A Numerical and Experimental Study Supporting a Methodology for Live Monitoring, Leak Detection, and Automatic Response in Water Pipelines

2019 ◽  
Author(s):  
Wadie Chalgham ◽  
Mihai Diaconeasa ◽  
Raju Gottumukkala ◽  
Abdennour Seibi
Keyword(s):  
Simulation Analysis ◽  
Leak Detection ◽  
Sensor Data ◽  
Structural Safety ◽  
Small Scale ◽  
Statistical Control ◽  
Time Data ◽  
Multiple Sensors ◽  
Pipeline Monitoring ◽  
Water Pipeline

Abstract This paper describes a numerical analysis supported by small scale experiments for demonstrating a monitoring and leak detection methodology. This study can be used to build a full-scale water pipeline monitoring and response system. The monitoring system is able to monitor the pipeline health and respond to hazard conditions through the use of multiple sensors driven by a hybrid rule-based and statistical monitoring control strategy. The system is programmed to automatically shut off its pump in the event of out-of-bounds/out-of-statistical control conditions detected by its sensors. In addition, this paper presents a numerical simulation analysis approach supported by an experiment that aims at finding a relationship between the location and size of an induced leak and the reported sensor data. The obtained results are used to inform a probabilistic model that can be used to estimate the leak location and size based on flow rate variations. The proposed project will enhance remote pipeline monitoring and structural safety by offering real-time data and automatic emergency response capabilities.

A Smart Pipeline Monitoring and Emergency Response System Using Web Services

2019 ◽  
Author(s):  
Wadie Chalgham ◽  
Mihai Diaconeasa ◽  
Khalid Elgazzar ◽  
Abdennour Seibi
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Data Visualization ◽  
Emergency Response ◽  
Structural Safety ◽  
Pipeline System ◽  
Statistical Control ◽  
Time Data ◽  
Real Time Data ◽  
Pipeline Monitoring

Abstract The Smart Pipeline Monitoring System introduced in this paper demonstrates a proof-of-concept for replicating a full-scale water pipeline system that is able to monitor the pipeline health and respond to hazard conditions through the use of multiple sensors and a statistical monitoring control strategy. The system aims at mitigating the effects of common sources of damage that occur in pipelines, such as leaks and overheating, by offering real time data visualization and autonomous actions in case of emergencies. The data visualization is provided by a desktop user interface and a mobile application. In the case of a detected anomaly, described by out-of-bounds/out-of-statistical control conditions detected by the sensors, the system is programmed to shut off its pump and alert the supervisors by SMS instantly. The proposed monitoring system will enhance remote pipeline monitoring and structural safety by offering real-time data and automatic emergency response capabilities. Our experimental results and prototype implementation show that the proposed system effectively detects anomaly conditions under various realistic scenarios and takes necessary safety measures to prevent further damages.

scholarly journals Deep Learning Based Data Fusion for Sensor Fault Diagnosis and Tolerance in Autonomous Vehicles

2020 ◽  
Author(s):  
Huihui Pan ◽  
Weichao Sun ◽  
Qiming Sun ◽  
Huijun Gao
Keyword(s):  
Fault Diagnosis ◽  
Data Fusion ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Environmental Perception ◽  
Sensor Data ◽  
Time Data ◽  
Multiple Sensors ◽  
Perception System

Abstract Environmental perception is one of the key technologies to realize autonomous vehicles. Autonomous vehicles are often equipped with multiple sensors to form a multi-source environmental perception system. Those sensors are very sensitive to light or background conditions, which will introduce a variety of global and local fault signals that bring great safety risks to autonomous driving system during long-term running. In this paper, a real-time data fusion network with fault diagnosis and fault tolerance mechanism is designed. By introducing prior features to realize the lightweight of the backbone network, the features of the input data can be extracted in real time accurately. Through the temporal and spatial correlation between sensor data, the sensor redundancy is utilized to diagnose the local and global condence of sensor data in real time, eliminate the fault data, and ensure the accuracy and reliability of data fusion. Experiments show that the network achieves the state-of-the-art results in speed and accuracy, and can accurately detect the location of the target when some sensors are out of focus or out of order.

scholarly journals Weather monitoring and forecasting system using IoT

2021 ◽  
Vol 8 (2) ◽  
pp. 008-016
Author(s):  
Balakrishnan Sivakumar ◽  
Chikkamadaiah Nanjundaswamy
Keyword(s):  
Weather Forecast ◽  
Cost Effective ◽  
Sensor Data ◽  
Solar Panels ◽  
Web Page ◽  
Time Data ◽  
Multiple Sensors ◽  
Wide Range ◽  
Compact Design ◽  
The Web

The system proposed is an advanced solution for weather monitoring that uses IoT to make its real time data easily accessible over a very wide range. The system deals with monitoring weather and climate changes like temperature, humidity, wind speed, moisture, light intensity, UV radiation and even carbon monoxide levels in the air; using multiple sensors. These sensors send the data to the web page and the sensor data is plotted as graphical statistics. The data uploaded to the web page can easily be accessible from anywhere in the world. The data gathered in these web pages can also be used for future references. The project even consists of an app that sends notifications as an effective alert system to warn people about sudden and drastic weather changes. For predicting more complex weather forecast that can’t be done by sensors alone we use an API that analyses the data collected by the sensors and predicts an accurate outcome. This API can be used to access the data anywhere and at any time with relative ease and can also be used to store data for future use. Due to the compact design and fewer moving parts this design requires less maintenance. The components in this project don’t consume much power and can even be powered by solar panels. Compared to other devices that are available in the market the Smart weather monitoring system is cheaper and cost effective. This project can be of great use to meteorological departments, weather stations, aviation and marine industries and even the agricultural industry.

scholarly journals Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion

2021 ◽  
Vol 4 (1) ◽  
pp. 3
Author(s):  
Parag Narkhede ◽  
Rahee Walambe ◽  
Shruti Mandaokar ◽  
Pulkit Chandel ◽  
Ketan Kotecha ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Network Architecture ◽  
Cost Effective ◽  
Sensor Data ◽  
Dense Layer ◽  
Gaseous Emissions ◽  
Multiple Sensors ◽  
Single Output ◽  
Novel Approach ◽  
Single Sensor

With the rapid industrialization and technological advancements, innovative engineering technologies which are cost effective, faster and easier to implement are essential. One such area of concern is the rising number of accidents happening due to gas leaks at coal mines, chemical industries, home appliances etc. In this paper we propose a novel approach to detect and identify the gaseous emissions using the multimodal AI fusion techniques. Most of the gases and their fumes are colorless, odorless, and tasteless, thereby challenging our normal human senses. Sensing based on a single sensor may not be accurate, and sensor fusion is essential for robust and reliable detection in several real-world applications. We manually collected 6400 gas samples (1600 samples per class for four classes) using two specific sensors: the 7-semiconductor gas sensors array, and a thermal camera. The early fusion method of multimodal AI, is applied The network architecture consists of a feature extraction module for individual modality, which is then fused using a merged layer followed by a dense layer, which provides a single output for identifying the gas. We obtained the testing accuracy of 96% (for fused model) as opposed to individual model accuracies of 82% (based on Gas Sensor data using LSTM) and 93% (based on thermal images data using CNN model). Results demonstrate that the fusion of multiple sensors and modalities outperforms the outcome of a single sensor.

scholarly journals BIM and IoT Sensors Integration: A Framework for Consumption and Indoor Conditions Data Monitoring of Existing Buildings

2021 ◽  
Vol 13 (8) ◽  
pp. 4496
Author(s):  
Giuseppe Desogus ◽  
Emanuela Quaquero ◽  
Giulia Rubiu ◽  
Gianluca Gatto ◽  
Cristian Perra
Keyword(s):  
Energy Consumption ◽  
Low Cost ◽  
Application Programming Interface ◽  
Visual Programming ◽  
Information Modeling ◽  
Sensor Data ◽  
Time Data ◽  
Existing Building ◽  
Building Maintenance ◽  
Indoor Conditions

The low accessibility to the information regarding buildings current performances causes deep difficulties in planning appropriate interventions. Internet of Things (IoT) sensors make available a high quantity of data on energy consumptions and indoor conditions of an existing building that can drive the choice of energy retrofit interventions. Moreover, the current developments in the topic of the digital twin are leading the diffusion of Building Information Modeling (BIM) methods and tools that can provide valid support to manage all data and information for the retrofit process. This paper shows the aim and the findings of research focused on testing the integrated use of BIM methodology and IoT systems. A common data platform for the visualization of building indoor conditions (e.g., temperature, luminance etc.) and of energy consumption parameters was carried out. This platform, tested on a case study located in Italy, is developed with the integration of low-cost IoT sensors and the Revit model. To obtain a dynamic and automated exchange of data between the sensors and the BIM model, the Revit software was integrated with the Dynamo visual programming platform and with a specific Application Programming Interface (API). It is an easy and straightforward tool that can provide building managers with real-time data and information about the energy consumption and the indoor conditions of buildings, but also allows for viewing of the historical sensor data table and creating graphical historical sensor data. Furthermore, the BIM model allows the management of other useful information about the building, such as dimensional data, functions, characteristics of the components of the building, maintenance status etc., which are essential for a much more conscious, effective and accurate management of the building and for defining the most suitable retrofit scenarios.

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