Applying Fusion Techniques to Graphical Methods for Knowledge based Processing of Product use Information

  title={Applying Fusion Techniques to Graphical Methods for Knowledge based Processing of Product use Information},
  author={Susanne Dienst and Fazel Ansari and Alexander Holland and Madjid Fathi},
In this paper the processing and modelling of product use information raised by graphical methods on the basis of a praxis and application scenario. Product Lifecycle Management (PLM) ensures a uniform data basis for supporting numerous engineering and economic organisational processes along the entire product life cycle – from the first product idea to disposal or recycling of the product respectively. The Product Use Information (PUI) -e.g. condition monitoring data, failures or incidences of… Expand
Necessity of using Dynamic Bayesian Networks for feedback analysis into product development
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Today technical product documentation and also related product use phase feedback contain valuable information about the company's products. In addition processing of various types of feedbackExpand
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Hydraulic systems are used in great numbers and serve a variety of purposes. Still, however, the operating efficiency of hydraulic systems is not as high as it could be. New ways of monitoring theExpand


Knowledge-Based Feedback of Product Use Information into Product Development
This paper presents a new solution approach for the integration of product use information into product development and the use of knowledge-based inference methods in order to carry out "What-If" analyses. Expand
Bayesian Networks and Decision Graphs
  • F. Jensen
  • Computer Science
  • Statistics for Engineering and Information Science
  • 2001
The book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams, and presents a thorough introduction to state-of-the-art solution and analysis algorithms. Expand
Combining Probability Distributions From Experts in Risk Analysis
This paper concerns the combination of experts' probability distributions in risk analysis, discussing a variety of combination methods and attempting to highlight the important conceptual andExpand
Bayesian networks of customer satisfaction survey data
BNs offer advantages in implementing models of cause and effect over other statistical techniques designed primarily for testing hypotheses, and include the ability to conduct probabilistic inference for prediction and diagnostic purposes with an output that can be intuitively understood by managers. Expand
Probabilistic Networks and Expert Systems
From the Publisher: Probabilistic expert systems are graphical networks that support the modelling of uncertainty and decisions in large complex domains, while retaining ease of calculation.Expand
AIS-BN: An Adaptive Importance Sampling Algorithm for Evidential Reasoning in Large Bayesian Networks
An adaptive importance sampling algorithm, AIS-BN, is proposed that shows promising convergence rates even under extreme conditions and seems to outperform the existing sampling algorithms consistently, and two heuristics for initialization of the importance function that are based on the theoretical properties of importance sampling in finite-dimensional integrals and the structural advantages of Bayesian networks. Expand
A Comparison on the Effectiveness of Two Heuristics for Importance Sampling
If-tempering consistently helps the EPIS-BN algorithm (Yuan and Druzdzel, 2003) achieve better precisions than -cutoff, a heuristic based on simulated tempering that is tested on three large real Bayesian networks. Expand
Bayesian Networks: An Introduction
This book will prove a valuable resource for postgraduate students of statistics, computer engineering, mathematics, data mining, artificial intelligence, and biology and users of comparable modelling or statistical techniques such as neural networks will also find this book of interest. Expand
Graphical Models: Methods for Data Analysis and Mining
  • E. Slud
  • Computer Science, Mathematics
  • Technometrics
  • 2003
The authors demonstrate and conclude that “singleconcentration decision and detection limits are of little practical value, despite their widespread use” (p. 56). Expand
Weighing and Integrating Evidence for Stochastic Simulation in Bayesian Networks
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