Faults Discovery By Using Mined Data

Fault discovery in the complex systems consist of model based reasoning, fault tree analysis, rule based inference methods, and other approaches. Model based reasoning builds models for the systems either by mathematic formulations or by experiment model. Fault Tree Analysis shows the possible causes of a system malfunction by enumerating the suspect components and their respective failure modes that may have induced the problem. The rule based inference build the model based on the expert knowledge. Those models and methods have one thing in common; they have presumed some prior-conditions. Complex systems often use fault trees to analyze the faults. Fault diagnosis, when error occurs, is performed by engineers and analysts performing extensive examination of all data gathered during the mission. International Space Station (ISS) control center operates on the data feedback from the system and decisions are made based on threshold values by using fault trees. Since those decision-making tasks are safety critical and must be done promptly, the engineers who manually analyze the data are facing time challenge. To automate this process, this paper present an approach that uses decision trees to discover fault from data in real-time and capture the contents of fault trees as the initial state of the trees

A framework for modelling mobile radio access networks for intelligent fault management

Postprin

Fifth annual conference on Alaskan placer mining

An abridged format of papers, presentations and addresses given during the 1983 conference held on March 30-31, 1983 compiled and edited by Bruce W. Campbell, Jim Madonna, and M. Susan Husted.Partial funding was provided by the Carl G. Parker Memorial Publishing Fund, University of Alaska, Fairbanks, and the Mining and Mineral Resources Research Institute, U.S. Department of the Interior, Bureau of Mines

3D attributed models for addressing environmental and engineering geoscience problems in areas of urban regeneration : a case study in Glasgow, UK

The City of Glasgow is situated on and around the lower floodplain and inner estuary of the River Clyde in the west of Scotland, UK. Glasgow’s urban hinterland once was one of Europe’s leading centres of heavy industry, and of ship building in particular. The industries were originally fed by locally mined coal and ironstone. In common with many European cities, the heavy industries declined and Glasgow was left with a legacy of industrial dereliction, widespread undermining, and extensive vacant and contaminated sites, some the infilled sites of clay pits and sand and gravel workings

Discovering Rules for Fault Management

. At the heart of the Internet revolution is global telecommunication systems. These systems initially designed for voice traffic provide the vast backbone bandwidth capabilities necessary for Internet traffic. They have builtin redundancy and complexity to ensure robustness and quality of service. To facilitate this, this requires complex fault identification and management systems. Fault identification and management is generally handled by reducing the amount of alarm events (symptoms) presented to the operating engineer through monitoring, filtering and masking. The ultimate goal is to determine and present the actual underlying fault. While en-route to automated fault identification it is useful to derive rules and techniques to attempt to present less symptoms with greater diagnostic assistance. With these objectives in mind computerassisted human discovery and human-assisted computer discovery techniques are discussed.

Active microwave remote sensing of earth/land, chapter 2

Geoscience applications of active microwave remote sensing systems are examined. Major application areas for the system include: (1) exploration of petroleum, mineral, and ground water resources, (2) mapping surface and structural features, (3) terrain analysis, both morphometric and genetic, (4) application in civil works, and (5) application in the areas of earthquake prediction and crustal movements. Although the success of radar surveys has not been widely publicized, they have been used as a prime reconnaissance data base for mineral exploration and land-use evaluation in areas where photography cannot be obtained

Discovering unbounded episodes in sequential data

One basic goal in the analysis of time-series data is to find frequent interesting episodes, i.e, collections of events occurring frequently together in the input sequence. Most widely-known work decide the interestingness of an episode from a fixed user-specified window width or interval, that bounds the subsequent sequential association rules. We present in this paper, a more intuitive definition that allows, in turn, interesting episodes to grow during the mining without any user-specified help. A convenient algorithm to efficiently discover the proposed unbounded episodes is also implemented. Experimental results confirm that our approach results useful and advantageous.Postprint (published version

Data mining in manufacturing: a review based on the kind of knowledge

In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas, including product and process design, assembly, materials planning, quality control, scheduling, maintenance, fault detection etc. Data mining has emerged as an important tool for knowledge acquisition from the manufacturing databases. This paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with a special emphasis on the type of functions to be performed on the data. The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis. The papers reviewed have therefore been categorized in these five categories. It has been shown that there is a rapid growth in the application of data mining in the context of manufacturing processes and enterprises in the last 3 years. This review reveals the progressive applications and existing gaps identified in the context of data mining in manufacturing. A novel text mining approach has also been used on the abstracts and keywords of 150 papers to identify the research gaps and find the linkages between knowledge area, knowledge type and the applied data mining tools and techniques