PRECEDENT-FREE FAULT LOCALIZATION AND DIAGNOSIS FOR HIGH SPEED TRAIN DRIVE SYSTEMS

In this paper, a framework for localization of sources of unprecedented faults in the drive train system of high speed trains is presented. The framework utilizes distributed anomaly detection, with anomaly detectors based on the recently introduced Growing Structure Multiple Model Systems (GSMMS) models. Physics based models of the drive system and its pertinent subsystems were derived and were calibrated using data collected over several actual trips on a high speed train. Simulation results demonstrate the ability to localize faults within various parts of the drive train system without the need for models of the underlying faults. In addition, traditional model based diagnosis was utilized for positive identification of faults, with signals emitted by the systems in the presence of those faults being available for modeling and subsequent recognition of faulty behavior

Digital Manufacturing as a basis for the development of the Industry 4.0 model

The digital manufacturing (DM) is base for Industry 4.0, that have following dimensions: (i) digital manufacturing based on advanced digital-oriented technologies, (ii) smart products (advanced production mode and new characteristics), and (iii) smart supply - chain (procurement of raw materials and delivery of finished products). Bidirectional exchange of information in collaborative production, using it exchange also for digital platforms of design of the innovative products. This paper presents developed model of Serbian digital factory with selected examples, specifically for the Manufacturing Execution System (MES) area

Digital Manufacturing as a basis for the development of the Industry 4.0 model

The digital manufacturing (DM) is base for Industry 4.0, that have following dimensions: (i) digital manufacturing based on advanced digital-oriented technologies, (ii) smart products (advanced production mode and new characteristics), and (iii) smart supply - chain (procurement of raw materials and delivery of finished products). Bidirectional exchange of information in collaborative production, using it exchange also for digital platforms of design of the innovative products. This paper presents developed model of Serbian digital factory with selected examples, specifically for the Manufacturing Execution System (MES) area

Cyber-Physical Manufacturing Metrology Model (CPM3) for Sculptured Surfaces - Turbine Blade Application

Cyber-Physical Manufacturing (CPM) and digital manufacturing represent the key elements for implementation of Industry 4.0 framework. Worldwide, Industry 4.0 becomes national research strategy in the field of engineering for the following ten years. The International Conference USA-EU-Far East-Serbia Manufacturing Summit was held from 31st May to 2nd June 2016 in Belgrade, Serbia. The result of the conference was the development of Industry 4.0 Model for Serbia as a framework for New Industrial Policy - Horizon 2020/2030. Implementation of CPM in manufacturing systems generates " smart factory". Products, resources, and processes within smart factory are realized and controlled through CPM model. This leads to significant advantages with respect to high product/process quality, real-time applications, savings in resources consumption, as well as, lower costs in comparison with classical manufacturing systems. Smart factory is designed in accordance with sustainable and service-oriented best business practices/models. It is based on optimization, flexibility, self-adaptability and learning, fault tolerance, and risk management. Complete manufacturing digitalization and digital factory are the key elements of Industry 4.0 Program. In collaborative research, which we carry out in the field of quality control and manufacturing metrology at University of Belgrade, Faculty of Mechanical Engineering in Serbia and at Department of Mechanical Engineering, University of Texas, Austin in USA, three research areas are defined: (a) Digital manufacturing - towards Cloud Manufacturing Systems (as a basis for CPS), in which quality and metrology represent integral parts of process optimization based on Taguchi model, and (sic) Cyber-Physical Quality Model (CPQM) - our approach, in which we have developed and tested intelligent model for prismatic parts inspection planning on CMM (Coordinate Measuring Machine). The third research area directs our efforts to the development of framework for Cyber-Physical Manufacturing Metrology Model (CPM3). CPM3 framework will be based on integration of digital product metrology information through metrology features recognition, and generation of global/local inspection plan for free-form surfaces; we will illustrate our approach using turbine blade example. This paper will present recent results of our research on CPM3

Cyber-Physical Manufacturing Metrology Model (CPM3) - Big Data Analytics Issue

Internet of Things (IoT) is changing the world, and therefore the application of ICT (Information and Communication Technology) in manufacturing. As a paradigm based on the Internet, IoT utilizes the benefits of interrelated technologies/smart devices such as RFID (Radio Frequency Identification) and WSAN (Wireless Sensor and Actuator Networks) for the retrieval and exchange of information thus opening up new possibilities for integration of manufacturing system and its cyber representation through Cyber-Physical Manufacturing (CPM) model. On the other hand, CPM and digital manufacturing represent the key elements for implementation of Industry 4.0 and backbone for "smart factory" generation. Interconnected smart devices generate huge databases (big data), so that Cloud computing becomes indispensable tool to support the CPM. In addition, CPM has an extremely expressed requirement for better control, monitoring and data management. Limitations still exist in storages, networks and computers, as well as in the tools for complex data analysis, detection of its structure and retrieval of useful information. Products, resources, and processes within smart factory are realized and controlled through CPM model. In this context, our recent research efforts in the field of quality control and manufacturing metrology are directed to the development of framework for Cyber-Physical Manufacturing Metrology Model (CPM3). CPM3 framework will be based on: 1) integration of digital product metrology information obtained from big data using BDA (big data analytics) through metrology features recognition, and 2) generation of global/local inspection plan for CMM (Coordinate Measuring Machine) from extracted information. This paper will present recent results of our research on CPM3 - big data analytics issue

PRECEDENT-FREE FAULT LOCALIZATION AND DIAGNOSIS FOR HIGH SPEED TRAIN DRIVE SYSTEMS

In this paper, a framework for localization of sources of unprecedented faults in the drive train system of high speed trains is presented. The framework utilizes distributed anomaly detection, with anomaly detectors based on the recently introduced Growing Structure Multiple Model Systems (GSMMS) models. Physics based models of the drive system and its pertinent subsystems were derived and were calibrated using data collected over several actual trips on a high speed train. Simulation results demonstrate the ability to localize faults within various parts of the drive train system without the need for models of the underlying faults. In addition, traditional model based diagnosis was utilized for positive identification of faults, with signals emitted by the systems in the presence of those faults being available for modeling and subsequent recognition of faulty behavior

Robust model-based control of multistage manufacturing processes

This paper presents a novel method that utilizes in-process measurements of product quality and models that relate those measurements with the underlying manufacturing process parameters to drive down the product quality errors via strategic adjustments of the controllable process parameters. Uniqueness of the new method is its robustness to inevitable inaccuracies in the underlying models, as well as the absence of traditional, but restrictive assumptions of Gaussianity and independence of measurement and process noise terms. The new approach was demonstrated using models and data from an automotive cylinder head machining process and an industrial-scale semiconductor lithography overlay process

An approach of development smart manufacturing metrology model as support industry 4.0

The framework for smart manufacturing metrology model (S3M), are based on integration of digital product metrology information through metrological identification, application artificial intelligence techniques and generation of global/local inspection plan for coordinate measuring machine (CMM). S3M has an extremely expressed requirement for better control, monitoring and data mining. Limitations still exist in data storages, networks and computers, as well as in the tools for complex data analysis, detection of its structure and retrieval of useful information. This paper will present recent results of our research on building of S3M as support Industry 4.0. Presented approach to S3M development includes four levels: (i) mathematical model of the measuring sensor path, which establishes a connection between the coordinate systems; (ii) generating the needed set of information to integrate the given tolerances and geometry of the parts by applying an ontological knowledge base; (iii) the application of AI techniques such as ACO and GA to optimize the measurement path, numbers of measuring part setup and configuration of the measuring probes; (iv) simulation of measurement path for a collision check. After simulation of the measurement path and visual checks of collisions, the path sequences are generated in the control data list for appropriate CMM. The experiment was successfully carried out on the examples of prismatic part and two turbine blades or its free-form measuring surfaces

Organization of big metrology data within the Cyber-Physical Manufacturing Metrology Model (CPM3)

In this paper, we propose a novel data curation concept that enables data mining and analytics within the recently described Cyber-Physical Manufacturing Metrology Model (CPM3). The newly proposed methodology is based on organizing the metrology data into tree-based database structures using distance-based unsupervised clustering of the raw metrology data. Compared to traditionally utilized temporally organized lists, the new tree-based database organization of metrology data enables logarithmic acceleration of searches within the data and thus provides dramatic advantages for data mining. The newly proposed data curation methodology was evaluated in case studies involving hyper-spectral metrology of nanopatterned surfaces, coordinate measurement machine (CMM) inspection of aircraft engine turbines and imaging-based metrology of nano-volume droplets in the jet and fill stage of imprint lithography processes. Significant improvements in search speeds with minimal or no losses in search precision and recall were observed in all case-studies, with benefits of tree-based data organization growing with the size of the data