The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Jul. 04, 2017
Filed:
Sep. 09, 2009
Nobuhiko Nishimura, Nagasaki, JP;
Fumitoshi Sakata, Tokyo, JP;
Mayumi Saito, Takasago, JP;
Kouji Satake, Takasago, JP;
Shintaro Kumano, Takasago, JP;
Nobuhiko Nishimura, Nagasaki, JP;
Fumitoshi Sakata, Tokyo, JP;
Mayumi Saito, Takasago, JP;
Kouji Satake, Takasago, JP;
Shintaro Kumano, Takasago, JP;
MITSUBISHI HEAVY INDUSTRIES, LTD., Tokyo, JP;
Abstract
A method for trouble managing in equipment is provided, with which optimal timing of repairing the equipment and occurrence of malfunction probable to occur concurrently with present malfunction or later stage can be inferred with sufficient accuracy, and which can be adopted for large-scale equipment used in a plant. The method for trouble managing of equipment by monitoring operation condition of the equipment with a monitoring means and inferring cause of malfunction of the equipment by an inference means which infers the cause of the malfunction using measured data concerning the operation condition obtained by the monitoring means when malfunctions occur as nodes of the inference means, comprises selecting acoustic data most similar to sound emitted from the equipment in which malfunction has occurred from among a plurality of acoustic data provide beforehand, selecting morphologic data most similar to a pattern of operating condition in the equipment from among a plurality of morphologic data provide beforehand, adding the selected acoustic data and the selected morphologic data to the nodes, and performing inference of cause of the malfunction of the equipment by a first Bayesian network base on the nodes.