Data mining techniques for improving the reliability of system identification

Saitta, Sandro; Raphael, Benny; Smith, Ian F. C.

doi:10.1016/j.aei.2005.07.005

Cited by 54 publications

(26 citation statements)

References 24 publications

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“…This gives an upper bound corresponding to the case where the uncertainties are perfectly and positively correlated with each other. These two approaches are particular cases of the general law of propagation of uncertainties, see Joint Committee for Guides in Metrology (JCGM) [7] For the purposes of interpretation, advanced techniques were proposed by Saitta et al [8][9][10] to find clusters within the candidate model set. An additional approach proposed by Smith and Saitta [11] used parameter reduction through principal component analysis in order to improve visualisation and interpretation of results that often lie in multi-dimensional spaces.…”

Section: Figure 1 -Cms4si Frameworkmentioning

confidence: 99%

CMS4SI Structural Identification Approach for Interpreting Measurements

Goulet¹,

Smith²

2010

IABSE Symposium, Venice 2010: Large Structures and Infrastructures for Environmentally Constrained and Urbanised Areas

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The ageing of existing structures and new innovative designs are increasing the necessity for a greater understanding of structural behaviour. A better understanding would improve effectiveness of activities such as assessing reserve capacity, evaluating load increases and replacement decision making. Identification methodologies are needed to indicate the right behaviour using indirect measurements and behaviour models.This paper proposes a methodology that is able to accommodate multiple explanations while overcoming limitations of other SI approaches. The algorithm is called Candidate Model Search for System Identification (CMS4SI). Metrology guidelines are extended for use in the field of system identification while systematically including uncertainties and their correlations. The CMS4SI approach provides the necessary robustness and simplicity to support decisions related to the identification and understanding of structural behaviour. The approach is evaluated by full scale- testing of the Langensand Bridge. A critical aspect for meaningful identification is the uncertainty associated with model simplifications. The adaptation of clustering techniques and the use of radar plots allow for a convenient visualisation of results involving several parameters. Finally, models that are identified can be used to perform predictions of unmeasured behaviour, thereby supporting infrastructure management.

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Section: Figure 1 -Cms4si Frameworkmentioning

confidence: 99%

CMS4SI Structural Identification Approach for Interpreting Measurements

Goulet¹,

Smith²

2010

IABSE Symposium, Venice 2010: Large Structures and Infrastructures for Environmentally Constrained and Urbanised Areas

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“…The concept of reliability stems from the description of the product's reliability, which refers to the capability that enables the product to complete the required function under a\specified condition and within the specified time (Sinha, 1986). Current research on reliability and association rule tends to consider rule mining based on statistical data, such as the product's basic reliability (Jayakrushna and Ashok, 2015) and useful life (Xu, 2008); or assess the data quality for mining using some reliability methods (Katja and M arc-Thorsten, 2011;Sandro, Benny and Ian, 2005). However, current research rarely provides objective and quantitative judgment on the reliability of the association rule.…”

Section: Introductionmentioning

confidence: 99%

A Reliability Evaluation System of Association Rules

Chen¹,

Feng²,

Luo³

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABS TRACT:In mining association rules, the evaluation of the rules is a highly important work because it directly affects the usability and applicability of the output results of mining. In this paper, the concept of reliability was imported into the association rule evaluation. The reliability of association rules was defined as the accordance degree that reflects the rules of the mining data set. Such degree contains three levels of measurement, namely, accuracy, completeness, and consistency of rules. To show its effectiveness, the "accuracy-completeness-consistency" reliability evaluation system was applied to two extremely different data sets, namely, a basket simulation data set and a multi-source lightning data fusion. Results show that the reliability evaluation system works well in both simulation data set and the actual problem. The three-dimensional reliability evaluation can effectively detect the useless rules to be screened out and add the missing rules thereby improving the reliability of mining results. Furthermore, the proposed reliability evaluation system is applicable to many research fields; using the system in the analysis can facilitate obtainment of more accurate, complete, and consistent association rules.

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“…The data mining process extracts knowledge from an existing data set and transforms it into a human-understandable structure for further use (Witten, Frank, & Hall, 2011). Data mining techniques are required to help in identification of model characteristics important to capture and document in an enhancement context of the safety and reliability of complex engineering systems (Saitta, Raphael, & Smith, 2005). Data mining applications are very suitable for quality improvement programs (e.g.…”

Section: Introductionmentioning

confidence: 99%

Mining association rules for the quality improvement of the production process

Kamsu-Foguem

Rigal

Mauget

2013

Expert Systems with Applications

132

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To cite this version:Bernard Kamsu-Foguem, Fabien Rigal, Félix Mauget. Mining association rules for the quality improvement of the production process. Expert Systems with Applications, Elsevier, 2013, vol. 40, pp. 1034-1045 b s t r a c tAcademics and practitioners have a common interest in the continuing development of methods and computer applications that support or perform knowledge-intensive engineering tasks. Operations management dysfunctions and lost production time are problems of enormous magnitude that impact the performance and quality of industrial systems as well as their cost of production. Association rule mining is a data mining technique used to find out useful and invaluable information from huge databases. This work develops a better conceptual base for improving the application of association rule mining methods to extract knowledge on operations and information management. The emphasis of the paper is on the improvement of the operations processes. The application example details an industrial experiment in which association rule mining is used to analyze the manufacturing process of a fully integrated provider of drilling products. The study reports some new interesting results with data mining and knowledge discovery techniques applied to a drill production process. Experiment's results on real-life data sets show that the proposed approach is useful in finding effective knowledge associated to dysfunctions causes.

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Data mining techniques for improving the reliability of system identification

Cited by 54 publications

References 24 publications

CMS4SI Structural Identification Approach for Interpreting Measurements

CMS4SI Structural Identification Approach for Interpreting Measurements

A Reliability Evaluation System of Association Rules

Mining association rules for the quality improvement of the production process

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