Method of estimating the lateral vibration of an elevator rope from the vertical vibration of a compensating sheave

Miura, Naruhisa; Matsuda, Takehisa; Higashi, Yoshiyuki; Ono, Hiroyuki; Nguyen, Xuan Thuan

doi:10.1080/13467581.2022.2086876

Cited by 1 publication

(2 citation statements)

References 22 publications

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“…The elevator starts to operate from the start angle. (19) The location of the end angle is marked B in Fig. 29 when the elevator stops.…”

Section: Dynamic Image Processingmentioning

confidence: 99%

“…(18) Although several methods of estimating the vibration displacement using image processing or laser displacement meters with an observer or a Kalan filter were developed, observing the vibration of the elevator cables in an elevator shaft is a difficult task because of the movement of the elevator car and the thinness of the cable as an observation target. (19) More importantly, the metal fatigue, along with abrasion, of the elevator cable may lead to the progressive loss of the metallic cross section. (20,21) Some features such as the roughened and pitted surfaces of the cables, reduced cable diameter, and broken wires can be seen.…”

Section: Introductionmentioning

confidence: 99%

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Detection of Elevator Cable Slippage Using Streaming Image Process Algorithm

Lin,

Peng

2024

Sensors and Materials

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Elevators are the most common transportation tool in modern buildings. To satisfy the safety requirement, it is mandatory to carry out regular maintenance in a certain period of time. Among maintenance items, the determination of the traction compartment cable slippage status is one of the greatest challenges in the elevator industry, but a simple and effective method is still lacking. Currently, in a real situation, the inspection accuracy relies considerably on suitable personnel experiences, and the inspection process is also time-consuming. Accordingly, we aim to develop an automatic streaming image process model for the detection of the elevator cable slipping distance. First, the geometric shape of an ellipse is established from a captured circle image. Second, the ellipse is averaged and segmented into 360° with a predefined label as a reference point. Third, the circumference formula is applied to the calculation of the cable slipping distance. Finally, the outcome of the arithmetic operation is sent to the cloud database through a graphical user interface (GUI) on the web for monitoring purposes. The experimental results reveal that the standard deviation is as small as 1.153 mm, and the measurement uncertainty is only 0.258 mm, demonstrating high accuracy, rapidity, and robustness.

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“…The elevator starts to operate from the start angle. (19) The location of the end angle is marked B in Fig. 29 when the elevator stops.…”

Section: Dynamic Image Processingmentioning

confidence: 99%