Study on remote monitoring system for landslide hazard based on wireless sensor network and its application

Gui, Yang; Tao, Zhigang; Wang, Changjun; Xing, Xie

doi:10.1007/s12404-011-0422-8

Cited by 17 publications

(6 citation statements)

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“…In order to enable the remote monitoring system, a certain level of situational awareness of products, processes and customers is required. Many smart technologies are used, such as RFID (radio frequency identification) (Zhou, Ling & Peng, 2007;Wang et al, 2011), sound/speech recognition system (Vacher et al, 2006), vibration sensor (Noh & Hong, 2011), wireless sensor network (WSN) (Gui et al, 2011), ZigBee (Cheng et al, 2012), image processing and motion detector (Takeda et al, 2010), amongst others. GE (General Electric), one of several high-profile companies, uses IoT capabilities in some of its products for remote monitoring purposes, including in aircraft engines to optimize fuel use (Michel, 2014).…”

Section: Machine Buildersmentioning

confidence: 99%

Cloud platforms for remote monitoring system: a comparative case study

Hao

Helo

Gunasekaran

2019

Production Planning & Control

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Currently, industrial companies are increasingly introducing services to extend their tangible products. Remote monitoring solutions are one of the most implemented services by machine builders to manage their relationship with customers and also improve their business performance in the digital manufacturing era. However, the conventional method of remote monitoring cannot fulfil distributed business environments. Therefore, new solutions are needed to enable remote connection in manufacturing. By reviewing recent literature and proposing new features for software which can be used for remote service and operations, this research paper introduces a remote monitoring system connecting into a central cloud-based system with edge computing network architecture, namely Cloud-based Remote Monitoring (CloudRM). This proposed CloudRM also has been implemented in two different case companies for analysis and evaluation from a value proposition and technical implementation point of view. It shows significant improvement of production management and measurement by using CloudRM.

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Section: Machine Buildersmentioning

confidence: 99%

Cloud platforms for remote monitoring system: a comparative case study

Hao

Helo

Gunasekaran

2019

Production Planning & Control

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“…Information communication technologies (ICTs), such as wireless communications, sensor networks, global positioning system (GPS), and cloud computing, have been implemented at open-pit-mining sites to facilitate real-time monitoring of the operating status and haulage information of equipment, as well as collection of equipment-tracking data on web servers [30][31][32][33][34][35][36][37][38]. Further, several In this study, an open-pit limestone mine installed with an ICT-based mine-safety-management system was used as the investigation location.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Pit Mines

Baek

Choi

2020

Applied Sciences

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This paper proposes a deep neural network (DNN)-based method for predicting ore production by truck-haulage systems in open-pit mines. The proposed method utilizes two DNN models that are designed to predict ore production during the morning and afternoon haulage sessions, respectively. The configuration of the input nodes of the DNN models is based on truck-haulage conditions and corresponding operation times. To verify the efficacy of the proposed method, training data for the DNN models were generated by processing packet data collected over the two-month period December 2018 to January 2019. Subsequently, following training under different hidden-layer conditions, it was observed that the prediction accuracy of morning ore production was highest when the number of hidden layers and number of corresponding nodes were four and 50, respectively. The corresponding values of the determination coefficient and mean absolute percentage error (MAPE) were 0.99% and 4.78%, respectively. Further, the prediction accuracy of afternoon ore production was highest when the number of hidden layers was four and the corresponding number of nodes was 50. This yielded determination coefficient and MAPE values of 0.99% and 5.26%, respectively. truck-dispatch techniques have been proposed to simulate truck systems using real-time data collected from mining sites. For example, Tan et al. [39] identified discrete haulage-operation events by analyzing historical GPS truck-tracking data and implemented a truck-dispatch simulation algorithm using the Arena ® software. Chaowasakoo et al. [40] developed a real-time truck-dispatch system that plans truck-haulage strategies using truck-shovel activity-time data obtained from GPS.However, providing a comprehensive and effective truck-haulage systems simulation algorithm is difficult. First, open-pit mines are characterized by frequent changes in loading points during operations along with the use of multiple equipment-movement paths, unlike underground mines [1]. Additionally, several environmental factors, such as the weather, rock characteristics, and road conditions, affect haulage operations [12,41]. Thus, it is difficult to design a simulation algorithm for truck-haulage systems in large open-pit mines, where ore loading and haulage operations are simultaneously performed at multiple locations. Moreover, several types of simulation algorithms can be developed for a haulage plan that changes in real time, and it is difficult to alter these algorithms depending on variations in the haulage plan. Therefore, it is necessary to develop a simulation strategy that understands not only the characteristics of truck-haulage systems but also the sequence of discrete haulage operations. Additionally, the simulation technique must accurately predict ore production and equipment utilization using equipment-tracking data previously accumulated without use of prior-knowledge-based algorithms.Deep learning methods have recently attracted increased research interest because they facilitate easy analysis ...

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“…Early slope failure monitoring can deliver valuable information with regards to its dynamics of landslide and could save lives and prevent damage of properties in case of slope failures (Vishal et al 2017, Gui et al 2011). Bláha and Müller (2008) suggested that geophysical methods are useful for initial stages of landslides monitoring.…”

Section: Introductionmentioning

confidence: 99%

A Physical and Numerical Based Model for Early Prediction of Landslides Using Wireless Sensor Network

Singh¹

2020

Preprint

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Landslides are a frequent and recurrent problem in hilly regions of India and predicting them is always a challenging task. In this paper, an attempt was made to deal with this problem using advanced physical and numerical modeling methods. Detailed understanding of the initial slope failures is very interesting, and challenging at the same time, in the design and development of wireless sensor network based on early warning of landslide monitoring. A small scale physical model was developed to assess the instability through a sensor network with variable rain fall intensity. This was achieved by increasing the simulated rain water flow intensity in different time spans (dry condition, at t=0 to t= 30 min, 0.5 mm/min at t=30 to t= 60 min, 0.75 mm/min at t=60 to t= 91 min and 1 mm/min at t=91 to t= 120 min). The water level and movement in the slope was recorded by rainfall sensor, vibration sensor, soil moisture sensor and a digital camera. The following changes were observed during the slope failure: a) movement of small particles at top of the slope; b) initial failure of medium size soil particle; c) scouring of soil mass; d) whole slope collapse. The obtained results clearly indicated the superiority and effectiveness of the proposed system in providing a factor of safety for the progressive slope.

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Study on remote monitoring system for landslide hazard based on wireless sensor network and its application

Cited by 17 publications

References 1 publication

Cloud platforms for remote monitoring system: a comparative case study

Cloud platforms for remote monitoring system: a comparative case study

Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Pit Mines

A Physical and Numerical Based Model for Early Prediction of Landslides Using Wireless Sensor Network

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