scholarly journals Development of prototype machine brake and car safety gear testers for traction lift systems

2021 ◽  
Vol 28 (1) ◽  
Author(s):  
Albert T P So ◽  
K H Lam ◽  
Andy C T Kong ◽  
Jeff H Tse

Machine brakes and safety gears of traction lifts are two critical components of the biggest safety concerns, as the main braking system and redundant fail-safe mechanism respectively stop and hold lift cars from unintended car movement, overspeed or under a free-fall condition during emergency. There are challenges of routine maintenance on how to continually verify the effectiveness of them to mitigate risk potentials of equipment failure. This pilot study intends to design, implement and test low-cost, non-intrusive and on-line prototype testers with the aid of sensors for real-time monitoring of critical parameters of these safety devices. Critical parameters that are measurable include real-time variations of brake lining temperature, brake solenoid current, vibrational patterns of brake arms and lift car, position of lift car, and the actuating status of safety gears. Critical parameters after processing include a newly defined energy of brake arm operation and estimated deceleration rate of a fully loaded car by safety gears operation on an unloaded car. Early signs of abnormalities of these two safety devices can be detected before they fail. Additionally, the prototype testers can facilitate more frequent unloaded full-speed testing of safety gears to verify the effectiveness. The proof-of-concept prototype could be a quick monitoring alternative to existing and new traction lift systems.

2020 ◽  
Vol 4 (3) ◽  
pp. 79 ◽  
Author(s):  
Zheng Wang ◽  
Runan Zhang ◽  
Patrick Keogh

Due to their flexibility, low cost and large working volume, 6-axis articulated industrial robots are increasingly being used for drilling, trimming and machining operations, especially in aerospace manufacturing. However, producing high quality components has demonstrated to be difficult, as a result of the inherent problems of robots, including low structural stiffness and low positional accuracy. These limit robotic machining to non-critical components and parts with low accuracy and surface finish requirements. Studies have been carried out to improve robotic machine capability, specifically positioning accuracy and vibration reduction. This study includes the description of the hardware, software and methodologies developed to compensate robot path errors in real time using a single three-degrees-of-freedom (DOF) laser tracker, as well as the experimental results with and without compensation. Performance tests conducted include ballbar dynamic path accuracy test, a series of drilling case studies and a machining test. The results demonstrate major improvements in path accuracy, hole position accuracy and hole quality, as well as increases in accuracy of a machined aluminum part.


Author(s):  
Gabriel de Almeida Souza ◽  
Larissa Barbosa ◽  
Glênio Ramalho ◽  
Alexandre Zuquete Guarato

2007 ◽  
Author(s):  
R. E. Crosbie ◽  
J. J. Zenor ◽  
R. Bednar ◽  
D. Word ◽  
N. G. Hingorani

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yong He ◽  
Hong Zeng ◽  
Yangyang Fan ◽  
Shuaisheng Ji ◽  
Jianjian Wu

In this paper, we proposed an approach to detect oilseed rape pests based on deep learning, which improves the mean average precision (mAP) to 77.14%; the result increased by 9.7% with the original model. We adopt this model to mobile platform to let every farmer able to use this program, which will diagnose pests in real time and provide suggestions on pest controlling. We designed an oilseed rape pest imaging database with 12 typical oilseed rape pests and compared the performance of five models, SSD w/Inception is chosen as the optimal model. Moreover, for the purpose of the high mAP, we have used data augmentation (DA) and added a dropout layer. The experiments are performed on the Android application we developed, and the result shows that our approach surpasses the original model obviously and is helpful for integrated pest management. This application has improved environmental adaptability, response speed, and accuracy by contrast with the past works and has the advantage of low cost and simple operation, which are suitable for the pest monitoring mission of drones and Internet of Things (IoT).


2021 ◽  
Vol 11 (11) ◽  
pp. 4940
Author(s):  
Jinsoo Kim ◽  
Jeongho Cho

The field of research related to video data has difficulty in extracting not only spatial but also temporal features and human action recognition (HAR) is a representative field of research that applies convolutional neural network (CNN) to video data. The performance for action recognition has improved, but owing to the complexity of the model, some still limitations to operation in real-time persist. Therefore, a lightweight CNN-based single-stream HAR model that can operate in real-time is proposed. The proposed model extracts spatial feature maps by applying CNN to the images that develop the video and uses the frame change rate of sequential images as time information. Spatial feature maps are weighted-averaged by frame change, transformed into spatiotemporal features, and input into multilayer perceptrons, which have a relatively lower complexity than other HAR models; thus, our method has high utility in a single embedded system connected to CCTV. The results of evaluating action recognition accuracy and data processing speed through challenging action recognition benchmark UCF-101 showed higher action recognition accuracy than the HAR model using long short-term memory with a small amount of video frames and confirmed the real-time operational possibility through fast data processing speed. In addition, the performance of the proposed weighted mean-based HAR model was verified by testing it in Jetson NANO to confirm the possibility of using it in low-cost GPU-based embedded systems.


Author(s):  
Cheyma BARKA ◽  
Hanen MESSAOUDI-ABID ◽  
Houda BEN ATTIA SETTHOM ◽  
Afef BENNANI-BEN ABDELGHANI ◽  
Ilhem SLAMA-BELKHODJA ◽  
...  

Author(s):  
Donya Khaledyan ◽  
Abdolah Amirany ◽  
Kian Jafari ◽  
Mohammad Hossein Moaiyeri ◽  
Abolfazl Zargari Khuzani ◽  
...  

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