Semantically defined Analytics for Industrial Equipment Diagnostics

In this age of digitalization, industries everywhere accumulate massive amount of data such that it has become the lifeblood of the global economy. This data may come from various heterogeneous systems, equipment, components, sensors, systems and applications in many varieties (diversity of sources), velocities (high rate of changes) and volumes (sheer data size). Despite significant advances in the ability to collect, store, manage and filter data, the real value lies in the analytics. Raw data is meaningless, unless it is properly processed to actionable (business) insights. Those that know how to harness data effectively, have a decisive competitive advantage, through raising performance by making faster and smart decisions, improving short and long-term strategic planning, offering more user-centric products and services and fostering innovation. Two distinct paradigms in practice can be discerned within the field of analytics: semantic-driven (deductive) and data-driven (inductive). The first emphasizes logic as a way of representing the domain knowledge encoded in rules or ontologies and are often carefully curated and maintained. However, these models are often highly complex, and require intensive knowledge processing capabilities. Data-driven analytics employ machine learning (ML) to directly learn a model from the data with minimal human intervention. However, these models are tuned to trained data and context, making it difficult to adapt. Industries today that want to create value from data must master these paradigms in combination. However, there is great need in data analytics to seamlessly combine semantic-driven and data-driven processing techniques in an efficient and scalable architecture that allows extracting actionable insights from an extreme variety of data. In this thesis, we address these needs by providing: • A unified representation of domain-specific and analytical semantics, in form of ontology models called TechOnto Ontology Stack. It is highly expressive, platform-independent formalism to capture conceptual semantics of industrial systems such as technical system hierarchies, component partonomies etc and its analytical functional semantics. • A new ontology language Semantically defined Analytical Language (SAL) on top of the ontology model that extends existing DatalogMTL (a Horn fragment of Metric Temporal Logic) with analytical functions as first class citizens. • A method to generate semantic workflows using our SAL language. It helps in authoring, reusing and maintaining complex analytical tasks and workflows in an abstract fashion. • A multi-layer architecture that fuses knowledge- and data-driven analytics into a federated and distributed solution. To our knowledge, the work in this thesis is one of the first works to introduce and investigate the use of the semantically defined analytics in an ontology-based data access setting for industrial analytical applications. The reason behind focusing our work and evaluation on industrial data is due to (i) the adoption of semantic technology by the industries in general, and (ii) the common need in literature and in practice to allow domain expertise to drive the data analytics on semantically interoperable sources, while still harnessing the power of analytics to enable real-time data insights. Given the evaluation results of three use-case studies, our approach surpass state-of-the-art approaches for most application scenarios.Im Zeitalter der Digitalisierung sammeln die Industrien überall massive Daten-mengen, die zum Lebenselixier der Weltwirtschaft geworden sind. Diese Daten können aus verschiedenen heterogenen Systemen, Geräten, Komponenten, Sensoren, Systemen und Anwendungen in vielen Varianten (Vielfalt der Quellen), Geschwindigkeiten (hohe Änderungsrate) und Volumina (reine Datengröße) stammen. Trotz erheblicher Fortschritte in der Fähigkeit, Daten zu sammeln, zu speichern, zu verwalten und zu filtern, liegt der eigentliche Wert in der Analytik. Rohdaten sind bedeutungslos, es sei denn, sie werden ordnungsgemäß zu verwertbaren (Geschäfts-)Erkenntnissen verarbeitet. Wer weiß, wie man Daten effektiv nutzt, hat einen entscheidenden Wettbewerbsvorteil, indem er die Leistung steigert, indem er schnellere und intelligentere Entscheidungen trifft, die kurz- und langfristige strategische Planung verbessert, mehr benutzerorientierte Produkte und Dienstleistungen anbietet und Innovationen fördert. In der Praxis lassen sich im Bereich der Analytik zwei unterschiedliche Paradigmen unterscheiden: semantisch (deduktiv) und Daten getrieben (induktiv). Die erste betont die Logik als eine Möglichkeit, das in Regeln oder Ontologien kodierte Domänen-wissen darzustellen, und wird oft sorgfältig kuratiert und gepflegt. Diese Modelle sind jedoch oft sehr komplex und erfordern eine intensive Wissensverarbeitung. Datengesteuerte Analysen verwenden maschinelles Lernen (ML), um mit minimalem menschlichen Eingriff direkt ein Modell aus den Daten zu lernen. Diese Modelle sind jedoch auf trainierte Daten und Kontext abgestimmt, was die Anpassung erschwert. Branchen, die heute Wert aus Daten schaffen wollen, müssen diese Paradigmen in Kombination meistern. Es besteht jedoch ein großer Bedarf in der Daten-analytik, semantisch und datengesteuerte Verarbeitungstechniken nahtlos in einer effizienten und skalierbaren Architektur zu kombinieren, die es ermöglicht, aus einer extremen Datenvielfalt verwertbare Erkenntnisse zu gewinnen. In dieser Arbeit, die wir auf diese Bedürfnisse durch die Bereitstellung: • Eine einheitliche Darstellung der Domänen-spezifischen und analytischen Semantik in Form von Ontologie Modellen, genannt TechOnto Ontology Stack. Es ist ein hoch-expressiver, plattformunabhängiger Formalismus, die konzeptionelle Semantik industrieller Systeme wie technischer Systemhierarchien, Komponenten-partonomien usw. und deren analytische funktionale Semantik zu erfassen. • Eine neue Ontologie-Sprache Semantically defined Analytical Language (SAL) auf Basis des Ontologie-Modells das bestehende DatalogMTL (ein Horn fragment der metrischen temporären Logik) um analytische Funktionen als erstklassige Bürger erweitert. • Eine Methode zur Erzeugung semantischer workflows mit unserer SAL-Sprache. Es hilft bei der Erstellung, Wiederverwendung und Wartung komplexer analytischer Aufgaben und workflows auf abstrakte Weise. • Eine mehrschichtige Architektur, die Wissens- und datengesteuerte Analysen zu einer föderierten und verteilten Lösung verschmilzt. Nach unserem Wissen, die Arbeit in dieser Arbeit ist eines der ersten Werke zur Einführung und Untersuchung der Verwendung der semantisch definierten Analytik in einer Ontologie-basierten Datenzugriff Einstellung für industrielle analytische Anwendungen. Der Grund für die Fokussierung unserer Arbeit und Evaluierung auf industrielle Daten ist auf (i) die Übernahme semantischer Technologien durch die Industrie im Allgemeinen und (ii) den gemeinsamen Bedarf in der Literatur und in der Praxis zurückzuführen, der es der Fachkompetenz ermöglicht, die Datenanalyse auf semantisch inter-operablen Quellen voranzutreiben, und nutzen gleichzeitig die Leistungsfähigkeit der Analytik, um Echtzeit-Daten-einblicke zu ermöglichen. Aufgrund der Evaluierungsergebnisse von drei Anwendungsfällen Übertritt unser Ansatz für die meisten Anwendungsszenarien Modernste Ansätze

IIoT Based Efficiency Optimization in Logistics Applications

The Industrial Internet of Thing (IIoT) approach to an Industry plant design, devises a comprehensive interconnection of the system components, from sections up to single devices, in order to get a general and punctual understanding of the process. Such an intelligent network, mostly based on Ethernet basic layers, when properly conceived, should be able to add relevant value to the plant operation. This paper shows how, within the IIoT frame topics, the plant efficiency can be addressed and bring relevant improvement. The reason is that variables directly related to the energy consumption, such as current, electric power, actuator and motor torque, speed, etc., can be timely and easily monitored in the entire plant, since they are already conveyed on the network, due to real time control and diagnostics purpose. A power consumption diagram can be derived, and give hints on how to optimize operations, based on some efficiency index. The paper, after a general discussion, proves it with practical examples based on a Gantry robot, driven in an EtherCAT based automation network, and on the stacker cranes of an automated warehouse

Smart Manufacturing as a framework for Smart Mining

Based on the analogy between manufacturing and mining (i.e. ore 'production'), smart mining has four dimensions: (i) advanced digital-oriented technologies (such as Cloud computing and the Internet of things) with automated Cyber-Physical Systems (CPSs), adaptable production processes (dependent on working conditions) and production volume control (with optimal resource consumption); (ii) smart maintenance of CPSs; (iii) new ways for workers to perform their activities, using advanced digital-oriented technologies; and (iv) smart supply-chain (procurement of materials and spare parts / products delivery). This paper presents a case study on the smart mining approach implemented at a coal mining system in Serbia

Smart Manufacturing as a framework for Smart Mining

Based on the analogy between manufacturing and mining (i.e. ore 'production'), smart mining has four dimensions: (i) advanced digital-oriented technologies (such as Cloud computing and the Internet of things) with automated Cyber-Physical Systems (CPSs), adaptable production processes (dependent on working conditions) and production volume control (with optimal resource consumption); (ii) smart maintenance of CPSs; (iii) new ways for workers to perform their activities, using advanced digital-oriented technologies; and (iv) smart supply-chain (procurement of materials and spare parts / products delivery). This paper presents a case study on the smart mining approach implemented at a coal mining system in Serbia

Communication of Internal Speech with Communicative Associative Robot via Spectral Neurointerface

Thought communications with an associative-communicative robot are carried out through the spectral neurointerface of internal speech.Internal speech is an energy physiological process.Internal speech is vibration from the mental vibration of thought.Mental vibration of thought is a process in the mental ethereal field.The vibrations of thoughts are reflected and observed by the mind in the form of semantic sensual images.Vibrations of semantic sensual images generate vibrations of internal speech action (internal speech) in the form of language communicative and associative stereotypes which are perceived by a touch zone of a brain of Wernicke.Internal speech is a linguistic mental vibration.It is felt and becomes internally audible and drawn to attention.The perception of vibrations of internal speech is carried out through energy channels,such as the internal posterior median canal of the spine.The spectral neurointerface perceives these vibrations.Neocortex makes us a reasonable person - allows us to think and talk.The spectral neurointerface is based on the principles of biosensors,bioenergy detectors,spectral analyzers and electrocorticography for neuroimaging parts of the brain that record vibrations of internal speech,such as the lower frontal gyrus,the upper and middle temporal gyrus,the medial prefrontal cortex,the hind parts of the wedge and precline and the dark temporal region,including the posterior Internal speech activity is associated with the semantic memory of the neocortex