Street Lamp Fault Diagnosis System Based on Extreme Learning Machine

Abstract

In view of the construction of urban lighting system needs a lot of manpower deployment, especially for its fault diagnosis problem management. This paper proposes a fault model detection and diagnosis subsystem based on the extreme learning machine for street lamps system. The subsystem is part of the event rule response system which is based on the complex event processing technology framework. The system can handle a large amount of sensor data, perform filtering, carry out complex data processing and decision making. The experimental results show that the proposed street lamp fault diagnosis system based on extreme learning machine can diagnose the street lamp fault effectively and respond to it automatically.