METHOD FOR TROUBLE MANAGING OF EQUIPMENT

• US 20110046995A1
• Filed: 09/09/2009
• Published: 02/24/2011
• Est. Priority Date: 11/25/2008
• Status: Active Grant

• Current Assignee: Mitsubishi Heavy Industries, Ltd.
• Original Assignee: Mitsubishi Heavy Industries, Ltd.
• Inventors: Sakata, Fumitoshi, Satake, Kouji, Nishimura, Nobuhiko, Kumano, Shintaro, Saito, Mayumi
• Granted Patent: US 9,696,718 B2
• Time in Patent Office: Days
• US Class Current: N/A
• CPC Class Codes: G01H 17/00 Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups see provisionally also G01H1/00
• CPC Class Codes: G05B 23/0278 Qualitative, e.g. if-then rules; Fuzzy logic; Lookup tables; Symptomatic search; FMEA
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/0631 Resource planning, allocation, distributing or scheduling for enterprises or organisations
• CPC Class Codes: G06Q 10/20 Administration of product repair or maintenance
• CPC Class Codes: G06Q 50/04 Manufacturing
• CPC Class Codes: Y02P 90/30 Computing systems specially adapted for manufacturing

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.

Claims

1. A 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 as nodes of the inference means the measured data concerning the operation condition obtained by the monitoring means when malfunctions occur, said method comprising

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 malfunction in 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.

2. A method for trouble managing of equipment according to claim 1, wherein a second Bayesian network is established on correlation between inference of cause of past malfunction of the equipment and actual repair implemented responding to the inferred cause of the malfunction, and strategy to take measure against the cause of the malfunction which is inferred as highly probable to be the cause by the first Bayesian network is inferred by the second Bayesian network.

3. A method for trouble managing of equipment according to claim 1, wherein

expense required to repair the equipment or analogous equipment in a case appropriate action was not taken responding to inference of cause of malfunction in past times are corrected in a database together with said inferences,

probability of occurrence of malfunction which may occur if present malfunction is neglected is inferred by the first Bayesian network base on the database, and

risk cost is calculated by multiplying the inferred probability by the expense required to repair in a case appropriate action was not taken responding to inference of cause of malfunction.

4. A method for trouble managing of equipment according to claim 3, wherein the expense to repair the present malfunction is estimated and compared with the risk cost.

5. A method for trouble managing of equipment according to claim 1, wherein a time series data of causes of malfunction occurred in the equipment is established, the time series data is inputted to the first Bayesian network, and causes of malfunctions highly probable to occur next to the present malfunction and probabilities of their occurrence are inferred.