998-11-30 | 2000-06-16 | Kanegafuchi Chem Ind Co Ltd | Method and device for determining abnormality in rotating equipment |
| JP2005121639A (en) * | 2003-09-22 | 2005-05-12 | Omron Corp | Inspection method, inspection apparatus and diagnostic apparatus for facility |
| JP4744826B2 (en) * | 2004-08-18 | 2011-08-10 | 東芝エレベータ株式会社 | Elevator abnormality diagnosis device |
| JP2006292734A (en) * | 2005-03-15 | 2006-10-26 | Omron Corp | Determination model producing support device for test device and test device, and endurance test device and endurance test method |
| JP6402541B2 (en) * | 2014-08-26 | 2018-10-10 | 株式会社豊田中央研究所 | Abnormality diagnosis apparatus and program |
| WO2016132468A1 (en) * | 2015-02-18 | 2016-08-25 | 株式会社日立製作所 | Data evaluation method and device, and breakdown diagnosis method and device |
| EP3385889A4 (en) * | 2015-12-01 | 2019-07-10 | Preferred Networks, Inc. | Abnormality detection system, abnormality detection method, abnormality detection program, and method for generating learned model |
| JP2017142153A (en) * | 2016-02-10 | 2017-08-17 | セイコーエプソン株式会社 | Life prediction method, life prediction device, and life prediction system |
| JP6450738B2 (en) * | 2016-12-14 | 2019-01-09 | ファナック株式会社 | Machine learning device, CNC device, and machine learning method for detecting sign of occurrence of tool vibration in machine tool |
| JP6896432B2 (en) * | 2017-01-11 | 2021-06-30 | 株式会社Ｙｅ Ｄｉｇｉｔａｌ | Failure prediction method, failure prediction device and failure prediction program |
| JP6751168B2 (en) * | 2017-02-02 | 2020-09-02 | 日本電信電話株式会社 | Abnormal factor estimation device, abnormal factor estimation method and program |
| EP3584573B1 (en) * | 2017-02-15 | 2023-01-04 | Nippon Telegraph and Telephone Corporation | Abnormal sound detection training device and method and program therefor |
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