scholarly journals A Real-Time Early Warning Seismic Event Detection Algorithm Using Smart Geo-Spatial Bi-Axial Inclinometer Nodes for Industry 4.0 Applications

2019 ◽  
Vol 9 (18) ◽  
pp. 3650 ◽  
Author(s):  
Hasan Tariq ◽  
Farid Touati ◽  
Mohammed Abdulla E. Al-Hitmi ◽  
Damiano Crescini ◽  
Adel Ben Mnaouer
Keyword(s):  
Real Time ◽  
Seismic Wave ◽  
Early Warning ◽  
Event Detection ◽  
Seismic Waves ◽  
Detection Algorithm ◽  
Time Data ◽  
Time Scaling ◽  
Time Average ◽  
Events Detection

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.

Early Warning of Power Plant Equipment Based on Massive Real-Time Data Mining Technology

2014 ◽  
Vol 599-601 ◽  
pp. 1487-1490 ◽  
Author(s):  
Li Kun Zheng ◽  
Kun Feng ◽  
Xiao Qing Xiao ◽  
Wei Qiao Song
Keyword(s):  
Data Mining ◽  
Power Plant ◽  
Real Time ◽  
Early Warning ◽  
Time Data ◽  
Mining Technology ◽  
Equipment Safety ◽  
State Evaluation ◽  
Power Plant Equipment ◽  
Real Time Data

This paper mainly discusses the application of the mass real-time data mining technology in equipment safety state evaluation in the power plant and the realization of the equipment comprehensive quantitative assessment and early warning of potential failure by mining analysis and modeling massive amounts of real-time data the power equipment. In addition to the foundational technology introduced in this paper, the technology is also verified by the application case in the power supply side remote diagnosis center of Guangdong electric institute.

Review of Tools for the Prediction of Patient Deterioration in the Digital Hospital Setting. (Preprint)

2021 ◽  
Author(s):  
Kay Debby Mann ◽  
Norm Good ◽  
Farhad Fatehi ◽  
Sankalp Khanna ◽  
Victoria Campbell ◽  
...  
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Hospital Setting ◽  
Warning System ◽  
Scoring Systems ◽  
Time Data ◽  
Time Dynamic ◽  
Patient Deterioration ◽  
Dynamic Risk ◽  
Risk Estimates

BACKGROUND Early warning tools identify patients at risk of deterioration in hospitals. Electronic medical records in hospitals offer real-time data, and the opportunity to automate early warning tools and provide real-time, dynamic risk estimates. OBJECTIVE This review describes published studies on the development, validation and implementation of tools for prediction of patient deterioration in hospital general wards. METHODS An electronic database search of peer-reviewed journal papers 2008-2020 identified studies reporting the use of tools and algorithms for predicting patient deterioration - defined by unplanned transfer to intensive care unit (ICU), cardiac arrest, or death. Studies conducted solely in ICUs, emergency departments or on single diagnosis patient groups were excluded. RESULTS Forty-five publications, eligible for inclusion, were heterogeneous in design, setting and outcome measures. Most papers were retrospective studies utilizing cohort data to develop, validate or statistically evaluate prediction tools. Tools consisted of early warning, screening or scoring systems based on physiologic data, as well as more complex algorithms developed to better represent real-time, deal with complexities of longitudinal data and warn of deterioration risk earlier. Only a few studies detailed the results of implementation of the deterioration warning tools. CONCLUSIONS Despite relative progress on the development of algorithms to predict patient deterioration, the literature has not shown that the deployment or implementation of such algorithms is reproducibly associated with improvement of patient outcomes. Further work is needed to realise the potential of automated predictions and updating dynamic risk estimates as part of an operational early warning system for inpatient deterioration.

scholarly journals Near real-time GPS applications for tsunami early warning systems

2010 ◽  
Vol 10 (2) ◽  
pp. 181-189 ◽  
Author(s):  
C. Falck ◽  
M. Ramatschi ◽  
C. Subarya ◽  
M. Bartsch ◽  
A. Merx ◽  
...  
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Early Warning System ◽  
Tide Gauge ◽  
Warning System ◽  
Early Warning Systems ◽  
Time Data ◽  
Essential Information ◽  
Tsunami Early Warning ◽  
Land Mass

Abstract. GPS (Global Positioning System) technology is widely used for positioning applications. Many of them have high requirements with respect to precision, reliability or fast product delivery, but usually not all at the same time as it is the case for early warning applications. The tasks for the GPS-based components within the GITEWS project (German Indonesian Tsunami Early Warning System, Rudloff et al., 2009) are to support the determination of sea levels (measured onshore and offshore) and to detect co-seismic land mass displacements with the lowest possible latency (design goal: first reliable results after 5 min). The completed system was designed to fulfil these tasks in near real-time, rather than for scientific research requirements. The obtained data products (movements of GPS antennas) are supporting the warning process in different ways. The measurements from GPS instruments on buoys allow the earliest possible detection or confirmation of tsunami waves on the ocean. Onshore GPS measurements are made collocated with tide gauges or seismological stations and give information about co-seismic land mass movements as recorded, e.g., during the great Sumatra-Andaman earthquake of 2004 (Subarya et al., 2006). This information is important to separate tsunami-caused sea height movements from apparent sea height changes at tide gauge locations (sensor station movement) and also as additional information about earthquakes' mechanisms, as this is an essential information to predict a tsunami (Sobolev et al., 2007). This article gives an end-to-end overview of the GITEWS GPS-component system, from the GPS sensors (GPS receiver with GPS antenna and auxiliary systems, either onshore or offshore) to the early warning centre displays. We describe how the GPS sensors have been installed, how they are operated and the methods used to collect, transfer and process the GPS data in near real-time. This includes the sensor system design, the communication system layout with real-time data streaming, the data processing strategy and the final products of the GPS-based early warning system components.

scholarly journals Landslide Geohazard Monitoring, Early Warning and Stabilization Control Methods

2014 ◽  
Vol 36 (1) ◽  
pp. 3-13 ◽  
Author(s):  
Zbigniew Bednarczyk
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Warning System ◽  
Landslide Monitoring ◽  
High Plasticity ◽  
Monitoring Methods ◽  
Time Data ◽  
Stabilization Control ◽  
Starting Point ◽  
On Line

Abstract This paper is a presentation of landslide monitoring, early warning and remediation methods recommended for the Polish Carpathians. Instrumentation included standard and automatic on-line measurements with the real-time transfer of data to an Internet web server. The research was funded through EU Innovative Economy Programme and also by the SOPO Landslide Counteraction Project. The landslides investigated were characterized by relatively low rates of the displacements. These ranged from a few millimetres to several centimetres per year. Colluviums of clayey flysch deposits were of a soil-rock type with a very high plasticity and moisture content. The instrumentation consisted of 23 standard inclinometers set to depths of 5-21 m. The starting point of monitoring measurements was in January 2006. These were performed every 1-2 months over the period of 8 years. The measurements taken detected displacements from several millimetres to 40 cm set at a depth of 1-17 m. The modern, on-line monitoring and early warning system was installed in May 2010. The system is the first of its kind in Poland and only one of several such real-time systems in the world. The installation was working with the Local Road Authority in Gorlice. It contained three automatic field stations for investigation of landslide parameters to depths of 12-16 m and weather station. In-place tilt transducers and innovative 3D continuous inclinometer systems with sensors located every 0.5 m were used. It has the possibility of measuring a much greater range of movements compared to standard systems. The conventional and real-time data obtained provided a better recognition of the triggering parameters and the control of geohazard stabilizations. The monitoring methods chosen supplemented by numerical modelling could lead to more reliable forecasting of such landslides and could thus provide better control and landslide remediation possibilities also to stabilization works which prevent landslides.

scholarly journals Real-Time Characterization of Finite Rupture and Its Implication for Earthquake Early Warning: Application of FinDer to Existing and Planned Stations in Southwest China

2021 ◽  
Vol 9 ◽  
Author(s):  
Jiawei Li ◽  
Maren Böse ◽  
Yu Feng ◽  
Chen Yang
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Line Source ◽  
Average Distance ◽  
Strong Motion ◽  
Earthquake Early Warning ◽  
Time Data ◽  
Yunnan Region ◽  
Motion Time ◽  
Source Models

Earthquake early warning (EEW) not only improves resilience against the risk of earthquake disasters, but also provides new insights into seismological processes. The Finite-Fault Rupture Detector (FinDer) is an efficient algorithm to retrieve line-source models of an ongoing earthquake from seismic real-time data. In this study, we test the performance of FinDer in the Sichuan-Yunnan region (98.5oE–106.0oE, 22.0oN–34.0oN) of China for two datasets: the first consists of seismic broadband and strong-motion records of 58 earthquakes with 5.0 ≤ MS ≤ 8.0; the second comprises additional waveform simulations at sites where new stations will be deployed in the near future. We utilize observed waveforms to optimize the simulation approach to generate ground-motion time series. For both datasets the resulting FinDer line-source models agree well with the reported epicenters, focal mechanisms, and finite-source models, while they are computed faster compared to what traditional methods can achieve. Based on these outputs, we determine a theoretical relation that can predict for which magnitudes and station densities FinDer is expected to trigger, assuming that at least three neighboring stations must have recorded accelerations of 4.6 cm/s2 or more. We find that FinDer likely triggers and sends out a report, if the average distance between the epicenter and the three closest stations, Depi, is equal or smaller than log10 (Ma + b) + c, where a = 1.91, b = 5.93, and c = 2.34 for M = MW ≥ 4.8, and c = 2.49 for M = MS ≥ 5.0, respectively. If the data used in this study had been available in real-time, 40–70% of sites experiencing seismic intensities of V-VIII (on both Chinese and MMI scales) and 20% experiencing IX-X could have been issued a warning 5–10 s before the S-wave arrives. Our offline tests provide a useful reference for the planned installation of FinDer in the nationwide EEW system of Chinese mainland.

Automated Drilling Variances Detection Through Smart Alarms System

2021 ◽  
Author(s):  
Lucian Toader ◽  
Paulinus Abhyudaya Bimastianto ◽  
Shreepad Purushottam Khambete ◽  
Suhail Mohammed Al Ameri ◽  
Erwan Couzigou ◽  
...  
Keyword(s):  
Real Time ◽  
Event Detection ◽  
Detection Algorithm ◽  
Physical Models ◽  
Critical Parameters ◽  
Sensor Calibration ◽  
Residual Pressure ◽  
Drilling Process ◽  
Real Time Analysis ◽  
Deterministic Modelling

Abstract In a drive to enhance drilling operational awareness, the Real-Time Operations Center (RTOC) has developed a State-of-the-Art event detection algorithm that consistently highlights the deviations of critical parameters by actively comparing real-time values against comprehensive physical models and alerting the users through a dashboard. The process relies on different levels of frequency and severity in order to detect events at their onset and prevent developing into a situation that compromises the operations. The first pillar of the solution consists of deterministic modelling of the expected values for a series of parameters in order to provide the basis for comparison and diagnostics. The main parameters sought to be modelled consist of the Standpipe Pressure, the Rotary Torque and the Hook load, which respectively are generated through individual methods taking into consideration actual conditions as well as relevant contextual data to ensure accuracy. The second pillar of the solution consists of visual alerts, triggered and displayed on a dashboard based on frequency and severity levels, as percentage of deviation from accepted operational envelope. The solution has been initially implemented during drilling operations where different issues were expected to take place, finding that whenever such occurrences took place, the algorithms were able to signal potential events in most of the cases. Some challenges were observed mainly due to sensor calibration and behavior since the expected model values not necessarily match reality, including residual pressure when the pumps are off or when the string is set on slips but the hook load values still present some variance. Also, it has been observed during transient periods where flow and rotation are changed drastically, that the stabilization to a steady state present with high variance, which has demanded the introduction of further logics within the algorithms to account for these effects and avoid the generation of false indications of issues. The solution has given encouraging results thus far in signaling different dysfunctions on the drilling process without the need of immediate human interpretation of data, which has allowed to move forward in the digitalization of operations, not only by timely signaling the onset of issues, but as well by providing the basis to further develop real time diagnosis of the problems to accelerate their resolution. The conception of the event detection based on deterministic real time analysis of individual channels against robust physical models from the existing digital twin solution has proven an immediate asset for operations on its own. By providing clear signaling of issues, while providing a solid framework to ultimately develop a diagnostic solution to translate a potential event into a proactive approach to support decision making process.

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