Elevator ride comfort monitoring and evaluation using smartphones

Purpose The comfort of lift passengers has a significant effect on their general health condition as well as stress levels during travel. This study reports the results of vibration measurements taken during travel in a passenger lift. Methods Vibration signals were analyzed by the empirical mode decomposition method and the Hilbert transform. Results Selected modes from the Hilbert spectral analysis were compared with the resonance frequencies of human body organs (range 20-90 Hz) as well as with the resonance frequencies of lift components. Conclusion The use of Hilbert spectral analysis enables the isolation of individual signal components and the determination of the dominant frequency in the signal. This, in turn, allows for the isolation of raw vibration frequencies from the signal that are particularly significant for passenger comfort assessment (resonance frequencies of human body organs) and analysis of their occurrence.

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“…Each of the measured accelerations a i (t) ( Fig. 5) can be decoupled into the modal components a i 1 (t), a i 2 (t),…, a i m (t) [21,22]:…”

Section: Results Analysismentioning

confidence: 99%

Assessment of Lift Passenger Comfort by the Hilbert–Huang Transform

Szydło

Wolszczak

Longwic

et al. 2019

J. Vib. Eng. Technol.

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“…Combining smartphones with structural health monitoring not only can improve the possibility of public participation but also can greatly reduce the requirements of professional equipment. Zhao et al applied smartphones in the field of structural health monitoring. Their experimental results showed that various sensors embedded in the smartphone can meet the requirements of structural damage detection.…”

Section: Methodsmentioning

confidence: 99%

Bolt loosening angle detection technology using deep learning

Zhao

Zhang

Wang

2018

Struct Control Health Monit

Self Cite

102

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As an important part of the steel structure, the bolt damage will affect the safety of the structure and even cause severe accidents. However, it is difficult to detect the bolt loosening from the perspective of the conventional dynamics, due to the complex vibration characteristics of the bolt joints. In order to detect structural damage intuitively, machine vision has been introduced into the field of structural health monitoring. Therefore, this paper combines deep learning and machine vision to propose a bolt loosening angle detection technology. First, the data sets with bolts were collected and divided into training sets, validation sets, and test sets. Second, the data sets were trained using Single Shot MultiBox Detector. And the recognition accuracy of the model was evaluated, which can reach 0.914. Thereafter, the images obtained from different angles and lighting conditions were detected by the training model;the results showed that this method still has high recognition accuracy and meets the requirements of engineering. Finally, the training model was migrated to the smartphone to achieve quick and simple bolt loosening monitoring.KEYWORDS bolt loosening angle, deep learning, machine vision, smartphone, SSD | INTRODUCTIONWith the advancement of steel structures, it has developed rapidly in high-rise and supertall buildings. Compared with the concrete structure, the steel structure adopts steel plate instead of reinforced concrete, which has higher strength and better seismic performance. And its components can be prefabricated at the factory, which greatly reduces the construction period. As an important joint, bolts are widely used in steel structures. Thus, the bolt loosening damage not only affects the safety of the building, but it also causes severe accidents. The conventional structural health monitoring methods mainly collects acceleration, 1 displacement, 2 and strain. 3 Then these characteristics are analyzed through wavelet transform 4,5 and Bayesian transformation 6-8 to obtain the location and size of the damage. However, these methods rely on a large number of distributed sensors, and the feature extraction process need to intervention. The damage feature parameters directly determine the results of the damage detection. However, machine vision can complete damage monitoring from another perspective. Many damages on the structure can be detected more intuitively by vision. The bolt damage is mainly divided into rusty, shedding, and loosening, of which the most difficult to detect is loosening. To date, many researchers also used machine vision methods to monitor bolt loosening damage. For instance, Park et al. 9 proposed a bolt loosening angle detection method using Hough transformation, which detects bolt

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“…Zhang et al [11] measured the level of comfort during the elevator ride by measuring the acceleration on three directions axes x, y and z using sensors embedded in a smartphone. The elevator ride comfort level is evaluated based on the collected data, and the assessment results are uploaded to a structural health monitoring site.…”

Section: Related Workmentioning

confidence: 99%

Elevint: A Cloud-based Internet of Elevators

Aljadani¹,

Almutairi²,

Ghaleb³

et al. 2022

IJACSA

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With the significant growth of the number of highrise buildings nowadays, the dependence on elevators has also increased. The issue that faces elevator passengers in case of breakdowns is the long waiting time for the arrival of the maintenance engineers to perform the repair, as the process of reporting is done manually. The safety concern increases when people are trapped. Most state-of-the-art approaches detect faults without providing means to facilitate the communication between elevator owners, maintenance companies, and engineers or notify them in case of breakdowns. Moreover, none of the proposed fault detection solutions rely on rules specified by experts in the field. This paper aims at addressing these issues by proposing a system that manages, monitors, detects faults and informs users of any faults instantly by sending notifications. Specifically, the paper proposes a mobile application, Elevint, that is, cloud-based and exploits the Internet of Things (IoT) technology. Elevint provides real-time monitoring of elevator operating conditions collected from sensors. The data is then transferred to the cloud, where faults are detected by applying rules that compare the current conditions with severity levels determined by experts. In the case that a fault is detected, elevator owners and maintenance companies are automatically notified. Moreover, through Elevint, maintenance companies can assign engineers to repair the fault and elevator owners can view and re-schedule the engineer's visit if needed. Testing our system on an elevator model shows 98% accuracy. In future, we intend to test it on real elevators to verify its applicability in practice.

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Elevator ride comfort monitoring and evaluation using smartphones

Cited by 29 publications

References 22 publications

Assessment of Lift Passenger Comfort by the Hilbert–Huang Transform

Assessment of Lift Passenger Comfort by the Hilbert–Huang Transform

Bolt loosening angle detection technology using deep learning

Elevint: A Cloud-based Internet of Elevators

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