A real-time early warning seismic event detection algorithm using smart geo-spatial bi-axial inclinometer nodes for Industry 4.0 applications

Earthquakes are one of the major natural calamities as well as a prime subject of interest for seismologists, state agencies, and ground motion instrumentation scientists. The real-time data analysis of multi-sensor instrumentation is a valuable knowledge repository for real-time early warning and trustworthy seismic events detection. In this work, an early warning in the first 1 micro-second and seismic wave detection in the first 1.7 milliseconds after event initialization is proposed using a seismic wave event detection algorithm (SWEDA). The SWEDA with nine low-computation-cost operations is being proposed for smart geospatial bi-axial inclinometer nodes (SGBINs) also utilized in structural health monitoring systems. SWEDA detects four types of seismic waves, i.e., primary (P) or compression, secondary (S) or shear, Love (L), and Rayleigh (R) waves using time and frequency domain parameters mapped on a 2D mapping interpretation scheme. The SWEDA proved automated heterogeneous surface adaptability, multi-clustered sensing, ubiquitous monitoring with dynamic Savitzky-Golay filtering and detection using nine optimized sequential and structured event characterization techniques. Furthermore, situation-conscious (context-aware) and automated computation of short-time average over long-time average (STA/LTA) triggering parameters by peak-detection and run-time scaling arrays with manual computation support were achieved. - 2019 by the authors.Funding: This publication was made possible by the NPRP grant # 8-1781-2-725 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

A real-time early warning seismic event detection algorithm using smart geo-spatial bi-axial inclinometer nodes for Industry 4.0 applications

Earthquakes are one of the major natural calamities as well as a prime subject of interest for seismologists, state agencies, and ground motion instrumentation scientists. The real-time data analysis of multi-sensor instrumentation is a valuable knowledge repository for real-time early warning and trustworthy seismic events detection. In this work, an early warning in the first 1 micro-second and seismic wave detection in the first 1.7 milliseconds after event initialization is proposed using a seismic wave event detection algorithm (SWEDA). The SWEDA with nine low-computation-cost operations is being proposed for smart geospatial bi-axial inclinometer nodes (SGBINs) also utilized in structural health monitoring systems. SWEDA detects four types of seismic waves, i.e., primary (P) or compression, secondary (S) or shear, Love (L), and Rayleigh (R) waves using time and frequency domain parameters mapped on a 2D mapping interpretation scheme. The SWEDA proved automated heterogeneous surface adaptability, multi-clustered sensing, ubiquitous monitoring with dynamic Savitzky-Golay filtering and detection using nine optimized sequential and structured event characterization techniques. Furthermore, situation-conscious (context-aware) and automated computation of short-time average over long-time average (STA/LTA) triggering parameters by peak-detection and run-time scaling arrays with manual computation support were achieved. - 2019 by the authors.Funding: This publication was made possible by the NPRP grant # 8-1781-2-725 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

Geographical area network-structural health monitoring utility computing model

In view of intensified disasters and fatalities caused by natural phenomena and geographical expansion, there is a pressing need for a more effective environment logging for a better management and urban planning. This paper proposes a novel utility computing model (UCM) for structural health monitoring (SHM) that would enable dynamic planning of monitoring systems in an efficient and cost-effective manner in form of a SHM geo-informatics system. The proposed UCM consists of networked SHM systems that send geometrical SHM variables to SHM-UCM gateways. Every gateway is routing the data to SHM-UCM servers running a geo-spatial patch health assessment and prediction algorithm. The inputs of the prediction algorithm are geometrical variables, environmental variables, and payloads. The proposed SHM-UCM is unique in terms of its capability to manage heterogeneous SHM resources. This has been tested in a case study on Qatar University (QU) in Doha Qatar, where it looked at where SHM nodes are distributed along with occupancy density in each building. This information was taken from QU routers and zone calculation models and were then compared to ideal SHM system data. Results show the effectiveness of the proposed model in logging and dynamically planning SHM.This publication was made possible by NPRP grant # 8-1781-2-725 from the Qatar National Research Fund (a member of Qatar Foundation). The publication of this article was funded by the Qatar National Library

A smart rig for calibration of gas sensor nodes

Electrochemical gas sensors require regular maintenance to check and secure proper functioning. Standard procedures usually involve testing and recalibration of the sensors, for which working environments are needed. Periodic calibration is therefore necessary to ensure reliable and accurate measurements. This paper proposes a dedicated smart calibration rig with a set of novel features enabling simultaneous calibration of multiple sensors. The proposed calibration rig system comprises a gas mixing system, temperature control system, a test chamber, and a process-control PC that controls all calibration phases. The calibration process is automated by a LabVIEW-based platform that controls the calibration environment for the sensor nodes, logs sensor data, and best fit equation based on interpolation for every sensor on the node and uploads it to the sensor node for next deployments. The communication between the PC and the sensor nodes is performed using the same IEEE 802.15.4 (ZigBee) protocol that the nodes also use in field deployment for air quality measurement. The results presented demonstrate the effectiveness of the sensors calibration rig.Scopu

Comparative study of classical and fuzzy -regulator in five phase synchronous machine control with open phase

Speed control of synchronous machines using Field Oriented Control (FOC) classically uses Proportional Integral (PI) or Integral Proportional (IP) regulators that allow to achieve satisfactory goals on the dynamics of speed and torque. However, the performance deteriorates with loss of one or more phases in multiphase machines with IP regulator. This paper present comparison between the use of IP regulator and Fuzzy Logic Regulator (FLR) under same conditions applied to Five Phase Permanent Magnet Synchronous Machine (FPSM). First, modeling and performance of the FPSM are presented. In the beginning, the control is ensured in healthy mode with an IP regulator then in degraded mode when one then two phases are opened. The performances of the FLR are compared to IP ones. Better performance of FLR is established in terms of faster dynamics.This publication was made possible by internal grant # [QUCP-CENG-17/18-2] from the Qatar University. The statements made herein are solely the responsibility of the authors.Scopu

Design and implementation of information centered protocol for long haul SHM monitoring

In structural health monitoring systems (SHM), robust data transmission is the fundamental constraint. In this work, an information centered protocol is being proposed for multi-sensor and multi-variable communication channels in (SHM). The core objective is communication traffic optimization, data streams compression, bottleneck compensation for seamless information system. A novel SHM hierarchical information model has been designed and implemented using addressing taxonomy and domain definitions accumulated with data segments, beacons and flags-handshaking. On both ends of an SHM channel, a SQLite based encoding and decoding preprocessor is implemented, which requires the use of serial protocols such as CANopen, UART, 12C and SPI. Results have shown that the proposed system optimizes traffic monitoring in handling critical situations of dynamic baud rate switching.Scopu

Real-time gradient-aware indigenous AQI estimation IoT platform

Environmental monitoring has gained significant importance in outdoor air quality measurement and assessment for fundamental survival as well as ambient assisted living. In real-time outdoor urban scale, instantaneous air quality index estimation, the electrochemical sensors warm-up time, cross-sensitivity computation-error, geo-location typography, instantaneous capacity or back up time; and energy efficiency are the six major challenges. These challenges lead to real-time gradient anomalies that effect the accuracy and pro-longed lags in air quality index mapping campaigns for state and environmental/meteorological agencies. In this work, a gradient-aware, multi-variable air quality sensing node is proposed with event-triggered sensing based on position, gas magnitudes, and cross-sensitivity interpolation. In this approach, temperature, humidity, pressure, geo-position, photovoltaic power, volatile organic compounds, particulate matter (2.5), ozone, Carbon mono-oxide, Nitrogen dioxide, and Sulphur dioxide are the principle variables. Results have shown that the proposed system optimized the real-time air quality monitoring for the chosen geo-spatial cluster (Qatar University).Scopu

Optimal Consensus Time Synchronizations for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have received an important consideration in recent years regarding its broad area of applications. Time synchronization is one of the most important challenges of WSNs. This paper proposes a novel Optimal Distributed Consensus Time Synchronization (O-DCTS) for WSN. The proposed O-DCTS is based on some known distributed algorithms such as consensus with averaging and graph theory features. The algorithm, iteratively, updates the skew and the offset parameters of each sensor node's virtual clock. The skew compensation parameters and the offset compensation parameters are employed to update the virtual clock of each sensor node. The optimization of the proposed algorithm is applied on these two parameters, skew and offset, with respect to a minimization criterion. The convergence of the O-DCTS is studied using distance regular graphs.Scopu