9 research outputs found

    A Study on Data Filtering Techniques for Event-Driven Failure Analysis

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    Engineering & Systems DesignHigh performance sensors and modern data logging technology with real-time telemetry facilitate system failure analysis in a very precise manner. Fault detection, isolation and identification in failure analysis are typical steps to analyze the root causes of failures. This systematic failure analysis provides not only useful clues to rectify the abnormal behaviors of a system, but also key information to redesign the current system for retrofit. The main barriers to effective failure analysis are: (i) the gathered sensor data logs, usually in the form of event logs containing massive datasets, are too large, and further (ii) noise and redundant information in the gathered sensor data that make precise analysis difficult. Therefore, the objective of this thesis is to develop an event-driven failure analysis method in order to take into account both functional interactions between subsystems and diverse user???s behaviors. To do this, we first apply various data filtering techniques to data cleaning and reduction, and then convert the filtered data into a new format of event sequence information (called ???eventization???). Four eventization strategies: equal-width binning, entropy, domain knowledge expert, and probability distribution estimation, are examined for data filtering, in order to extract only important information from the raw sensor data while minimizing information loss. By numerical simulation, we identify the optimal values of eventization parameters. Finally, the event sequence information containing the time gap between event occurrences is decoded to investigate the correlation between specific event sequence patterns and various system failures. These extracted patterns are stored in a failure pattern library, and then this pattern library is used as the main reference source to predict failures in real-time during the failure prognosis phase. The efficiency of the developed procedure is examined with a terminal box data log of marine diesel engines.ope

    Proceedings of the ECMLPKDD 2015 Doctoral Consortium

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    ECMLPKDD 2015 Doctoral Consortium was organized for the second time as part of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD), organised in Porto during September 7-11, 2015. The objective of the doctoral consortium is to provide an environment for students to exchange their ideas and experiences with peers in an interactive atmosphere and to get constructive feedback from senior researchers in machine learning, data mining, and related areas. These proceedings collect together and document all the contributions of the ECMLPKDD 2015 Doctoral Consortium

    FAULT DETECTION AND PREDICTION IN ELECTROMECHANICAL SYSTEMS VIA THE DISCRETIZED STATE VECTOR-BASED PATTERN ANALYSIS OF MULTI-SENSOR SIGNALS

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    Department of System Design and Control EngineeringIn recent decades, operation and maintenance strategies for industrial applications have evolved from corrective maintenance and preventive maintenance, to condition-based monitoring and eventually predictive maintenance. High performance sensors and data logging technologies have enabled us to monitor the operational states of systems and predict fault occurrences. Several time series analysis methods have been proposed in the literature to classify system states via multi-sensor signals. Since the time series of sensor signals is often characterized as very-short, intermittent, transient, highly nonlinear, and non-stationary random signals, they make time series analyses more complex. Therefore, time series discretization has been popularly applied to extract meaningful features from original complex signals. There are several important issues to be addressed in discretization for fault detection and prediction: (i) What is the fault pattern that represents a system???s faulty states, (ii) How can we effectively search for fault patterns, (iii) What is a symptom pattern to predict fault occurrences, and (iv) What is a systematic procedure for online fault detection and prediction. In this regard, this study proposes a fault detection and prediction framework that consists of (i) definition of system???s operational states, (ii) definitions of fault and symptom patterns, (iii) multivariate discretization, (iv) severity and criticality analyses, and (v) online detection and prediction procedures. Given the time markers of fault occurrences, we can divide a system???s operational states into fault and no-fault states. We postulate that a symptom state precedes the occurrence of a fault within a certain time period and hence a no-fault state consists of normal and symptom states. Fault patterns are therefore found only in fault states, whereas symptom patterns are either only found in the system???s symptom states (being absent in the normal states) or not found in the given time series, but similar to fault patterns. To determine the length of a symptom state, we present a symptom pattern-based iterative search method. In order to identify the distinctive behaviors of multi-sensor signals, we propose a multivariate discretization approach that consists mainly of label definition, label specification, and event codification. Discretization parameters are delicately controlled by considering the key characteristics of multi-sensor signals. We discuss how to measure the severity degrees of fault and symptom patterns, and how to assess the criticalities of fault states. We apply the fault and symptom pattern extraction and severity assessment methods to online fault detection and prediction. Finally, we demonstrate the performance of the proposed framework through the following six case studies: abnormal cylinder temperature in a marine diesel engine, automotive gasoline engine knockings, laser weld defects, buzz, squeak, and rattle (BSR) noises from a car door trim (using a typical acoustic sensor array and using acoustic emission sensors respectively), and visual stimuli cognition tests by the P300 experiment.ope

    An Event-based Analysis Framework for Open Source Software Development Projects

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    The increasing popularity and success of Open Source Software (OSS) development projects has drawn significant attention of academics and open source participants over the last two decades. As one of the key areas in OSS research, assessing and predicting OSS performance is of great value to both OSS communities and organizations who are interested in investing in OSS projects. Most existing research, however, has considered OSS project performance as the outcome of static cross-sectional factors such as number of developers, project activity level, and license choice. While variance studies can identify some predictors of project outcomes, they tend to neglect the actual process of development. Without a closer examination of how events occur, an understanding of OSS projects is incomplete. This dissertation aims to combine both process and variance strategy, to investigate how OSS projects change over time through their development processes; and to explore how these changes affect project performance. I design, instantiate, and evaluate a framework and an artifact, EventMiner, to analyze OSS projects’ evolution through development activities. This framework integrates concepts from various theories such as distributed cognition (DCog) and complexity theory, applying data mining techniques such as decision trees, motif analysis, and hidden Markov modeling to automatically analyze and interpret the trace data of 103 OSS projects from an open source repository. The results support the construction of process theories on OSS development. The study contributes to literature in DCog, design routines, OSS development, and OSS performance. The resulting framework allows OSS researchers who are interested in OSS development processes to share and reuse data and data analysis processes in an open-source manner

    Stock Trend Prediction by Sequential Chart Pattern via K-Means and AprioriAll Algorithm

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    5th International Conference on Advanced Research Methods and Analytics (CARMA 2023)

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    Research methods in economics and social sciences are evolving with the increasing availability of Internet and Big Data sources of information. As these sources, methods, and applications become more interdisciplinary, the 5th International Conference on Advanced Research Methods and Analytics (CARMA) is a forum for researchers and practitioners to exchange ideas and advances on how emerging research methods and sources are applied to different fields of social sciences as well as to discuss current and future challenges.Martínez Torres, MDR.; Toral Marín, S. (2023). 5th International Conference on Advanced Research Methods and Analytics (CARMA 2023). Editorial Universitat Politècnica de València. https://doi.org/10.4995/CARMA2023.2023.1700
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