Data preparation and preprocessing for broadcast systems monitoring in PHM framework

Nowadays, companies producing goods use production systems that are equipped by different sensors in order to monitor efficiently their behavior. Most of the time, the information collected by these sensors is mainly used for production monitoring rather than to analyzing the state of health of the production system. By so doing, these companies have a large and growing amount of data at their disposal. These data make it possible to extract information and knowledge for a better control of the system in order to improve its efficiency and reliability. With the emergence of Prognostics and Health Management (PHM) paradigm few years ago, it has become possible to study the state of health of an equipment and predict its future evolution. Globally, the principle of PHM is to transform a set of raw data gathered on the monitored equipment into one or more health indicators. In this framework, the present paper addresses issues related to raw data. A generic approach is proposed for obtaining monitoring data that are reliable and exploitable in a PHM application. The proposed approach is based on 2 steps: collecting data and preprocessing data. This approach will be applied to a real world case in broadcast industry to show its feasibility

Recent Advances in Anomaly Detection Methods Applied to Aviation

International audienceAnomaly detection is an active area of research with numerous methods and applications. This survey reviews the state-of-the-art of data-driven anomaly detection techniques and their application to the aviation domain. After a brief introduction to the main traditional data-driven methods for anomaly detection, we review the recent advances in the area of neural networks, deep learning and temporal-logic based learning. In particular, we cover unsupervised techniques applicable to time series data because of their relevance to the aviation domain, where the lack of labeled data is the most usual case, and the nature of flight trajectories and sensor data is sequential, or temporal. The advantages and disadvantages of each method are presented in terms of computational efficiency and detection efficacy. The second part of the survey explores the application of anomaly detection techniques to aviation and their contributions to the improvement of the safety and performance of flight operations and aviation systems. As far as we know, some of the presented methods have not yet found an application in the aviation domain. We review applications ranging from the identification of significant operational events in air traffic operations to the prediction of potential aviation system failures for predictive maintenance

Performance of new GNSS satellite clocks

In Global Navigation Satellite Systems (GNSS), the on-board clocks are a key component from which timing and navigation signals are generated. This thesis reviews the performance of the first Passive Hydrogen Maser (PHM) launched by the Galileo system in 2008; and demonstrates how the new PHM can be consider as the best clock in space, pushing the physical clock error contribution below the noise floor of geodetic time transfer capabilities. Furthermore, overall GNSS clock peformance is reviewe

Integrating Machine Learning Paradigms for Predictive Maintenance in the Fourth Industrial Revolution era

In the last decade, manufacturing companies have been facing two significant challenges. First, digitalization imposes adopting Industry 4.0 technologies and allows creating smart, connected, self-aware, and self-predictive factories. Second, the attention on sustainability imposes to evaluate and reduce the impact of the implemented solutions from economic and social points of view. In manufacturing companies, the maintenance of physical assets assumes a critical role. Increasing the reliability and the availability of production systems leads to the minimization of systems’ downtimes; In addition, the proper system functioning avoids production wastes and potentially catastrophic accidents. Digitalization and new ICT technologies have assumed a relevant role in maintenance strategies. They allow assessing the health condition of machinery at any point in time. Moreover, they allow predicting the future behavior of machinery so that maintenance interventions can be planned, and the useful life of components can be exploited until the time instant before their fault. This dissertation provides insights on Predictive Maintenance goals and tools in Industry 4.0 and proposes a novel data acquisition, processing, sharing, and storage framework that addresses typical issues machine producers and users encounter. The research elaborates on two research questions that narrow down the potential approaches to data acquisition, processing, and analysis for fault diagnostics in evolving environments. The research activity is developed according to a research framework, where the research questions are addressed by research levers that are explored according to research topics. Each topic requires a specific set of methods and approaches; however, the overarching methodological approach presented in this dissertation includes three fundamental aspects: the maximization of the quality level of input data, the use of Machine Learning methods for data analysis, and the use of case studies deriving from both controlled environments (laboratory) and real-world instances

Satellite clock time offset prediction in global navigation satellite systems

In an operational sense, satellite clock time offset prediction (SCTOP) is a fundamental requirement in global navigation satellite systems (GNSS) tech- nology. SCTOP uncertainty is a significant component of the uncertainty budget of the basic GNSS pseudorange measurements used in standard (i.e not high-precision), single-receiver applications. In real-time, this prediction uncertainty contributes directly to GNSS-based positioning, navigation and timing (PNT) uncertainty. In short, GNSS performance in intrinsically linked to satellite clock predictability. Now, satellite clock predictability is affected by two factors: (i) the clock itself (i.e. the oscillator, the frequency standard etc.) and (ii) the prediction algorithm. This research focuses on aspects of the latter. Using satellite clock data—spanning across several years, corresponding to multiple systems (GPS and GLONASS) and derived from real measurements— this thesis first presents the results of a detailed study into the characteristics of GNSS satellite clocks. This leads onto the development of strategies for modelling and estimating the time-offset of those clocks from system time better, with the final aim of predicting those offsets better. The satellite clock prediction scheme of the International GNSS Service (IGS) is analysed, and the results of this prediction scheme are used to evaluate the performance of new methods developed herein. The research presented in this thesis makes a contribution to knowledge in each of the areas of characterisation, modelling and prediction of GNSS satellite clocks. Regarding characterisation of GNSS satellite clocks, the space-borne clocks of GPS and GLONASS are studied. In terms of frequency stability—and thus predictability—it is generally the case that the GPS clocks out-perform GLONASS clocks at prediction lengths ranging from several minutes up to one day ahead. There are three features in the GPS clocks—linear frequency drift, periodic signals and and complex underlying noise processes—that are not observable in the GLONASS clocks. The standard clock model does not capture these features. This study shows that better prediction accuracy can be obtained by an extension to the standard clock model. The results of the characterisation and modelling study are combined in a Kalman filter framework, set up to output satellite clock predictions at a range of prediction intervals. In this part of the study, only GPS satellite clocks are considered. In most, but not all cases, the developed prediction method out- performs the IGS prediction scheme, by between 10% to 30%. The magnitude of the improvement is mainly dependent upon clock type

Data-driven maintenance in railway

Industrial Engineerin

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen

Technological roadmap on AI planning and scheduling

At the beginning of the new century, Information Technologies had become basic and indispensable constituents of the production and preparation processes for all kinds of goods and services and with that are largely influencing both the working and private life of nearly every citizen. This development will continue and even further grow with the continually increasing use of the Internet in production, business, science, education, and everyday societal and private undertaking. Recent years have shown, however, that a dramatic enhancement of software capabilities is required, when aiming to continuously provide advanced and competitive products and services in all these fast developing sectors. It includes the development of intelligent systems – systems that are more autonomous, flexible, and robust than today’s conventional software. Intelligent Planning and Scheduling is a key enabling technology for intelligent systems. It has been developed and matured over the last three decades and has successfully been employed for a variety of applications in commerce, industry, education, medicine, public transport, defense, and government. This document reviews the state-of-the-art in key application and technical areas of Intelligent Planning and Scheduling. It identifies the most important research, development, and technology transfer efforts required in the coming 3 to 10 years and shows the way forward to meet these challenges in the short-, medium- and longer-term future. The roadmap has been developed under the regime of PLANET – the European Network of Excellence in AI Planning. This network, established by the European Commission in 1998, is the co-ordinating framework for research, development, and technology transfer in the field of Intelligent Planning and Scheduling in Europe. A large number of people have contributed to this document including the members of PLANET non- European international experts, and a number of independent expert peer reviewers. All of them are acknowledged in a separate section of this document. Intelligent Planning and Scheduling is a far-reaching technology. Accepting the challenges and progressing along the directions pointed out in this roadmap will enable a new generation of intelligent application systems in a wide variety of industrial, commercial, public, and private sectors

Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning

The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques