Graph-based prediction of missing KPIs through optimization and random forests for KPI systems

Key performance indicators (KPIs) are widely used to monitor and control the production in industry. On an aggregated level, often represented as graphs or interrelated KPI systems, a comprehensive overview is given. However, missing or inaccurate sensor data and KPIs, as well inconsistencies in KPI based management are a major hurdle disturbing operations. To counter the impact of such missing KPIs, we propose a value optimization based approach to reconstruct the values of missing KPIs within a KPI system. While the approach shows successful reconstruction in the case study, the value optimization can be sped up through a random forest prediction of the initial optimization set. Thus, the inclusion of previous knowledge about the system behavior proves beneficial and superior to the pure optimization based approach, as validated by both randomized and simulation-based measurement data

Разработка системы показателей управления проектной командной работой как социальным макротехнопакетом: результаты практических исследований

Рассмотрены вопросы, связанные с разработкой системы показателей управления проектной командной работой как социальным макротехнопакетом (СМПТ) при анализе и ликвидации чрезвычайных ситуаций различных типов. Приводятся принципы разработки эффективной системы ключевых показателе

Data-Driven Fault Detection and Reasoning for Industrial Monitoring

This open access book assesses the potential of data-driven methods in industrial process monitoring engineering. The process modeling, fault detection, classification, isolation, and reasoning are studied in detail. These methods can be used to improve the safety and reliability of industrial processes. Fault diagnosis, including fault detection and reasoning, has attracted engineers and scientists from various fields such as control, machinery, mathematics, and automation engineering. Combining the diagnosis algorithms and application cases, this book establishes a basic framework for this topic and implements various statistical analysis methods for process monitoring. This book is intended for senior undergraduate and graduate students who are interested in fault diagnosis technology, researchers investigating automation and industrial security, professional practitioners and engineers working on engineering modeling and data processing applications. This is an open access book

Data-Driven Fault Detection and Reasoning for Industrial Monitoring

This open access book assesses the potential of data-driven methods in industrial process monitoring engineering. The process modeling, fault detection, classification, isolation, and reasoning are studied in detail. These methods can be used to improve the safety and reliability of industrial processes. Fault diagnosis, including fault detection and reasoning, has attracted engineers and scientists from various fields such as control, machinery, mathematics, and automation engineering. Combining the diagnosis algorithms and application cases, this book establishes a basic framework for this topic and implements various statistical analysis methods for process monitoring. This book is intended for senior undergraduate and graduate students who are interested in fault diagnosis technology, researchers investigating automation and industrial security, professional practitioners and engineers working on engineering modeling and data processing applications. This is an open access book

Robust Adaptive Stabilization of Nonholonomic Mobile Robots with Bounded Disturbances

The stabilization problem of nonholonomic mobile robots with unknown system parameters and environmental disturbances is investigated in this paper. Considering the dynamic model and the kinematic model of mobile robots, the transverse function approach is adopted to construct an additional control parameter, so that the closed-loop system is not underactuated. Then the adaptive backstepping method and the parameter projection technique are applied to design the controller to stabilize the system. At last, simulation results demonstrate the effectiveness of our proposed controller schemes

Design of an integrated monitoring and optimal control system for supervisory operation of anaerobic digesters

EngD ThesisAnaerobic digestion with biogas production has both economic and environmental benefits. 25 % of all bioenergy in the future could potentially be sourced from biogas (Holm-Nielsen et al., 2009). Although anaerobic digesters have seen wide applicability, they typically perform below their optimum as a consequence of the complexity of the underlying process. This work involves the development of a generic advanced process control system for the optimisation of the performance of anaerobic digesters. There is a requirement for a configurable monitoring and optimisation system with associated sensors to optimise the production of biogas, combined with a degree of flexibility for quality and content of the digestate. Several analyses are conducted to establish the baseline performance of the four benchmarked sites. Significant findings are revealed which include lack of superior technology between the four varying processes, differing performance due to optimisation activities through increased monitoring and whole plant optimisation such as energy usage and production. Potential improvements are presented including increased monitoring and a reduction in the variability of key parameters such as thicker percentage dry solids (% DS), steady feed rate, and temperature. The lack of instrumentation in anaerobic digestion processes is a key bottleneck as sensors and analysers are necessary to reduce the uncertainty related to the initial conditions, kinetics and the input concentrations of the process. Without knowledge of the process conditions, the process is inevitably difficult to control. Financial gains that can be achieved through increased instrumentation were calculated to justify the business case for the need for process improvement. An instrumentation review is presented with the minimum and ideal instrumentation requirements for the AD process. Improved monitoring is achieved through soft sensor development for volatile solids (VS), an important variable that is currently only monitored offline. The inferential sensor is developed using data from an industrial process and compared with the results from a simulation study where feed flow and biogas production rate are used for modelling VS. This theme of improving monitoring with inferential sensors is continued with development of soft sensors with microbial data and data from different reactor designs

Dynamic Navigation Method with Multisubstations Based on Doppler Shift

The mobile terminals must be compensated for the Doppler effect in their moving communication. This special characteristic of mobile communication can be developed in some new applications. This paper proposes methods to realize mobile navigation calculation via Doppler shifts. It gives the theory of relationship between the motion parameters, like directions and speed, and frequency shifts caused by multibase stations. The simulation illustrates how to compute the movement parameters of numerical calculation and what should be care for the problem near angle 90 degree. It also gives an application with Google map and dynamical locating position and direction on a mobile phone by public wireless network. Given the simulation analysis and navigation test, the results show that this method has a good effect