Yüksek LisansAçık Erişim

Iot application for fault diagnosis and prediction in elevators

2017
0 görüntülenme
0 i̇ndirme
Danışman: Prof. Dr. Erhan Akın

Özet (EN)

In this thesis an elevator monitoring system of rules based on installed system and IOT. Through multi-sensor data obtaining, much data can be gotten to, for example, running noise, vibration, direction, quickening, speed, warmth of the traction machine, floor stopping place, entryway turn of the lift car and power supply voltage, now, in the meantime the noise and the temperature of the PC stay with the data if there is anybody in the lift, etc. A wide area network association between the elevator parameter observing stations and the remote control focus is set up to understand the center on checking and automatic remote disappointment caution for the lift operation. The goal was a find error and detection. Monitoring air quality in elevator rooms to make more suitable and healthy has tremendously risen. Working at a sensor level, Network level, and Application level. Therefore, our system measures polluted the air in elevator room or closed environment. Such as temperature and humidity, at a certain level. The design which it connects each of sensors, network and application are called a Wireless Sensor Network. This application collects and reads the data from the sensors; it displays the readings and also notifies us whenever there is polluted air in the elevator room. We benefit from IP in order to connect the sensors with the computer application. The design provides a solution for reforming elevators by IOT. With the application of more advanced sensors, the real time running status of elevator can be sensed more detailed and comprehensively. The elevator monitoring alarm system that made full use of advanced sensing technology and combined with modern communication technology can transfer the information from many elevators in a certain area to the monitor computer.

Yazar

Omıd Saleem Saeed Saeed

Bu Yayına Nasıl Atıf Yapılır

Omıd Saleem Saeed Saeed (Master Thesis). Iot application for fault diagnosis and prediction in elevators, 2017, Fırat University.

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