Alarm flood reduction using multiple data sources

The introduction of distributed control systems in the process industry has increased the number of alarms per operator exponentially. Modern plants present a high level of interconnectivity due to steam recirculation, heat integration and the complex control systems installed in the plant. When there is a disturbance in the plant it spreads through its material, energy and information connections affecting the process variables on the path. The alarms associated to these process variables are triggered. The alarm messages may overload the operator in the control room, who will not be able to properly investigate each one of these alarms. This undesired situation is called an “alarm flood”. In such situations the operator might not be able to keep the plant within safe operation. The aim of this thesis is to reduce alarm flood periods in process plants. Consequential alarms coming from the same process abnormality are isolated and a causal alarm suggestion is given. The causal alarm in an alarm flood is the alarm associated to the asset originating the disturbance that caused the flood. Multiple information sources are used: an alarm log containing all past alarms messages, process data and a topology model of the plant. The alarm flood reduction is achieved with a combination of alarm log analysis, process data root-cause analysis and connectivity analysis. The research findings are implemented in a software tool that guides the user through the different steps of the method. Finally the applicability of the method is proved with an industrial case study

Report of the Topical Group on Top quark physics and heavy flavor production for Snowmass 2021

This report summarizes the work of the Energy Frontier Topical Group on EW Physics: Heavy flavor and top quark physics (EF03) of the 2021 Community Summer Study (Snowmass). It aims to highlight the physics potential of top-quark studies and heavy-flavor production processes (bottom and charm) at the HL-LHC and possible future hadron and lepton colliders and running scenarios

A Novel IMU Extrinsic Calibration Method for Mass Production Land Vehicles

Multi-modal sensor fusion has become ubiquitous in the field of vehicle motion estimation. Achieving a consistent sensor fusion in such a set-up demands the precise knowledge of the misalignments between the coordinate systems in which the different information sources are expressed. In ego-motion estimation, even sub-degree misalignment errors lead to serious performance degradation. The present work addresses the extrinsic calibration of a land vehicle equipped with standard production car sensors and an automotive-grade inertial measurement unit (IMU). Specifically, the article presents a method for the estimation of the misalignment between the IMU and vehicle coordinate systems, while considering the IMU biases. The estimation problem is treated as a joint state and parameter estimation problem, and solved using an adaptive estimator that relies on the IMU measurements, a dynamic single-track model as well as the suspension and odometry systems. Additionally, we show that the validity of the misalignment estimates can be assessed by identifying the misalignment between a high-precision INS/GNSS and the IMU and vehicle coordinate systems. The effectiveness of the proposed calibration procedure is demonstrated using real sensor data. The results show that estimation accuracies below 0.1 degrees can be achieved in spite of moderate variations in the manoeuvre execution

Alarm flood reduction using multiple data source

Consulta en la Biblioteca ETSI Industriales (Riunet)[EN] The introduction of distributed control systems in the process industry has increased the number of alarms per operator exponentially. Modern plants present a high level of interconnectivity due to steam recirculation, heat integration and the complex control systems installed in the plant. When there is a disturbance in the plant it spreads through its material, energy and information connections affecting the process variables on the path. The alarms associated to these process variables are triggered. The alarm messages may overload the operator in the control room, who will not be able to properly investigate each one of these alarms. This undesired situation is called an “alarm flood”. In such situations the operator might not be able to keep the plant within safe operation. The aim of this thesis is to reduce alarm flood periods in process plants. Consequential alarms coming from the same process abnormality are isolated and a causal alarm suggestion is given. The causal alarm in an alarm flood is the alarm associated to the asset originating the disturbance that caused the flood. Multiple information sources are used: an alarm log containing all past alarms messages, process data and a topology model of the plant. The alarm flood reduction is achieved with a combination of alarm log analysis, process data root-cause analysis and connectivity analysis. The research findings are implemented in a software tool that guides the user through the different steps of the method. Finally the applicability of the method is proved with an industrial case study.Rodrigo Marco, JV. (2015). Alarm flood reduction using multiple data source. http://hdl.handle.net/10251/55857.Archivo delegad

Experimental validation of vehicle velocity, attitude and IMU bias estimation

This paper proposes an estimation method for the three dimensional velocity, and the roll and pitch angles of a land vehicle. The usage of low-cost sensors is required for applications in production vehicles. The proposed estimation method uses a low-cost inertial measurement unit (IMU) of which the biases are estimated. A nonlinear system is used to create an observer for simultaneous state and parameter estimation. Experimental results show the potential of this approach in a real world environment

Experimental validation of vehicle velocity, attitude and IMU bias estimation

This paper proposes an estimation method for the three dimensional velocity, and the roll and pitch angles of a land vehicle. The usage of low-cost sensors is required for applications in production vehicles. The proposed estimation method uses a low-cost inertial measurement unit (IMU) of which the biases are estimated. A nonlinear system is used to create an observer for simultaneous state and parameter estimation. Experimental results show the potential of this approach in a real world environment

A Novel IMU Extrinsic Calibration Method for Mass Production Land Vehicles

Multi-modal sensor fusion has become ubiquitous in the field of vehicle motion estimation. Achieving a consistent sensor fusion in such a set-up demands the precise knowledge of the misalignments between the coordinate systems in which the different information sources are expressed. In ego-motion estimation, even sub-degree misalignment errors lead to serious performance degradation. The present work addresses the extrinsic calibration of a land vehicle equipped with standard production car sensors and an automotive-grade inertial measurement unit (IMU). Specifically, the article presents a method for the estimation of the misalignment between the IMU and vehicle coordinate systems, while considering the IMU biases. The estimation problem is treated as a joint state and parameter estimation problem, and solved using an adaptive estimator that relies on the IMU measurements, a dynamic single-track model as well as the suspension and odometry systems. Additionally, we show that the validity of the misalignment estimates can be assessed by identifying the misalignment between a high-precision INS/GNSS and the IMU and vehicle coordinate systems. The effectiveness of the proposed calibration procedure is demonstrated using real sensor data. The results show that estimation accuracies below 0.1 degrees can be achieved in spite of moderate variations in the manoeuvre execution.TU Berlin, Open-Access-Mittel – 202

Multi-modal sensor fusion for highly accurate vehicle motion state estimation

In the context of autonomous driving in urban environments accurate and reliable information about the vehicle motion is crucial. This article presents a multi-modal sensor fusion scheme that, based on standard production car sensors and an inertial measurement unit, estimates the three-dimensional vehicle velocity and attitude angles (pitch and roll). Moreover, in order to enhance the estimation accuracy, the scheme simultaneously estimates the gyroscope and accelerometer biases. The approach relies on a state-affine representation of a kinematic model with an additional measurement equation based on a single-track model. The sensor fusion scheme is built upon a recently proposed adaptive estimator, which allows a direct consideration of model uncertainties and sensor noise. In order to provide accurate estimates during collision avoidance manoeuvres, a measurement covariance adaptation is introduced, which reduces the influence of the single-track model when its information is superfluous. A validation using experimental data demonstrates the effectiveness of the method during both regular urban drives and collision avoidance manoeuvres

Global attitudes in the management of acute appendicitis during COVID-19 pandemic: ACIE Appy Study

Background: Surgical strategies are being adapted to face the COVID-19 pandemic. Recommendations on the management of acute appendicitis have been based on expert opinion, but very little evidence is available. This study addressed that dearth with a snapshot of worldwide approaches to appendicitis. Methods: The Association of Italian Surgeons in Europe designed an online survey to assess the current attitude of surgeons globally regarding the management of patients with acute appendicitis during the pandemic. Questions were divided into baseline information, hospital organization and screening, personal protective equipment, management and surgical approach, and patient presentation before versus during the pandemic. Results: Of 744 answers, 709 (from 66 countries) were complete and were included in the analysis. Most hospitals were treating both patients with and those without COVID. There was variation in screening indications and modality used, with chest X-ray plus molecular testing (PCR) being the commonest (19\ub78 per cent). Conservative management of complicated and uncomplicated appendicitis was used by 6\ub76 and 2\ub74 per cent respectively before, but 23\ub77 and 5\ub73 per cent, during the pandemic (both P < 0\ub7001). One-third changed their approach from laparoscopic to open surgery owing to the popular (but evidence-lacking) advice from expert groups during the initial phase of the pandemic. No agreement on how to filter surgical smoke plume during laparoscopy was identified. There was an overall reduction in the number of patients admitted with appendicitis and one-third felt that patients who did present had more severe appendicitis than they usually observe. Conclusion: Conservative management of mild appendicitis has been possible during the pandemic. The fact that some surgeons switched to open appendicectomy may reflect the poor guidelines that emanated in the early phase of SARS-CoV-2

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P &lt; 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)