ABNORMALITY DIAGNOSTIC DEVICE AND METHOD

PROBLEM TO BE SOLVED: To provide an abnormality diagnostic device capable of accurately diagnosing abnormality of a railway vehicle, and a method.SOLUTION: An abnormality diagnostic device relating to one embodiment comprises a learning section and a diagnostic section. The learning section learns d...

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description PROBLEM TO BE SOLVED: To provide an abnormality diagnostic device capable of accurately diagnosing abnormality of a railway vehicle, and a method.SOLUTION: An abnormality diagnostic device relating to one embodiment comprises a learning section and a diagnostic section. The learning section learns data selection conditions for selecting sensor data used for a diagnosis of a diagnostic object based on a model generated on the basis of the sensor data of the diagnostic object of a railway vehicle. The diagnostic section diagnoses abnormality of the diagnostic object based on the sensor data satisfying the data selection conditions and a diagnosis model obtained by modeling a relation between the sensor data and the abnormality of the diagnostic object.SELECTED DRAWING: Figure 1 【課題】鉄道車両の異常を精度よく診断できる異常診断装置及び方法を提供する。【解決手段】一実施形態に係る異常診断装置は、学習部と、診断部と、を備える。学習部は、鉄道車両における診断対象のセンサデータに基づいて生成されるモデルに基づいて、診断対象の診断に利用するセンサデータを選択するためのデータ選択条件を学習する。診断部は、データ選択条件を満たすセンサデータと、センサデータと診断対象の異常との関係をモデル化した診断モデルと、に基づいて、診断対象の異常を診断する。【選択図】図1
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The learning section learns data selection conditions for selecting sensor data used for a diagnosis of a diagnostic object based on a model generated on the basis of the sensor data of the diagnostic object of a railway vehicle. The diagnostic section diagnoses abnormality of the diagnostic object based on the sensor data satisfying the data selection conditions and a diagnosis model obtained by modeling a relation between the sensor data and the abnormality of the diagnostic object.SELECTED DRAWING: Figure 1 【課題】鉄道車両の異常を精度よく診断できる異常診断装置及び方法を提供する。【解決手段】一実施形態に係る異常診断装置は、学習部と、診断部と、を備える。学習部は、鉄道車両における診断対象のセンサデータに基づいて生成されるモデルに基づいて、診断対象の診断に利用するセンサデータを選択するためのデータ選択条件を学習する。診断部は、データ選択条件を満たすセンサデータと、センサデータと診断対象の異常との関係をモデル化した診断モデルと、に基づいて、診断対象の異常を診断する。【選択図】図1</description><language>eng ; jpn</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; CONTROL OR REGULATING SYSTEMS IN GENERAL ; CONTROLLING ; COUNTING ; ELECTRIC EQUIPMENT OR PROPULSION OF ELECTRICALLY-PROPELLEDVEHICLES ; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES, IN GENERAL ; ENSURING THE SAFETY OF RAILWAY TRAFFIC ; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS ; GUIDING RAILWAY TRAFFIC ; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES ; MEASURING ; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS ORELEMENTS ; PERFORMING OPERATIONS ; PHYSICS ; RAILWAYS ; REGULATING ; TESTING ; TESTING STATIC OR DYNAMIC BALANCE OF MACHINES ORSTRUCTURES ; TESTING STRUCTURES OR APPARATUS NOT OTHERWISE PROVIDED FOR ; TRANSPORTING ; VEHICLES IN GENERAL</subject><creationdate>2017</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20170622&amp;DB=EPODOC&amp;CC=JP&amp;NR=2017109650A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,778,883,25547,76298</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20170622&amp;DB=EPODOC&amp;CC=JP&amp;NR=2017109650A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>EZAWA TORU</creatorcontrib><title>ABNORMALITY DIAGNOSTIC DEVICE AND METHOD</title><description>PROBLEM TO BE SOLVED: To provide an abnormality diagnostic device capable of accurately diagnosing abnormality of a railway vehicle, and a method.SOLUTION: An abnormality diagnostic device relating to one embodiment comprises a learning section and a diagnostic section. 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The learning section learns data selection conditions for selecting sensor data used for a diagnosis of a diagnostic object based on a model generated on the basis of the sensor data of the diagnostic object of a railway vehicle. The diagnostic section diagnoses abnormality of the diagnostic object based on the sensor data satisfying the data selection conditions and a diagnosis model obtained by modeling a relation between the sensor data and the abnormality of the diagnostic object.SELECTED DRAWING: Figure 1 【課題】鉄道車両の異常を精度よく診断できる異常診断装置及び方法を提供する。【解決手段】一実施形態に係る異常診断装置は、学習部と、診断部と、を備える。学習部は、鉄道車両における診断対象のセンサデータに基づいて生成されるモデルに基づいて、診断対象の診断に利用するセンサデータを選択するためのデータ選択条件を学習する。診断部は、データ選択条件を満たすセンサデータと、センサデータと診断対象の異常との関係をモデル化した診断モデルと、に基づいて、診断対象の異常を診断する。【選択図】図1</abstract><oa>free_for_read</oa></addata></record>
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subjects CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
CONTROL OR REGULATING SYSTEMS IN GENERAL
CONTROLLING
COUNTING
ELECTRIC EQUIPMENT OR PROPULSION OF ELECTRICALLY-PROPELLEDVEHICLES
ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES, IN GENERAL
ENSURING THE SAFETY OF RAILWAY TRAFFIC
FUNCTIONAL ELEMENTS OF SUCH SYSTEMS
GUIDING RAILWAY TRAFFIC
MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES
MEASURING
MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS ORELEMENTS
PERFORMING OPERATIONS
PHYSICS
RAILWAYS
REGULATING
TESTING
TESTING STATIC OR DYNAMIC BALANCE OF MACHINES ORSTRUCTURES
TESTING STRUCTURES OR APPARATUS NOT OTHERWISE PROVIDED FOR
TRANSPORTING
VEHICLES IN GENERAL
title ABNORMALITY DIAGNOSTIC DEVICE AND METHOD
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