Abstract: Although Europe is on the threshold of a new industrial revolution called Industry 4.0, many believe that this will increase the flexibility of production, the mass adaptation of products to consumers and the speed of their service; it will also improve product quality and dramatically increase productivity. However, as expected, all the benefits of Industry 4.0 face many of the inevitable changes and challenges they pose. One of them is the inevitable transformation of current competition and business models. This article examines the possible results of competitive conversion from the classic Bertrand and Cournot models to qualitatively new competition based on innovation. Ability to deliver a new product quickly and the possibility to produce the individual design (through flexible and quickly configurable factories) by reducing equipment failures and increasing process automation and control is highly important. This study shows that the ongoing transformation of the competition model is changing the game. This, together with the creation of complex value networks, means huge investments that make it particularly difficult for small and medium-sized enterprises. In addition, the ongoing digitalization of data raises new concerns regarding legal obligations, intellectual property, and security.
Abstract: Sophisticated numerical simulations like finite element analysis (FEA) involve a complicated process from model setup to post-processing tasks that require replication of time-consuming steps. Utilizing FEA automation program simplifies the complexity of the involved steps while minimizing human errors in analysis set up, calculations, and results processing. One of the main challenges in designing FEA automation programs is to identify user requirements and link them to possible design alternatives. This paper presents a decision-making framework to design a Python based FEA automation program for modal analysis, frequency response analysis, and random vibration fatigue (RVF) analysis procedures. Analytical hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) are applied to evaluate design alternatives considering the feedback received from experts and program users.
Abstract: A need exists to identify the sources of risks associated with the process automation systems within petrochemical companies or similar energy related industries. These companies use many different process automation technologies in its value chain. A crucial part of the process automation system is the information technology component featuring in the supervisory control layer. The ever-changing technology within the process automation layers and the rate at which it advances pose a risk to safe and predictable automation system performance. The age of the automation equipment also provides challenges to the operations and maintenance managers of the plant due to obsolescence and unavailability of spare parts. The main objective of this research was to determine the risk sources associated with the equipment that is part of the process automation systems. A secondary objective was to establish whether technology managers and technicians were aware of the risks and share the same viewpoint on the importance of the risks associated with automation systems. A conceptual model for risk sources of automation systems was formulated from models and frameworks in literature. This model comprised six categories of risk which forms the basis for identifying specific risks. This model was used to develop a questionnaire that was sent to 172 instrument technicians and technology managers in the company to obtain primary data. 75 completed and useful responses were received. These responses were analyzed statistically to determine the highest risk sources and to determine whether there was difference in opinion between technology managers and technicians. The most important risks that were revealed in this study are: 1) the lack of skilled technicians, 2) integration capability of third-party system software, 3) reliability of the process automation hardware, 4) excessive costs pertaining to performing maintenance and migrations on process automation systems, and 5) requirements of having third-party communication interfacing compatibility as well as real-time communication networks.
Abstract: Business process automation is an important task in an
enterprise business environment software development. The
requirements of processing acceleration and automation level of
enterprises are inherently different from one organization to another.
We present a methodology and system for automation of business
process management system architecture by multi-agent collaboration
based on SOA. Design layer processes are modeled in semantic
markup language for web services application. At the core of our
system is considering certain types of human tasks to their further
automation across over multiple platform environments. An
improved abnormality processing with model for automation of
BPMS architecture by multi-agent collaboration based on SOA is
introduced. Validating system for efficiency of process automation,
an application for educational knowledge base instance would also be
described.
Abstract: This paper presents an optimization of the hull
separation, i.e. transverse clearance. The main objective is to identify
the feasible speed ranges and find the optimum transverse clearance
considering the minimum wave-making resistance. The dimensions
and the weight of hardware systems installed in the catamaran
structured fuel cell powered USV (Unmanned Surface Vehicle) were
considered as constraints. As the CAE (Computer Aided Engineering)
platform FRIENDSHIP-Framework was used. The hull surface
modeling, DoE (Design of Experiment), Tangent search optimization,
tool integration and the process automation were performed by
FRIENDSHIP-Framework. The hydrodynamic result was evaluated
by XPAN the potential solver of SHIPFLOW.
Abstract: Rapid progress in process automation and tightening
quality standards result in a growing demand being placed on fault
detection and diagnostics methods to provide both speed and
reliability of motor quality testing. Doubly fed induction generators
are used mainly for wind energy conversion in MW power plants.
This paper presents a detection of an inter turn stator and an open
phase faults, in a doubly fed induction machine whose stator and
rotor are supplied by two pulse width modulation (PWM) inverters.
The method used in this article to detect these faults, is based on
Park-s Vector Approach, using a neural network.