JP2014130141A - マルチモーダル流体コンディションセンサプラットフォーム及びそのためのシステム - Google Patents
マルチモーダル流体コンディションセンサプラットフォーム及びそのためのシステム Download PDFInfo
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Abstract
【解決手段】本発明は、自動車のレシプロエンジン及び車両のトランスミッションのような循環システムに含まれる流体の様々なアスペクトをマルチモーダル統合同時測定するための実施の形態を網羅する。これらの循環システムは、不断の内部潤滑と熱及び混入物の除去とを実行して、内部可動部品を通常動作における固有摩擦及び損傷から保護する。その流体は、長い年月の間に保護特性を失い、内的及び外的イベントによる分解/劣化又は性能変化が起こり得る。実施の形態において、流体ベース閉鎖系環境における複数のセンサモードの測定から導出される流体のマルチ特性の連続モニタリングのための統合システムが提供される。
【選択図】図1
Description
Claims (21)
- 流体ベース閉鎖系環境における複数のセンサモードによる測定から導出される流体のマルチ特性の連続モニタリングのための統合システム。
- 原動機内潤滑油モニタリングシステムである
請求項1に記載の統合システム。 - 前記モニタリングは実時間モニタリングである
請求項1に記載の統合システム。 - レシプロエンジンオイルドレインプラグに見出される標準のサイズ及び形状のオイルプラグエレメントの形状要素に組み込まれ、有線又は無線のデータテレメトリにより表示・処理部から遠隔配置される
請求項1に記載の統合システム。 - 遠隔配置された処理・表示部を更に備える
請求項1に記載の統合システム。 - 前記複数のセンサモードは、電気センサ、温度センサ、磁気センサ、光センサ、及び圧力センサの少なくとも二つを備える
請求項1に記載の統合システム。 - 前記複数のセンサモードの少なくとも一つは、インダクタを備える
請求項1に記載の統合システム。 - 前記複数のセンサモードは、少なくとも磁気センサ及び光センサを備える
請求項1に記載の統合システム。 - 前記複数のセンサモードは、少なくとも、電気センサ、磁気センサ、及び光センサを備える
請求項1に記載の統合システム。 - 高温高圧環境に耐えることができるエポキシカプセルに収容された
請求項1に記載の統合システム。 - シングル及びマルチ関連流体特性の両方を検出するためのマルチデジタル信号処理モジュールを更に備える
請求項1に記載の統合システム。 - エラー指示、特定データ特性検出信号、特定データ特性信号検出強度レベル、及び高速フーリエ変換(FFT)データ出力からなるグループから選択されるマルチステージ出力信号生成を更に備える
請求項1に記載の統合システム。 - センサモード測定は、カルマンフィルタリング法を用いて分析される
請求項1に記載の統合システム。 - センサモード測定は、ベイズ分析法を用いて分析される
請求項1に記載の統合システム。 - センサモード測定は、隠れマルコフフィルタリング法を用いて分析される
請求項1に記載の統合システム。 - センサモード測定は、ファジィ理論分析法を用いて分析される
請求項1に記載の統合システム。 - センサモード測定は、ニューラルネットワーク分析法を用いて分析される
請求項1に記載の統合システム。 - センサモード測定は、下記a)〜m)のうちの少なくとも一つを含む:
a)差分温度比較、
b)差分磁気センサ比較、
c)差分誘導センサ比較、
d)差分電気インピーダンス比較、
e)差分光吸収率比較、
f)二つのセンサからなる少なくとも一つの組から構成される任意の組合せ及び統合比較、
g)各センサベクトル対時間及び温度のデータ比較、
h)少なくとも二つのセンサが組み合されてなる組から構成される統合ベクトルのデータ比較、
i)誘導データ比較対時間及び温度、
j)光学的データ比較対時間及び温度、
k)光学的データ比較対温度及び圧力、
l)ピーク熱を検出するための温度データ比較対時間及び圧力、及び
m)他のセンサ組合せ
請求項1に記載の統合システム。 - 第1センサモードを用いて機械の作動流体の第1コンディションを測定し、
第2センサモードを用いて前記流体の第2コンディションを測定し、
前記第1センサモード及び前記第2センサモードからのデータをフィルタリングし、
前記データを統合し、
前記データを解析し、
前記データから前記流体の特性を導出し、
導出された前記流体のコンディションの前記特性を受信機に送信し、
上記プロセスを繰り返して前記流体の動作コンディションの変化をトラッキングする流体特性の時系列を蓄積する
機械の作動流体の連続モニタリング方法。 - 任意のシングル又はマルチコンディションの変化の期待速度対観測速度の時系列を計算することで前記流体のコンディションをトラッキングする
請求項19に記載の方法。 - 予想及び期待された測定値変化対期待されない変化のマルチセンサ時系列データに横断的に、期待された発散又は収束を計算する
請求項19に記載の方法。
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US13/731,647 US9568461B2 (en) | 2012-12-31 | 2012-12-31 | Multi-modal fluid condition sensor platform and system therefor |
US13/731,647 | 2012-12-31 |
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CN (1) | CN103913559A (ja) |
AU (1) | AU2013270490B2 (ja) |
CA (1) | CA2835307A1 (ja) |
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Cited By (4)
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JP2018526614A (ja) * | 2015-04-27 | 2018-09-13 | バーチャル・フルイド・モニタリング・サービシズ,エルエルシー | 流体分析および監視のための、システム、装置、および方法 |
US10557776B2 (en) | 2015-04-27 | 2020-02-11 | Virtual Fluid Monitoring Services, Llc | Fluid analysis and monitoring using optical spectrometry |
WO2020045720A1 (ko) * | 2018-08-30 | 2020-03-05 | 엠텍비젼 주식회사 | 인공신경망을 이용한 멀티모달 센서 이상 감지 장치 및 방법 |
US11442019B2 (en) | 2018-06-19 | 2022-09-13 | Virtual Fluid Monitoring Services, Llc | Fluid analysis and monitoring using optical spectroscopy |
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DE102015009079A1 (de) | 2014-07-21 | 2016-01-21 | Hella Kgaa Hueck & Co. | Vorrichtung zur Bestimmung eines Füllstandes eines Fluides |
US10942046B2 (en) * | 2014-09-23 | 2021-03-09 | Infineon Technologies Ag | Sensor system using safety mechanism |
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US20140188407A1 (en) | 2014-07-03 |
AU2013270490B2 (en) | 2018-03-15 |
AU2013270490A1 (en) | 2014-07-17 |
EP2749853A2 (en) | 2014-07-02 |
EP2749853A3 (en) | 2017-02-08 |
HK1200036A1 (en) | 2015-07-31 |
WO2014143812A2 (en) | 2014-09-18 |
WO2014143812A3 (en) | 2014-11-20 |
CA2835307A1 (en) | 2014-06-30 |
US20140188404A1 (en) | 2014-07-03 |
US9568461B2 (en) | 2017-02-14 |
US9651538B2 (en) | 2017-05-16 |
CN103913559A (zh) | 2014-07-09 |
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