JP2014524094A - パターン・シーケンスを持つカーネル回帰モデリングを用いる監視システム - Google Patents
パターン・シーケンスを持つカーネル回帰モデリングを用いる監視システム Download PDFInfo
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Abstract
【解決手段】本システムは、オブジェクトの正常動作状態を示す基準データと、複数の入力パターン・アレイとを持つ経験的モデルを有する。各入力パターン・アレイは複数の時間順序の入力ベクトルを持ち、各入力ベクトルは、オブジェクトのカレント状態を示す複数のパラメータを表す複数の入力値を持つ。経験的モデルは、入力パターン・アレイ及び基準データを使用して入力値と基準データとの間の類似性測度を決定する計算に基づいて、複数の推定値を生成する。差計算モジュールが、複数の推定値を対応する複数の入力値と比較して、この比較の結果生じる値を、オブジェクトの状態を決定するために使用できるようにする。
【選択図】図1
Description
1)各端に境界を有するスカラー範囲内にあり、
2)2つのベクトルが同一である場合、境界の端の1つで1の値(又は、他の選択された値)を持ち、
3)スカラー範囲にわたって単調に変化し、また
4)2つのベクトルが同一になるように近づくにつれて増大する絶対値を持つ。
12 センサ
20 差計算モジュール
32 入力ベクトル
100 コンピュータ
104 記憶装置
106 フィルタ・モジュール
300 プロセス
Claims (41)
- オブジェクトの状態を決定するための監視システムであって、
当該監視システムは、オブジェクトの正常動作状態を示す基準データ、及び複数の入力パターン・アレイを持つ経験的モデルを有し、各々の入力パターン・アレイが複数の時間順序の入力ベクトルを持ち、各入力ベクトルは、オブジェクトのカレント状態を示す複数のパラメータを表す複数の入力値を持ち、
前記経験的モデルは、入力パターン・アレイ及び基準データを使用して入力値と基準データとの間の類似性測度を決定する計算に基づいて、複数の推定値を生成するように構成されており、
当該監視システムはまた、前記複数の推定値を対応する複数の入力値と比較して、この比較の結果生じる値を、オブジェクトの状態を決定するために使用できるようにする差計算モジュールを有している、監視システム。 - 前記複数の入力ベクトルの少なくとも1つは、複数の入力パターン・アレイ内にある、請求項1記載のシステム。
- 各入力パターン・アレイは、オブジェクトの動作状態を表す一シーケンスの入力ベクトルに沿った複数の時点を含む期間を表している、請求項1記載のシステム。
- 各入力パターン・アレイは、少なくとも1つの他の入力パターン・アレイの期間とは異なるが、該期間とオーバーラップする期間を表している、請求項3記載のシステム。
- 各入力パターン・アレイは、前記一シーケンスの入力ベクトルの内のカレント一次ベクトルであって、該入力パターン・アレイ内の最も最近の時点を表すカレント一次ベクトルを持っている、請求項3記載のシステム。
- 前記複数の入力ベクトルは、前記シーケンスに沿ってルック・バック窓を動かして、カレント一次ベクトルを選択し、次いで前記シーケンスに沿って前記一次ベクトルに続き且つ前記窓内に適合する1つ以上の相対的に古いベクトルを選択することによって、各入力パターン・アレイについて選択される、請求項5記載のシステム。
- 前記相対的に古いベクトルは前記シーケンスに沿って相次いで存在する、請求項6記載のシステム。
- 前記相対的に古いベクトルは前記シーケンスに沿って相次いで存在していない、請求項6記載のシステム。
- 入力パターン・アレイ内の複数の入力ベクトルによって表される複数の時点の間の時間間隔は一様である、請求項1記載のシステム。
- 入力パターン・アレイ内の複数の入力ベクトルによって表される複数の時点の間の時間間隔は均等でない、請求項1記載のシステム。
- 入力パターン・アレイ内の複数の入力ベクトルによって表される複数の時点の間の時間間隔は、より最近の複数の入力ベクトル間の間隔が同じ入力パターン内のより遅い複数の入力ベクトル間の間隔よりも短い、請求項1記載のシステム。
- 複数の入力パターン・アレイによって表される全持続時間が変化する、請求項1記載のシステム。
- 前記計算は下記の式に基づくものである、請求項1記載のシステム。
- 前記計算は下記の式に基づくものである、請求項1記載のシステム。
- 前記類似性測度は、入力値及び基準値が範囲の一端で同一であり且つ該範囲の別の端で何ら類似性を持たないことを示す境界のある範囲上の値である、請求項1記載のシステム。
- 前記基準データは少なくとも1つの習得逐次的パターン・マトリクスを有し、各習得逐次的パターン・マトリクスは複数の基準ベクトルを持ち、各基準ベクトルは複数のパラメータを表す複数の基準値を持つ、請求項1記載のシステム。
- 前記基準データは、習得逐次的パターン・マトリクスの3次元コレクションを形成する、請求項16記載のシステム。
- 前記3次元コレクションは、第1の次元に沿って複数のパラメータを表し、第2の次元に沿って複数の習得逐次的パターン・マトリクスを表し、また第3の次元に沿って複数の基準ベクトルによって表される時間を表している3つの次元によって規定される、請求項17記載のシステム。
- 各入力パターン・アレイが前記3次元コレクション内の各習得逐次的パターン・マトリクスと比較されて、各習得逐次的パターン・マトリクスについて類似性測度を決定する、請求項17記載のシステム。
- 各習得逐次的パターン・マトリクスは、複数の基準ベクトルによって表される複数の時点を含む期間を表しており、該期間は、3次元コレクション内の他の少なくとも幾つかの習得逐次的パターン・マトリクスの期間とは異なるが、該期間とオーバーラップしている、請求項17記載のシステム。
- 前記複数の習得逐次的パターン・マトリクスは、前記複数の入力パターン・アレイと同じ数のベクトルを持っている、請求項17記載のシステム。
- 前記複数の習得逐次的パターン・マトリクスは前記複数の入力パターン・アレイと同じ複数の時点を表している、請求項17記載のシステム。
- 前記複数の習得逐次的パターン・マトリクスの1つと前記複数の入力パターン・アレイの1つとの間の類似性は、それぞれのアレイ内に表されている他のベクトルの位置に対して同じベクトル位置を表す対応する基準及び入力値の間の類似性に基づいている、請求項17記載のシステム。
- 前記複数の習得逐次的パターン・マトリクスの1つと前記複数の入力パターン・アレイの1つとの間の類似性は、前記複数のアレイによって表されている各パラメータについて平均スカラー類似性値を計算することを有している、請求項17記載のシステム。
- 前記複数の入力パターン・アレイの少なくとも幾つかは、前記複数の習得逐次的パターン・マトリクスの少なくとも1つによって表される期間とは異なる期間を表す、請求項17記載のシステム。
- 前記複数の習得逐次的パターン・マトリクスによって表される期間が変化する、請求項17記載のシステム。
- 前記経験的モデルは、複数の推定値ベクトルを持つ推定値マトリクスの形式で複数の推定値を生成するように構成される、請求項1記載のシステム。
- 各推定値マトリクス内の複数の推定値ベクトルは、複数の入力パターン・アレイ内の前記複数の入力ベクトルによって表される複数の時点に対応する、請求項27記載のシステム。
- 複数の推定値マトリクス内の全ての推定値よりも少ない数の推定値を使用することにより、オブジェクトの状態を決定する、請求項27記載のシステム。
- 前記差計算モジュールは、オブジェクトの状態を決定するために各推定値マトリクス内の最も最近の時点を表し複数の推定値のみ使用する、請求項27記載のシステム。
- オブジェクトの状態を決定するために複数の推定値マトリクスにわたって表される各時点について単一の推定値ベクトルを供給することを有する、請求項27記載のシステム。
- 前記単一の推定値ベクトルを供給するために、
複数の推定値マトリクスにわたる時点において他の複数の推定値ベクトルに対して最大、最小又はメジアン値を持つ1つ以上の推定値を持つ推定値ベクトルを選択すること、
複数の推定値マトリクスにわたる時点における複数の推定値の平均、重み付き平均、又は重み付きノルムを計算すること、及び
複数の推定値マトリクスにわたる時点における他の複数の推定値ベクトルに対して対応する入力ベクトルに最大の類似性を持つ推定値ベクトルを選択すること、
の内の少なくとも1つを使用する、請求項31記載のシステム。 - 前記経験的モデルは、
最大、最小又はメジアン推定値を持つ各推定値マトリクス内の推定値ベクトル、
各マトリクスについて1つの推定値を形成するために前記推定値マトリクス内の複数の推定値の重み付き平均又は重み付きノルム、及び
それぞれの対応する入力ベクトルに最大の類似性を持つ前記推定値マトリクス内の推定値ベクトル
の内の少なくとも1つを使用することによって、オブジェクトの状態を決定するための推定値ベクトルを供給する、請求項27記載のシステム。 - 前記経験的モデルは、複数の入力パターン・アレイ内に表されていない複数のパラメータを表す習得逐次的パターン・マトリクスの3次元コレクションと共に計算に複数の重みとして複数の類似性測度を使用するように構成されている、請求項1記載のシステム。
- 複数の類似性測度を生成するために使用される基準データが、前記3次元コレクションによって表される全てのパラメータを表していない、請求項34記載のシステム。
- 前記3次元コレクションは、前記入力パターン・アレイによって表されていないパラメータのみを表している、請求項34記載のシステム。
- 前記3次元コレクションは、前記複数の入力パターン・アレイによって表されていない複数のパラメータ及び前記複数の入力パターン・アレイによって表されている複数のパラメータの両方を表している、請求項35記載のシステム。
- 各入力パターン・アレイについて複数の推定値を生成するためにどの基準データを使用すべきかを再決定する局在化モジュールを有している請求項1記載のシステム。
- 前記複数の推定値が、正常動作を表す対応する入力パターン・アレイを示しているとき、前記複数の入力パターン・アレイを前記基準データに付加するように構成された適応モジュールを有している請求項1記載のシステム。
- オブジェクトの状態を決定するための監視システムであって、
当該監視システムは、少なくとも1つのプロセッサによって演算される経験的モデルを有し、該経験的モデルは、オブジェクトの正常動作状態を示す基準データを、複数の習得逐次的パターン・マトリクスの形式で有し、各習得逐次的パターン・マトリクスは複数の基準ベクトルを持ち、各基準ベクトルは複数のパラメータを表す複数の基準値を持ち、
前記モデルはまた、オブジェクトのカレント状態を示す複数のパラメータを表す入力データを持ち、
前記経験的モデルは、入力データ及び習得逐次的パターン・マトリクスを用いて前記入力データと前記複数の基準マトリクス内の基準値との間の類似性測度を決定する計算に基づいて、複数の推定値を生成するように構成され、
当該監視システムはまた、前記複数の推定値を対応する複数の入力値と比較して、この比較の結果生じる値を、オブジェクトの状態を決定するために使用できるようにする差計算モジュールを有している、監視システム。 - オブジェクトの状態を決定するための監視システムであって、
当該監視システムは、オブジェクトの正常動作状態を示す基準データを持つ経験的モデルを有し、
前記経験的モデルはまた、オブジェクトのカレント状態を示す複数のパラメータを表す入力データを持ち、
前記経験的モデルは、入力データ及び基準データの両方を使用して入力データと基準データとの間の類似性測度を決定する計算に基づいて、複数の推定値を生成するように構成されており、これらの推定値は、複数の時間順序の推定値ベクトルを持つ推定値マトリクスの形式で生成され、各推定値ベクトルは複数のパラメータを表す複数の推定値を持ち、 また、当該監視システムは、複数の推定値マトリクスによって表される各期間についての少なくとも1つの推定値ベクトルを、入力データと比較して、この比較の結果生じる値を、オブジェクトの状態を決定するために使用できるようにする差計算モジュールを有している、監視システム。
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AU2012284497A1 (en) | 2014-01-30 |
CN103842922B (zh) | 2016-12-21 |
US20130024166A1 (en) | 2013-01-24 |
KR20140058501A (ko) | 2014-05-14 |
US8660980B2 (en) | 2014-02-25 |
CN103842922A (zh) | 2014-06-04 |
WO2013012534A1 (en) | 2013-01-24 |
AU2012284497B2 (en) | 2016-07-14 |
BR112014001227A2 (pt) | 2017-02-21 |
CA2841997A1 (en) | 2013-01-24 |
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