36,639 research outputs found
Learning for Advanced Motion Control
Iterative Learning Control (ILC) can achieve perfect tracking performance for
mechatronic systems. The aim of this paper is to present an ILC design tutorial
for industrial mechatronic systems. First, a preliminary analysis reveals the
potential performance improvement of ILC prior to its actual implementation.
Second, a frequency domain approach is presented, where fast learning is
achieved through noncausal model inversion, and safe and robust learning is
achieved by employing a contraction mapping theorem in conjunction with
nonparametric frequency response functions. The approach is demonstrated on a
desktop printer. Finally, a detailed analysis of industrial motion systems
leads to several shortcomings that obstruct the widespread implementation of
ILC algorithms. An overview of recently developed algorithms, including
extensions using machine learning algorithms, is outlined that are aimed to
facilitate broad industrial deployment.Comment: 8 pages, 15 figures, IEEE 16th International Workshop on Advanced
Motion Control, 202
Multivariate KPI for energy management of cooling system in food industry
Within EU, the food industry is currently ranked among the energy-intensive sectors, mainly as a consequence of the cooling
system shareover the total energy demand.
As such, the definition of appropriate key performance indicators (KPI) for ammonia chillers can play a strategic role for the
efficient monitoring of the energy performance of the cooling systems.
The goal of this paper is to develop an appropriate management approach, to account for energy inefficiency of the single
compressors, and to identify the specific variables driving the performance outliers.
To this end, a new KPI is proposed which correlates the energy consumption and the different process variables. The construction
of the new indicator was carried out by means of multivariate statistical analysis, in particular using Kernel Partial Least Square
(KPLS).This method is able to evaluate the maximum correlation between dataset and energy consumption employing nonlinear
regression techniques.
The validity of the new KPI is discussed on a case study relevant to the cooling system of a frozen ready meals industry. The
assessment of the proposed metric is one against Specific Energy Consumption (SEC) like indicator, typically used in the context
of the Energy Management Systems
Evaluation of Cognitive Architectures for Cyber-Physical Production Systems
Cyber-physical production systems (CPPS) integrate physical and computational
resources due to increasingly available sensors and processing power. This
enables the usage of data, to create additional benefit, such as condition
monitoring or optimization. These capabilities can lead to cognition, such that
the system is able to adapt independently to changing circumstances by learning
from additional sensors information. Developing a reference architecture for
the design of CPPS and standardization of machines and software interfaces is
crucial to enable compatibility of data usage between different machine models
and vendors. This paper analysis existing reference architecture regarding
their cognitive abilities, based on requirements that are derived from three
different use cases. The results from the evaluation of the reference
architectures, which include two instances that stem from the field of
cognitive science, reveal a gap in the applicability of the architectures
regarding the generalizability and the level of abstraction. While reference
architectures from the field of automation are suitable to address use case
specific requirements, and do not address the general requirements, especially
w.r.t. adaptability, the examples from the field of cognitive science are well
usable to reach a high level of adaption and cognition. It is desirable to
merge advantages of both classes of architectures to address challenges in the
field of CPPS in Industrie 4.0
MLI: An API for Distributed Machine Learning
MLI is an Application Programming Interface designed to address the
challenges of building Machine Learn- ing algorithms in a distributed setting
based on data-centric computing. Its primary goal is to simplify the
development of high-performance, scalable, distributed algorithms. Our initial
results show that, relative to existing systems, this interface can be used to
build distributed implementations of a wide variety of common Machine Learning
algorithms with minimal complexity and highly competitive performance and
scalability
The role of socio-technical experiments in introducing sustainable Product-Service System innovations
This is the pre-print version of the chapter published in 2015 by Springer in the book “The Handbook of Service Innovation” (edited by Renu Agarwal, Willem Selen, Göran Roos and Roy Green).
The final publication is available at Springer via http://dx.doi.org/10.1007/978-1-4471-6590-3_18Product-Service System (PSS) innovations represent a promising approach to sustainability, but their implementation and diffusion are hindered by several cultural, corporate, and regulative barriers. Hence, an important challenge is not only to conceive sustainable PSS concepts, but also to understand how to manage, support, and orient the introduction and diffusion of these concepts. Building upon insights from transition studies (in particular, the concepts of Strategic Niche Management and Transition Management), and through an action research project, the chapter investigates the role of design in introducing sustainable radical service innovations. A key role is given to the implementation of socio-technical experiments, partially protected spaces where innovations can be incubated and tested, become more mature, and potentially favor the implementation and scaling up process
Data-driven Soft Sensors in the Process Industry
In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work
Data-based fault detection in chemical processes: Managing records with operator intervention and uncertain labels
Developing data-driven fault detection systems for chemical plants requires managing uncertain data labels and dynamic attributes due to operator-process interactions. Mislabeled data is a known problem in computer science that has received scarce attention from the process systems community. This work introduces and examines the effects of operator actions in records and labels, and the consequences in the development of detection models. Using a state space model, this work proposes an iterative relabeling scheme for retraining classifiers that continuously refines dynamic attributes and labels. Three case studies are presented: a reactor as a motivating example, flooding in a simulated de-Butanizer column, as a complex case, and foaming in an absorber as an industrial challenge. For the first case, detection accuracy is shown to increase by 14% while operating costs are reduced by 20%. Moreover, regarding the de-Butanizer column, the performance of the proposed strategy is shown to be 10% higher than the filtering strategy. Promising results are finally reported in regard of efficient strategies to deal with the presented problemPeer ReviewedPostprint (author's final draft
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