A Smart Robotic System for Industrial Plant Supervision

In today's chemical plants, human field operators perform frequent integrity checks to guarantee high safety standards, and thus are possibly the first to encounter dangerous operating conditions. To alleviate their task, we present a system consisting of an autonomously navigating robot integrated with various sensors and intelligent data processing. It is able to detect methane leaks and estimate its flow rate, detect more general gas anomalies, recognize oil films, localize sound sources and detect failure cases, map the environment in 3D, and navigate autonomously, employing recognition and avoidance of dynamic obstacles. We evaluate our system at a wastewater facility in full working conditions. Our results demonstrate that the system is able to robustly navigate the plant and provide useful information about critical operating conditions.Comment: Final submission for IEEE Sensors 202

Machine Learning for Health: Algorithm Auditing & Quality Control

Developers proposing new machine learning for health (ML4H) tools often pledge to match or even surpass the performance of existing tools, yet the reality is usually more complicated. Reliable deployment of ML4H to the real world is challenging as examples from diabetic retinopathy or Covid-19 screening show. We envision an integrated framework of algorithm auditing and quality control that provides a path towards the effective and reliable application of ML systems in healthcare. In this editorial, we give a summary of ongoing work towards that vision and announce a call for participation to the special issue Machine Learning for Health: Algorithm Auditing & Quality Control in this journal to advance the practice of ML4H auditing

Personale Erfolgsfaktoren von Innovationsmanagement und Innovationsfuehrung

Available from Bibliothek des Instituts fuer Weltwirtschaft, ZBW, Duesternbrook Weg 120, D-24105 Kiel A 196338 / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEDEGerman

Optimal Powertrain Design through a Virtual Development Process

The ever more stringent global CO2 and pollutant emission regulations imply that the optimization of conventional powertrains can only provide partial reductions in fleet emissions. Vehicle manufacturers are therefore responding by increasing the electrification of their powertrain portfolios. This in turn, results in higher levels of electrification of the individual powertrain units. The increase in electric power leads to a comprehensive range of possible technologies—from 48 V mild hybrids to pure electric concepts. The powertrain topology and the configuration of the electrical components of a hybrid powertrain play a decisive role in determining the overall efficiency when considering the individual market requirements. Different hybrid functions as well as performance and customer requirements are determined from statutory cycles and in customer operation. A virtual development chain that is based on MATLAB/Simulink then represents the steps for the identification, configuration, and evaluation of new electrified powertrains. The tool chain presented supports powertrain development through automated conceptualization, design, and evaluation of powertrain systems and their components. The outcome of the entire tool chain is a robust concept decision for future powertrains. Using this methodical and reproducible approach, future electrified powertrain concepts are identified

Aspekte des Real Time (Process Equipment) Performance Monitoring (RTPM)

Dem RTPM (Real-Time Performance Monitoring) wurde in den letzten Jahren in der Automatisierungstechnik immer mehr Beachtung geschenkt. Drei ausgewählte Aspekte des RPTM werden behandelt: Alarmanalyse, Reglerperformance und Stelleinrichtungen. Die Reduktion von Alarmmeldungen mit Hilfe einer Alarmanalyse wird mit Hilfe von Beispielen aus der Industrie veranschaulicht. Ziel einer Analyse ist die Identifikation von (1) falschen Alarmgrenzen, (2) Reglern, bei denen Störungen im Handbetrieb ausgeregelt werden, (3) Reglern, bei denen Betriebspunktänderungen im Handbetrieb ausgeführt werden, (4) Reglern mit Stellgrößen bei 0% oder 100%, (5) falschen Reglerparametern sowie (6) Fehlern in der Messtechnik, Antrieben, Klappen oder Ventilen. Die industrielle Anwendung der Überwachung der Reglerperformance wird anhand des in das Prozessautomatisierungssystem DeltaV von Emerson Process Management integrierten Softwareproduktes DeltaV Inspect erläutert. DeltaV überwacht und bewertet (1) die Bereichsüberschreitungen der Regelgrößen und der Stellsignale, (2) die Betriebsarten (Hand oder Automatik) und (3) die Regelungsgüte. Die Regelungsgüte wird bei einem konstanten Sollwert und stochastischen Störungen aus dem Unterschied zwischen der tatsächlichen und der theoretisch erreichbaren Varianz des Regelfehlers berechnet. Anstelle einer Korrelations- bzw. Regressionsanalyse wird die theoretisch erreichbare minimale Varianz aus der aktuellen Varianz des Regelfehlers und der Varianz der Abweichung der aufeinander folgenden Regelfehlerabtastwerte berechnet.In recent years attention to RTPM (Real-Time Performance Monitoring) has increased. Three aspects of RPTM are considered: alarm analysis, control loop performance and actuators. The reduction of alarms using alarm analysis is illustrated using industrial examples. The industrial application of the surveillance of the control loop performance is demonstrated with a standard tool of a distributed control system