Our Company, aims at tracking huge plant parameters (Big Data), managed by Nuclear Power Plants. By application of this Big Data, along with NEC Corporation we developed a system to detect any equipment abnormalities accurately and as much as possible at an early stage. After the implementation of the system, checks of the actual working of the system can be conducted and performance gains enhanced drastically. ...
英字タイトル:
Development of the Plant Monitor System utilized Invariant Analysis Technology and Big Data

We have developed the prediction monitoring and diagnostic system for the purpose of the early detection of the abnormal behavior of the complicated system like a nuclear power plant. Furthermore, the techniques for the modeling of the relationships between sensors are applied to evaluate the system performance. The developed method is applied to the primary loop recirculation (PLR) pumps and the system performance indicator is calculated. The results are compared with the maintenance record of the PLR p...
英字タイトル:
Application of Prediction Monitoring and Diagnostic System to Plant Performance Evaluation

It is thought that we can let knowledge of "Human" substitute by building a function to utilize predictive information, and to extract a similar example from these documents in AI. In this way, we can largely reduce a burden on "Human" by building past accidents as a database, and being resemblance or equivalence, and extracting a cormorant from that. But we may list it by expression varying among document creators even if it is the same company. When we performed a document search with disregard to such a ...
英字タイトル:
Examination of a method to search past similar cases registered in NUCIA from predictive information detected by SIAT

System Invariant Analysis Technology, SIAT can detect abnormal behavior of plant parameters. However, the cause analysis functionality for the abnormal behavior has been strongly desired to support engineers making decisions for taking appropriate actions. For that purpose, we have developed the cause analysis functionality based on the result of the broken invariant analysis. Database of the broken invariant distribution with that cause is prepared and the similarity with the current abnormal behavior of t...
英字タイトル:
Development of the cause analysis functionality for the abnormal behavior detected by SIAT

A place to depend on an experienced of the knowledge of operation engineer and the maintenance engineer so-called experienced engineer about plant administration is big and, in us, will entrust it to "Human" from a process of the phenomenon identification to the last judgment of the result. Because a human resource is limited, we cannot depend on "Human" for all the judgments. Therefore, about keeping a function to substitute in [Human = experienced person] in AI namely SIAT and other AI system namely IBM W...
英字タイトル:
Advance of plant monitoring technology by cooperation of an system invariant analysis technology (SIAT) and IBM Watson Explorer

We applied NEC SIAT engine in cooperation with an NEC stock and developed the system which detected the abnormality of the plant early. This system was able to demonstrate that I could detect abnormality early by the inspection of conventional actual machine data. However, I depended on the experienced engineer about the details of the phenomenon, the cause. I applied Watson of IBM this time and developed a technique to study to the details of the phenomenon, a cause by letting you cooperate with an omen sy...
英字タイトル:
Times New Advancement of the plant monitoring technology by the cooperation of omen monitoring system (SIAT) and Watson

Our Company, aims at tracking huge plant parameters (Big Data), managed by Nuclear Power Plants. By application of this Big Data, along with NEC Corporation we developed a system to detect any equipment abnormalities accurately and as much as possible at an early stage. After the implementation of the system, checks of the actual working of the system can be conducted and performance gains enhanced drastically. ...