Research on State Recognition Technology of Elevator Traction Machine Based on Modulation Feature Extraction

Li, Dongyang; Yang, Jianyi; Liu, Yong

doi:10.3390/s22239247

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…The condition monitoring and fault diagnosis of elevator traction machines is incredibly important for ensuring the safety of elevator users. Li et al [ 7 ] proposed a new method for recognizing the state of a traction machine based on analyzing its vibration signals. To do this, they use a novel demodulation method that involves time-frequency analysis and principal component analysis.…”

Section: Overview Of Contributionmentioning

confidence: 99%

Efficient Intelligence with Applications in Embedded Sensing

Zuo

Liu

2023

Sensors

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Section: Overview Of Contributionmentioning

confidence: 99%

Efficient Intelligence with Applications in Embedded Sensing

Zuo

Liu

2023

Sensors

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“…Liu Y. et al [ 11 ] used a new demodulation method based on time–frequency analysis and principal component analysis (DPCA) to extract periodic modulated wave signals, which provided a new method for the rapid and effective condition monitoring of traction machinery. This method can be extended to detect other background disturbances and typical faults.…”

Section: Introductionmentioning

confidence: 99%

Traction Machine State Recognition Method Based on DPCA Algorithm and Convolution Neural Network

Yang

Pan

et al. 2023

Sensors

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It is important to improve the identification accuracy of the operating status of elevator traction machines. The distribution difference of the time-frequency signals utilized to identify operating circumstances is modest, making it difficult to extract features from the vibration signals of traction machines under various operating conditions, leading to low recognition accuracy. A novel method for identifying the operating status of traction machines based on signal demodulation method and convolutional neural network (CNN) is proposed. The original vibration time-frequency signals are demodulated by the demodulation method based on time-frequency analysis and principal component analysis (DPCA). Firstly, the signal demodulation method based on principal component analysis is used to extract the modulation features of the experimentally measured vibration signals. Then, The CNN is used for feature vector extraction, and the training model is obtained through multiple iterations to achieve automatic recognition of the running state. The experimental results show that the proposed method can effectively extract feature parameters under different states. The diagnostic accuracy is up to 96.94%, which is about 16.61% higher than conventional methods. It provides a feasible solution for identifying the operating status of elevator traction machines.

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Elevating Innovation: Unveiling the Twin Traction Method for a 50-Ton Load Capacity Elevator in Building and Construction Applications

Kim,

Jang

2024

Buildings

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Most commercial elevators for buildings exceeding four stories use a cable-driven traction system. Typically, a single traction machine operates by hoisting the main cable on a traction sheave, thus vertically transporting the elevator car through rotational motion of the sheave. This research introduces a groundbreaking advancement aimed at elevating loading capacity to an unprecedented 50 tons—the highest known in the world. The innovation involves the development of a twin traction system, wherein two traction machines collaborate to lift the elevator. This novel elevator system has demonstrated remarkable capabilities, showcasing the ability to transport up to 300 passengers in a single trip. The installation of this high-capacity elevator system has yielded substantial improvements in construction work efficiency and safety protocols, particularly in scenarios where cranes are traditionally used. The newly developed elevator could lift 50 tons of equipment 60 times a day, whereas the crane was limited to 8 times. The positive impact on labor is also noteworthy, with increased safety and health considerations, especially in adverse weather conditions. By eliminating the need for manual stair climbing, the well-being of the workforce is prioritized. Furthermore, the heightened productivity resulting from a significant reduction in wait times for conventional elevators is a key outcome of this transformative technology. This research not only unveils a groundbreaking twin traction system but also highlights its multifaceted features in enhancing efficiency, safety, and overall productivity in various industries.

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Research on State Recognition Technology of Elevator Traction Machine Based on Modulation Feature Extraction

Cited by 3 publications

References 28 publications

Efficient Intelligence with Applications in Embedded Sensing

Efficient Intelligence with Applications in Embedded Sensing

Traction Machine State Recognition Method Based on DPCA Algorithm and Convolution Neural Network

Elevating Innovation: Unveiling the Twin Traction Method for a 50-Ton Load Capacity Elevator in Building and Construction Applications

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