JP2022516159A - 風力タービンギアボックス構成要素における観察可能な損傷のリスクを予測するための方法及びシステム - Google Patents
風力タービンギアボックス構成要素における観察可能な損傷のリスクを予測するための方法及びシステム Download PDFInfo
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- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
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- G05B17/02—Systems involving the use of models or simulators of said systems electric
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D80/00—Details, components or accessories not provided for in groups F03D1/00 - F03D17/00
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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- Life Sciences & Earth Sciences (AREA)
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- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
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Abstract
Description
本出願は、2018年12月31日に出願された米国仮出願第62/786,817号に対する優先権を主張するものであり、その開示全体が参照により本明細書に組み込まれる。
本開示は、概して、風力タービンに関する。より具体的には、本開示は、概して、風力タービンギアボックス構成要素の観察可能な損傷のリスクを評価する方法に関する。
Claims (28)
- 風力タービンの風力タービンギアボックスの損傷状態を判定するための方法であって、
損傷状態診断を生成することであって、
前記風力タービンから測定データセットを取得することと、
現場操作者提供の現場インテリジェンスを前記測定データセットに統合することと、
前記風力タービンギアボックスの既知の故障挙動に関して前記測定データセットを正規化して、正規化された測定データセットを生成することと、
正規化された前記測定データセットを前記既知の故障挙動と組み合わせて、前記損傷状態診断を生成することと、を含む、生成することと、
動作データ及び状態遷移関数を使用して前記風力タービンギアボックスの損傷進行モデルを生成することと、
ハイブリッドプログノスティックスモデルを使用して前記損傷状態診断を前記損傷進行モデルと組み合わせて、現在の損傷状態推定の確率分布を生成することと、を含む、方法。 - 前記ハイブリッドプログノスティックモデルが、サンプルベースの予測因子を含み、前記方法が、前記ハイブリッドプログノスティックスモデルを使用して、未来状態予測を生成することを更に含み、前記未来状態予測が、
前記風力タービンギアボックスが予測範囲の致命的損傷閾値に達する確率、
前記予測範囲に達するまでの時間、及び
タイムステップでの前記風力タービンギアボックスのシステム状態を含む、請求項1に記載の方法。 - 前記損傷進行モデルが、物理学ベースのモデルを含む、請求項1に記載の方法。
- 前記物理学ベースのモデルが、有限要素分析、計算流体力学、多体力学、又は線形弾性破壊力学のうちの1つ以上を含む、請求項3に記載の方法。
- 前記損傷進行モデルが、機械学習ベースのモデルを含む、請求項1に記載の方法。
- 前記現場操作者提供の現場インテリジェンスが、操作者によって発見された、これまで確認された故障又は損傷を含む、請求項1に記載の方法。
- 前記操作者によって発見された、これまで確認された故障又は損傷が、ボアスコープ分析に存在しない事例を含む、請求項6に記載の方法。
- 前記操作者によって発見された、これまで確認された故障又は損傷を使用して、モデルを構築することを更に含む、請求項6に記載の方法。
- 前記操作者によって発見された、これまで確認された故障又は損傷を使用して、データ表現を構築することを更に含む、請求項6に記載の方法。
- 前記測定データセットが、直接測定データ、間接測定データ、大域的挙動データ、又は動作データ、のうちの1つ以上を含む、請求項1に記載の方法。
- 前記測定データセットが、直接測定データを含み、前記測定データセットを取得することが、測定ユニットを使用して直接測定を行って、前記直接測定データを取得することを含む、請求項10に記載の方法。
- 前記測定ユニットが、ボアスコープを備える、請求項11に記載の方法。
- 前記測定データセットが、間接測定データを含み、前記測定データセットを取得することが、温度センサ又は振動センサを使用して間接測定を行うことを含む、請求項10に記載の方法。
- 大域的挙動データが、前記風力タービンと同様に構成された1つ以上の類似する風力タービンの動作データの比較を含む、請求項10に記載の方法。
- 動作データが、監視制御及びデータ収集(SCADA)データを含む、請求項10に記載の方法。
- SCADAデータが、システム入力データ、システム出力データ、又はシステムログの時系列、のうちの1つ以上を含む、請求項15に記載の方法。
- 前記SCADAデータが、システム入力データを含み、前記システム入力データが、風速又は風向のうちの1つ以上を含む、請求項16に記載の方法。
- 前記SCADAデータが、システム出力データを含み、前記システム出力データが、発生した電力を含む、請求項16に記載の方法。
- 前記SCADAデータが、前記システムログの時系列を含み、前記システムログの時系列が、風力タービンの状態又は障害の時系列を含む、請求項16に記載の方法。
- 動作データが、監視制御及びデータ収集(SCADA)データを含む、請求項1に記載の方法。
- SCADAデータが、システム入力データ、システム出力データ、又はシステムログの時系列、のうちの1つ以上を含む、請求項20に記載の方法。
- 前記SCADAデータが、システム入力データを含み、前記システム入力データが、風速又は風向のうちの1つ以上を含む、請求項21に記載の方法。
- 前記SCADAデータが、システム出力データを含み、前記システム出力データが、発生した電力を含む、請求項21に記載の方法。
- 前記SCADAデータが、前記システムログの時系列を含み、前記システムログの時系列が、風力タービンの状態又は障害の時系列を含む、請求項21に記載の方法。
- 前記既知の故障挙動が、履歴データから導出される、請求項1に記載の方法。
- 前記履歴データが、保守記録、現場故障情報、根本原因分析、又は材料表データを含む、請求項25に記載の方法。
- 請求項1~26のいずれかに記載の方法を実行するように構成されたプロセッサを備える、風力タービンギアボックスの損傷状態を判定するためのシステム。
- プロセッサによって実施されたときに、請求項1~26のいずれかに記載の方法を前記プロセッサに行わせる命令を有する、有形の非一時的コンピュータ可読媒体。
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US201862786817P | 2018-12-31 | 2018-12-31 | |
US62/786,817 | 2018-12-31 | ||
PCT/US2019/069140 WO2020142542A1 (en) | 2018-12-31 | 2019-12-31 | Methods and systems for predicting risk of observable damage in wind turbine gearbox components |
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AU (1) | AU2019417827A1 (ja) |
BR (1) | BR112021012919A2 (ja) |
CL (1) | CL2021001743A1 (ja) |
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US11486483B1 (en) * | 2019-07-08 | 2022-11-01 | Gpms International, Inc. | Condition monitoring for components of a gearbox |
EP3958458A1 (en) * | 2020-08-19 | 2022-02-23 | Siemens Aktiengesellschaft | Software based condition monitoring for machines |
ES2936221T3 (es) | 2020-09-14 | 2023-03-15 | Nordex Energy Se & Co Kg | Un método para operar una turbina eólica |
US12038354B2 (en) * | 2020-09-25 | 2024-07-16 | Ge Infrastructure Technology Llc | Systems and methods for operating a power generating asset |
US11640164B2 (en) * | 2021-03-31 | 2023-05-02 | Caterpillar Inc. | Predicting risk of machine components not achieving agreed life systems and methods |
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JP7514242B2 (ja) | 2024-07-10 |
ZA202105732B (en) | 2023-05-31 |
BR112021012919A2 (pt) | 2023-02-14 |
CN113614359A (zh) | 2021-11-05 |
EP3906360A4 (en) | 2022-10-12 |
US11835030B2 (en) | 2023-12-05 |
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