iEDS Intelligent Expert Diagnosis System

Technical parameter

• iEDS intelligent expert diagnosis system is a professional doctor for switchgear, which integrates existing diagnostic knowledge, diagnostic strategy analysis methods, industrial experts' experience, as well as a large number of test station fault data, to automatically realize intelligent diagnosis, service life prediction and operation & maintenance guidance of switchgear and to improve the operating stability and reliability of switchgear products in power grids. 

Features

• iEDS intelligent expert diagnosis system is to install various sensors on the switchgear to collect the temperature, current and operating curves of the switchgear. Besides, it integrates professional diagnostic methods and theories, comprehensively collects data from all aspects of switchgear and big data about fault of equipment in the switchgear industry, conducts real-time self-diagnosis and automatic fault identification of medium-voltage switchgear, timely analyzes potential faults of switchgear, and gives early warnings and provides effective solutions in the early stage of defects, so as to effectively prevent and resolve further development of potential faults and to greatly reduce the probability of unexpected power outages. 
• In addition, for the faults that have occurred, the expert diagnosis system can accurately analyze the fault cause and the most possible fault location, provide effective solutions, reduce the times and cost of hiring experts to resolve the fault, quickly remove the fault and restore power. 
• The expert diagnosis system can push the diagnosis conclusion and operation & maintenance strategy to the client side by IoT communication technology to achieve integrated remote monitoring and diagnosis, so as to reduce the technical requirements for fault diagnosis, prevent equipment faults and extend the service life of equipment. The expert diagnosis system can also update the fault database information by self-learning method, so as to promote the accuracy of fault analysis, health monitoring and service life prediction.