Fuzzy reasoning spiking neural P system for fault diagnosis

Spiking neural P systems (SN P systems) have been well established as a novel class of distributed parallel computing models. Some features that SN P systems possess are attractive to fault diagnosis. However, handling fuzzy diagnosis knowledge and reasoning is required for many fault diagnosis applications. The lack of ability is a major problem of existing SN P systems when applying them to the fault diagnosis domain. Thus, we extend SN P systems by introducing some new ingredients (such as three types of neurons, fuzzy logic and new firing mechanism) and propose the fuzzy reasoning spiking neural P systems (FRSN P systems). The FRSN P systems are particularly suitable to model fuzzy production rules in a fuzzy diagnosis knowledge base and their reasoning process. Moreover, a parallel fuzzy reasoning algorithm based on FRSN P systems is developed according to neuron’s dynamic firing mechanism. Besides, a practical example of transformer fault diagnosis is used to demonstrate the feasibility and effectiveness of the proposed FRSN P systems in fault diagnosis problem.Ministerio de Ciencia e Innovación TIN2009–13192Junta de Andalucía P08-TIC-0420

Protection concepts in distribution networks with decentralised energy resources

Die stetig steigende Anbindung von dezentralen Energieerzeugern (DER) an Mittel- (MS) und Niederspannungsnetze (NS) fordert eine Analyse der bestehenden Netzschutzkonzepte. Die Beeinflussung der Netzschutzkonzepte ist abhängig davon, wie die DER an das Mittelspannungsnetz angebunden sind. Die vorliegende Arbeit konzentriert sich auf die Analyse von Beeinflussungen durch kleine DER, die an das Mittelspannungsnetz über einen Umrichter angebunden sind. Das erste Problem, das in dieser Arbeit untersucht ist, ist die Beeinflussung der unterschiedlichen Schutzalgorithmen durch hohe Anteile von Harmonischen. Diese werden verursacht durch die steigende Zahl elektrischer Geräte, sowohl auf der Verbraucherseite als auch auf der Seite der Energieerzeuger. Die Beeinflussung, entsprechend der Norm IEC 61000-3–2, wurde an unterschiedlichen Typen von Netzschutzsystemen untersucht. Die getesteten Distanzschutzalgorithmen basierten auf konventionellen Methoden zu Berechnung der Impedanz wie: SinusAlgorithmen, Algorithmen basierend auf der Leitungs-Differentialgleichung erster oder zweiter Ordnung, Filteralgorithmen für Berechnung komplexer Zeiger, und Algorithmen, die auf künstliche Intelligenz basieren, wie harmonisch aktivierte neuronale Netze. Die unterschiedlichen Typen von Netzschutzprinzipien, die untersucht wurden sind: Überstrom, Distanz und Differenzial. Einige Untersuchungen wurden auch im Netzschutzlabor der Universität durchgeführt. Bei beiden Tests konnte nachgewiesen werden, dass die heutigen state-of-the-art Netzschutzsysteme durch Harmonische entsprechend IEC 61000-3–2, praktisch nicht beeinflusst werden. Der zweite Problemkreis der in dieser Arbeit diskutiert wird sind die Anforderungen, welche die Anbindung von DER an das Netz, an moderne Netzschutzsysteme stellen. Einige Beispiele illustrieren die Lage der Energieversorgung der Zukunft und zeigen Selektivitätsprobleme auf, sollten nur konventionelle Netzschutzsysteme benutzt werden. In dieser Arbeit wird ein neues Schutzkonzept für Mittelspannungsnetze mit hohem Anteil an DER vorgestellt und analysiert. Das Konzept beruht auf der neuen Norm für „Substation Automatisation System - IEC 61850“ und einem Netzschutz-Managementsystem. Die Methode der zusätzlichen Signal-Einspeisung wurde ebenfalls vorgestellt. Die Basis eines effizienten Netzschutz-Managementsystems ist das Wissen vom Verhalten des Systems in normalen Betrieb und unter Fehlerbedingungen. Die Computer- und Internettechnologie, die moderne Kommunikation, der interdisziplinäre Datenaustausch stellen ganz neue Anforderungen an die Wissensbasis energietechnischer Ingenieure. Mit dem Ziel neue Medien in der Ingenieurausbildung einzusetzen ist, im Rahmen dieser Arbeit ein E-learning Kurs entwickelt worden. Dabei ermöglicht das Internet neue Methoden zur Wissensvermittlung zu entwickeln. Die Unabhängigkeit von Zeit und Ort, die große Anzahl von Lehrmöglichkeiten und die Online-Diskussionen sind nur einige zu nennende Vorteile. In dieser Arbeit ist die Idee zur Realisierung sowie Ergebnisse des E-learning Kurses im Bereich digitaler Netzschutztechnik, als Erweiterung der konventionellen Lehrveranstaltung präsentiert worden. Dieser Kurs wird den Studenten der Universität in einem speziell gestalteten Multimedialabor angeboten. Es besteht via Internet die Möglichkeit den Kurses z.B. zu Hause zur Wiederholung und Prüfungsvorbereitung nochmals zu bearbeiten.    The continuously rising implementation of DER in the distribution network requests analyses of the present network protection concepts. Depending on the type of connection to the network, the influences of the DER on the network protection systems vary. This dissertation concentrates on the analyses of the influence of implementation of small DER, which are connected to the network via an inverter. The first problem discussed in this dissertation is the influence of high level of harmonics on the protection devices. The rising implementation of power electronic devices into the network, both on the side of the energy generation and energy consumption, leads to a high level of injected harmonics into the network. The influence of a high amount of harmonics, according to the Standard IEC 61000-3–2, on different types of algorithms implemented in different types of protection devices was investigated using a test network. The tested algorithms implemented in the distance protection devices were based on conventional methods such as steady state algorithms, algorithms using the differential equation of first or second order written for the protected line, algorithms based on the filter approach, and on the “new” methods using artificial intelligence i.e.: parametrical estimation and harmonic activated neuronal networks. The different types of protection devices that were investigated were based on the principle of over-current (definite-current and inverse time), distance and differential. Some of the tests were conducted in the protection technique laboratory at the university. From both tests (simulation and practical) it is concluded that the state-of-the-art protection devices are insensitive to harmonics according to the allowed level by the standard IEC 61000-3–2. The tendency of today’s protection technology engineers lies in searching for ways to shorten of the calculation time of the algorithms. The second problem discussed is the challenge set to the network protection systems in the distribution networks with implemented DER. A few examples illustrate the situation of the energy supply of the future illustrate the problems of lack of protection with the present protection concepts. In this sense, this work presents and analyses a protectionconcept in distribution networks with DER, using the substation automation system and the protection management system based on the new standard IEC 61850 for communication networks in substations. The method of using an additional signal injection as additional criteria for the presented network protection concept is also discussed. The basis for efficient protection system management is the knowledge of power system performance under fault and normal operation (service) conditions as well as the switchgear interfaces. This requires a proper knowledge of power system engineering. With a changeable power system infrastructure, the protection system management becomes a real challenge to the network protection experts. Computer- and internet technology, modern serial communications, sharing of data with other disciplines and a trend towards system engineering require a broader knowledge and close co-operation with others, beside the protection system engineers. With the goal of spreading the knowledge of network protection systems, in the frames of this work a special e-learning course was realised. The internet provides new possibilities for gaining and spreading knowledge. The time and place independence, the high amount of possibilities for knowledge sources and on line discussions are just a few of the possibilities. In this work, the idea, the realisation and the implementation of this new way of teaching and studying digital network protection alongside the conventional way are presented as well. An importance is also given to the feed back of the user of the e-learning course. This course is offered to the students at the university in a specially realised multimedia laboratory and used for gaining knowledge in the area of network protection technique. The possibility of using the course at home for re-capitulation of the taught material and for self-test is also possible, by simply logging on to the e-learning course. This course could also be used by engineers who want to refresh their knowledge in the form of a fast (self) training.   &nbsp

Design of software-oriented technician for vehicle’s fault system prediction using AdaBoost and random forest classifiers

Detecting and isolating faults on heavy duty vehicles is very important because it helps maintain high vehicle performance, low emissions, fuel economy, high vehicle safety and ensures repair and service efficiency. These factors are important because they help reduce the overall life cycle cost of a vehicle. The aim of this paper is to deliver a Web application model which aids the professional technician or vehicle user with basic automobile knowledge to access the working condition of the vehicles and detect the fault subsystem in the vehicles. The scope of this system is to visualize the data acquired from vehicle, diagnosis the fault component using trained fault model obtained from improvised Machine Learning (ML) classifiers and generate a report. The visualization page is built with plotly python package and prepared with selected parameter from On-board Diagnosis (OBD) tool data. The Histogram data is pre-processed with techniques such as null value Imputation techniques, Standardization and Balancing methods in order to increase the quality of training and it is trained with Classifiers. Finally, Classifier is tested and the Performance Metrics such as Accuracy, Precision, Re-call and F1 measure which are calculated from the Confusion Matrix. The proposed methodology for fault model prediction uses supervised algorithms such as Random Forest (RF), Ensemble Algorithm like AdaBoost Algorithm which offer reasonable Accuracy and Recall. The Python package joblib is used to save the model weights and reduce the computational time. Google Colabs is used as the python environment as it offers versatile features and PyCharm is utilised for the development of Web application. Hence, the Web application, outcome of this proposed work can, not only serve as the perfect companion to minimize the cost of time and money involved in unnecessary checks done for fault system detection but also aids to quickly detect and isolate the faulty system to avoid the propagation of errors that can lead to more dangerous cases

Fault diagnosis method of polymerization kettle equipment based on rough sets and BP neural network

Polyvinyl chloride (PVC) polymerizing production process is a typical complex controlled object, with complexity features, such as nonlinear, multivariable, strong coupling, and large time-delay. Aiming at the real-time fault diagnosis and optimized monitoring requirements of the large-scale key polymerization equipment of PVC production process, a real-time fault diagnosis strategy is proposed based on rough sets theory with the improved discernibility matrix and BP neural networks. The improved discernibility matrix is adopted to reduct the attributes of rough sets in order to decrease the input dimensionality of fault characteristics effectively. Levenberg-Marquardt BP neural network is trained to diagnose the polymerize faults according to the reducted decision table, which realizes the nonlinear mapping from fault symptom set to polymerize fault set. Simulation experiments are carried out combining with the industry history datum to show the effectiveness of the proposed rough set neural networks fault diagnosis method. The proposed strategy greatly increased the accuracy rate and efficiency of the polymerization fault diagnosis system

TEXTUAL DATA MINING FOR NEXT GENERATION INTELLIGENT DECISION MAKING IN INDUSTRIAL ENVIRONMENT: A SURVEY

This paper proposes textual data mining as a next generation intelligent decision making technology for sustainable knowledge management solutions in any industrial environment. A detailed survey of applications of Data Mining techniques for exploiting information from different data formats and transforming this information into knowledge is presented in the literature survey. The focus of the survey is to show the power of different data mining techniques for exploiting information from data. The literature surveyed in this paper shows that intelligent decision making is of great importance in many contexts within manufacturing, construction and business generally. Business intelligence tools, which can be interpreted as decision support tools, are of increasing importance to companies for their success within competitive global markets. However, these tools are dependent on the relevancy, accuracy and overall quality of the knowledge on which they are based and which they use. Thus the research work presented in the paper uncover the importance and power of different data mining techniques supported by text mining methods used to exploit information from semi-structured or un-structured data formats. A great source of information is available in these formats and when exploited by combined efforts of data and text mining tools help the decision maker to take effective decision for the enhancement of business of industry and discovery of useful knowledge is made for next generation of intelligent decision making. Thus the survey shows the power of textual data mining as the next generation technology for intelligent decision making in the industrial environment

Simultaneous-Fault Diagnosis of Gas Turbine Generator Systems Using a Pairwise-Coupled Probabilistic Classifier

A reliable fault diagnostic system for gas turbine generator system (GTGS), which is complicated and inherent with many types of component faults, is essential to avoid the interruption of electricity supply. However, the GTGS diagnosis faces challenges in terms of the existence of simultaneous-fault diagnosis and high cost in acquiring the exponentially increased simultaneous-fault vibration signals for constructing the diagnostic system. This research proposes a new diagnostic framework combining feature extraction, pairwise-coupled probabilistic classifier, and decision threshold optimization. The feature extraction module adopts wavelet packet transform and time-domain statistical features to extract vibration signal features. Kernel principal component analysis is then applied to further reduce the redundant features. The features of single faults in a simultaneous-fault pattern are extracted and then detected using a probabilistic classifier, namely, pairwise-coupled relevance vector machine, which is trained with single-fault patterns only. Therefore, the training dataset of simultaneous-fault patterns is unnecessary. To optimize the decision threshold, this research proposes to use grid search method which can ensure a global solution as compared with traditional computational intelligence techniques. Experimental results show that the proposed framework performs well for both single-fault and simultaneous-fault diagnosis and is superior to the frameworks without feature extraction and pairwise coupling

Automated Intelligent real-time system for aggregate classification

Traditionally, mechanical sieving and manual gauging are used to determine the quality of the aggregates. In order to obtain aggregates with better characteristics, it must pass a series of mechanical, chemical and physical tests which are often performed manually, and are slow, highly subjective and laborious. This research focuses on developing an intelligent real-time classification system called NeuralAgg which consists of 3 major subsystems namely the real-time machine vision, the intelligent classification and the database system. The image capturing system can send high quality images of moving aggregates to the image processing subsystem, and then to the intelligent system for shape classification using artificial neural network. Finally, the classification information is stored in the database system for data archive, which can be used for post analysis purposes. These 3 subsystems are integrated to work in real-time mode which takes an average of 1.23 s for a complete classification process. The system developed in this study has an accuracy of approximately 87% and has the potential to significantly reduce the processing and/or classification time and workload

Degradation modelling in process control applications

Degradation of industrial equipment is often influenced by how a system is operated, with certain operating points likely to accelerate degradation. The ability to mitigate degradation of an industrial system would result in improved performance and decreased costs of operation. The thesis aims to provide ways for managing degradation by adjusting the operating conditions of a system. The thesis provides original insights and a new classification of models of degradation to facilitate the integration of degradation models into process control applications. The thesis also develops an adaptive algorithm for degradation detection and prediction in turbomachinery, which is able to predict the expected future values of a degradation indicator and to quantify the uncertainty of the prediction. The thesis then proposes two frameworks for load-sharing in a compressor station in which the compressors are subject to degradation. One framework considers management of degradation and the other one focuses on power consumption of the whole station. These examples show how modelling of degradation can have an impact on the operation of an industrial system. The approaches have been evaluated with case studies developed in collaboration with industrial partners. As demonstrated in the case studies, the outcomes of the research presented in this thesis provide new ways to take account of degradation in process control applications. The thesis discusses steps and directions for future work to facilitate the technology transfer from academic to industrial implementation.Open Acces