The reliability of an elevator system in a smart city is of great importance. This chapter develops a conceptual framework for the design and development of an automated online condition monitoring system for elevators (AOCMSE) using IoT techniques to avoid failures. The elevators are powered by the traction motors. Therefore, by placing vibration sensors at various locations within the traction motor, the vibration data can be acquired and converted to 2D grayscale images. Then, maximum response-based directional texture pattern (MRDTP) can be applied to those images which are an advanced method of feature extraction. The feature vectors can also be reduced in dimension using principal component analysis (PCA) and then given to extreme learning machine (ELM) for the classification of the faults to five categories. Thus, the failure of elevators and the consequences can be prevented by sending this detected fault information to the maintenance team.
Design and Development of a Nonintrusive Pressure Measurement System for Pipeline Monitoring
