Cost model development using virtual manufacturing and data mining: part I—methodology development

The CF-18 aircraft is a complex system for which a variety of data are systematically being recorded: operational flight data from sensors and Built-In Test Equipment (BITE) and maintenance activities recorded by personnel. These data resources are stored and used within the operating organization but new analytical and statistical techniques and tools are being developed that could be applied to these data to benefit the organization. This paper investigates the utility of readily available CF-18 data to develop data mining-based models for prognostics and health management (PHM) systems. We introduce a generic data mining methodology developed to build prognostic models from operational and maintenance data and elaborate on challenges specific to the use of CF-18 data from the Canadian Forces. We focus on a number of key data mining tasks including: data gathering, information fusion, data pre-processing, model building, and evaluation. The solutions developed to address these tasks are described. A software tool developed to automate the model development process is also presented. Finally, the paper discusses preliminary results on the creation of models to predict F404 No. 4 Bearing and MFC (Main Fuel Control) failures on the CF-18.

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Dynamic quality cost model based on complexity theory

International Journal of Quality & Reliability Management ◽

10.1108/ijqrm-07-2014-0085 ◽

2016 ◽

Vol 33 (5) ◽

Cited By ~ 4

Author(s):

NASARIO DE SOUSA FILIPE DUARTE JUNIOR

Keyword(s):

Decision Making ◽

Complexity Theory ◽

Design Methodology ◽

Cost Model ◽

Dynamic Balance ◽

Time Lag ◽

Management Strategies ◽

Model Development ◽

Quality Costs ◽

Dynamic Quality

Purpose This article presents a dynamic model of decision-making on Quality Costs, using the concepts of the Complexity Theory, consistent with the PAF models (Prevention Appraisal-Failures) and with the “optimum” derivation feature (generally towards Zero Defects). Design/methodology/approach After a literature review and the mathematical model development, this model was simulated in several situations and the results were evaluated, producing new insights. Findings The results show that the larger the delay between action and reaction "t" is, the more complex the system will be, and the effects of the decisions are experienced for “k” later periods, but for some specific “t” a dynamic balance is possible. Research limitations/implications The strategy is immutable. The quantity produced is not a variable in the model. The investment amount “x” is fixed. Originality/value This model is original due to the use of the Complexity Theory, and also to show that the optimum in terms of quality costs can be a positive value or Zero Defects, being in fact a moving target, depending on external conditions. The model value lies in the fact that it is dynamic, so closer to the reality of decision-making enterprises, and for revealing the importance of factors involved with complexity has, such as the time lag "t" for the success of management strategies of Quality Costs.

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Prediction of maintenance cost for road construction equipment: a case study

Canadian Journal of Civil Engineering ◽

10.1139/cjce-2014-0500 ◽

2016 ◽

Vol 43 (5) ◽

pp. 480-492 ◽

Cited By ~ 6

Author(s):

Sharif Mohammad Bayzid ◽

Yasser Mohamed ◽

Maria Al-Hussein

Keyword(s):

Data Mining ◽

Model Development ◽

Road Construction ◽

Study Data ◽

Construction Equipment ◽

Maintenance Cost ◽

Cost Models ◽

Construction Operations ◽

Development Data

Equipment maintenance cost is significant in construction operations budgets. This study proposes a systematic approach to predict maintenance cost of road construction equipment. First, maintenance cost data over more than 10 years was collected from a partner company’s equipment management information system. Data was cleaned and analyzed to obtain a general understanding of maintenance costs trends. Next, traditional cumulative cost models and alternative data mining models were generated to predict maintenance cost based on available equipment and operation attributes. Data mining models were evaluated and validated using portions of the collected data that have not been used in model development. Data collection, analyses, modeling, and validation steps are discussed. The paper also presents the performance of different model types. Based on the case study data, regression model trees performed better than other model types with equipment work hours being the most significant parameter for predicting maintenance cost.

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