Wojciech Moczulski

Identification of dynamic diagnostic models with the use of methodology of knowledge discovery in databases

This paper deals with an approach to knowledge discovery in databases applied in order to identify a dynamic model of a real-existing machine. The problem considered within the paper is how to identify dynamic models suitable for model-based diagnosing of a physical object. A special attention is paid to identification on unsupervised way, while big databases collected by a SCADA system is handled. In the paper a method of identification of dynamic models of objects and processes is presented. The usefulness of the method in technical diagnostics are shown. The elaborated method of analysis of quantitative dynamic data is based on applications of accessible methods of knowledge discovery in databases. The essence of the method is to project values of considered set of attributes into the so-called multidimensional space of regressors. In order to select the subset of relevant features the genetic algorithm was used. Knowledge was induced using the support vector machines (SVM) method. The AIC measure as well as our own heuristic function were applied as evaluation criteria. The method was applied in a process of discovery of a model of changes of temperature of a pump. Within framework of the research, data gathered by means of an industrial system registering data on a peculiar object, which was deep-well pumping station, was analyzed.

On case-based control of dynamic industrial processes with the use of fuzzy representation

Abstract Model-based diagnostics and control of industrial processes has become even more popular nowadays. To this end, a model of the given (dynamic) process is needed. However, there are several objects involved in processes, which may both cause severe problems when they are subject to a model, thus making a model-based approach inapplicable. An example of such an object is an electric furnace for reduction of copper from slag. Research focused on creating an analytical model of this object was unsuccessful. Therefore, the authors proposed a new approach based on case-based reasoning (CBR). Knowledge among the system is carried by selected realizations of the dynamic non-stationary process. Signals and attributes measured in initial state of the process, during its course and at some control points, are represented using a special process-oriented method that allows reducing unnecessary information. A special fuzzy similarity measure was introduced for efficient search of resembling examples. Properties of both the system of data representation and similarity measure are controlled by means of multiple parameters. To assure sufficient performance of the CBR system, an evolutionary algorithm was efficiently used. The paper ends with some exemplary results obtained by means of software, which implements the developed methodology.

Methodology of neural modelling in fault detection with the use of chaos engineering

The paper deals with the problem of robust fault detection using recurrent neural networks and chaos engineering. The main part of the proposed approach is a locally recurrent neural network that is composed of complex dynamic neural units for which chaotic behaviour can be obtained. Selected global and local optimization methods are connected to have diphase strategies for training this kind of neural networks. And beyond this, chaos engineering is incorporated into both the evolutionary and simulated annealing algorithm in order to improve the efficiency of the tuning procedure. The problem of selecting the most relevant input variables is solved by means of extended Hellwig?s method of integral capacity of information. Criteria isolines and a sensitive-based method are used to identify the suitable architecture of a neural network. Moreover, the issue of stability analysis of neural models is also considered in this paper. Recurrence quantification analysis is proposed for residual evaluation in order to have the comprehensive methodology of neural model-based fault detection. The preliminary verification of the elaborated methodology in modelling tasks was carried out for both simulation and industrial data. The fundamental verification was conducted for the data made available within DAMADICS benchmark problem. The achieved results confirm the effectiveness of the proposed approach. Graphical abstractDisplay Omitted HighlightsNeural modelling using chaos engineering is proposed to be used for designing robust fault detection systems.The proposed locally recurrent neural network can be used to model chaotic or stochastic behaviours of a system.Recurrence quantification analysis is proposed for residual evaluation to have the comprehensive approach to fault detection.The capability of the proposed methodology is presented using simulation and industrial data.

Acquisition and Knowledge Representation in the Product Development Process with the Use of Augmented Reality

The authors present a method and system based on a method for aiding a designer by use of augmented reality techniques. The presented method uses Unified Modeling Language diagrams in acquisition and knowledge representation. The presented method allows the delivering of knowledge to the designer in both procedural and declarative representations. It is important that the method also use multimedia representation of knowledge, especially interactive animated 3D CAD models. Knowledge is delivered to the user by use of augmented reality techniques. AR is a technique for changing views of virtual data – especially 3D models – in a real environment. AR allows the user to understand the presented virtual data and knowledge in a more comprehensive way, thus making the design process more efficient than that presently supported by conventional present-day CAD systems.

Applications of augmented reality in machinery design, maintenance and diagnostics

The paper deals with technical applications of Augmented Reality (AR) technology. The discussion starts with the conception of AR. Further on, three applications connected with different stages of existence of a technical means (machinery or equipment) are presented. They address design process, maintenance and diagnostics of different objects. Finally, further research is outlined.

Augmented Reality for Machinery Systems Design and Development

The paper presents the application of augmented reality (AR) to support product design and development. The paper concentrates on a new engineering tool. By this reason design and its issues are presented in a very limited way. The goal of the research was to improve design process and reduce product development time. The presented system bases on CAD software which is integrated with an expert system and AR techniques as a human–computer communication interface. The authors present possibilities of using AR technology in CAD design with the hope that maybe someday it would become integral part of a standard design process of more reliable and durable machinery systems.

Mobile robot for inspecting ventilation ducts

The paper deals with a concept and design of a mobile robot capable of inspecting ventilation ducts made of steel sheet. The robot can operate in several modes including: autonomous, manual, and training one. Some subsystems are briefly outlined. Mobility of the robot is achieved by four wheels that include permanent magnets. There are 2 DOFs associated to each wheel. The detection system assesses the internal state of the robot and its subsystems, and perceives the surrounding environment, providing the control system with vital data that allows completing inspection tasks. The robot is also equipped with environment recognition system that collects data whose meaning is twofold. Primarily, it allows assessing actual condition of the ducts being inspected. Further on, the data makes possible creating plans of long-term movements that are required in order to complete the mission. The work of all these systems is coordinated by the control system. It is based on behaviors that are selected or combined by a neural controller. The controller is able to learn better behaviors from examples. These issues are discussed in details in other papers presented at this event.

Control System of the Mobile Robot TELERESCUER

This paper describes a Main control system of the mobile robot TELERESCUER for inspecting coal mine areas affected by catastrophic events. Carried out in a framework of a EU programme of the Research Fund for Coal and Steel under the grant agreement No. RFCR-CT-2014-00002. The main problem is to applicate the very demanding safe requirements of ATEX on the design of the control system including its communication with other robot subsystems. The solving of this problem is presented in this paper.

A methodology of leakage detection and location in water distribution networks — The case study

The paper deals with a methodology of detecting and localizing leakages in water distribution networks. Two approaches are presented: based on a hydraulic model of the network, and based on approximate models and fuzzy classifiers. Moreover, a case study concerning applications of the methodology to a real network in Rybnik, Poland is presented. A pilot version of the system has been running since 2013 and shows quite satisfactory efficiency.

Exploration Mobile Robot for Coal Mines

This paper describes a Main Control System (MCS) and 3D map subsystem of the mobile robot TELERESCUER for inspecting coal mine areas affected by catastrophic events. It is carried out in a framework of a EU programme of the Research Fund for Coal and Steel under the grant agreement No. RFCR-CT-2014-00002. The interesting problem is to applicate the very demanding safe requirements of ATEX on the design of the control system including its communication with other robot subsystems. Due to a very innovative and advanced HMI the system allows the rescuer to operate the robot as if he were at the very center point of the scene of the robot’s operation.6 This project is solved by consortium of: Silesian University of Technology (Poland), VSB – Technical University of Ostrava (Czech Republic), AITEMIN (Spain), SIMMERSION GMBH (Austria) and SKYTECH RESEARCH (Poland).