A survey of the machine interference problem

This paper surveys the research published on the machine interference problem since the 1985 review by Stecke & Aronson. After introducing the basic model, we discuss the literature along several dimensions. We then note how research has evolved since the 1985 review, including a trend towards the modelling of stochastic (rather than deterministic) systems and the corresponding use of more advanced queuing methods for analysis. We conclude with some suggestions for areas holding particular promise for future studies.Natural Sciences and Engineering Research Council (NSERC) Discovery Grant 238294-200

Rejuvenation and the Age of Information

International audienc

Effects of Bridge Surface and Pavement Maintenance Activities on Asset Rating

This study provides and demonstrates a methodology to quantify the impact of INDOT’s standard maintenance treatments on state highway pavements and bridge deck surfaces, in terms of their condition ratings. Of the specific objectives, the first is to generate requisite reset values that INDOT’s asset manager can use in the agency’s PMS and BMS software packages. The second is to measure the longer-term effectiveness of specific maintenance treatments in terms of the extension to asset life. The third specific objective is to use this information to assess the cost-effectiveness of the treatments. The research product from this project is a set of averages or models that represent the impacts (performance jump, post-treatment performance vs. age relationship, and cost) of each treatment type typically applied to INDOT’s assets. The performance impacts are expressed in terms of the requisite performance indicators. The performance jump models showed that the asset’s functional class and pre-treatment condition, and the treatment type were major significant predictors of the performance jump and post-treatment performance loss. The first deliverable from this project is the average (mean) impact for each treatment type under investigation. The second is the overall statistical description of the impact, namely, the minimum and maximum impact, and range and standard deviation of impact; a statistical model that predicts the impact as a function of asset and treatment attributes. The third is a set of charts that describe the sensitivity of the treatment impact to factors related to the asset or the treatment. The study also developed cost models for each of the pavement and bridge treatments and used these results to assess the long-term cost-effectiveness of the treatments

Data Mining in MRO

Data mining seems to be a promising way to tackle the problem of unpredictability in MRO organizations. The Amsterdam University of Applied Sciences therefore cooperated with the aviation industry for a two-year applied research project exploring the possibilities of data mining in this area. Researchers studied more than 25 cases at eight different MRO enterprises, applying a CRISP-DM methodology as a structural guideline throughout the project. They explored, prepared and combined MRO data, flight data and external data, and used statistical and machine learning methods to visualize, analyse and predict maintenance. They also used the individual case studies to make predictions about the duration and costs of planned maintenance tasks, turnaround time and useful life of parts. Challenges presented by the case studies included time-consuming data preparation, access restrictions to external data-sources and the still-limited data science skills in companies. Recommendations were made in terms of ways to implement data mining – and ways to overcome the related challenges – in MRO. Overall, the research project has delivered promising proofs of concept and pilot implementation

Data Mining in MRO

Data mining seems to be a promising way to tackle the problem of unpredictability in MRO organizations. The Amsterdam University of Applied Sciences therefore cooperated with the aviation industry for a two-year applied research project exploring the possibilities of data mining in this area. Researchers studied more than 25 cases at eight different MRO enterprises, applying a CRISP-DM methodology as a structural guideline throughout the project. They explored, prepared and combined MRO data, flight data and external data, and used statistical and machine learning methods to visualize, analyse and predict maintenance. They also used the individual case studies to make predictions about the duration and costs of planned maintenance tasks, turnaround time and useful life of parts. Challenges presented by the case studies included time-consuming data preparation, access restrictions to external data-sources and the still-limited data science skills in companies. Recommendations were made in terms of ways to implement data mining – and ways to overcome the related challenges – in MRO. Overall, the research project has delivered promising proofs of concept and pilot implementation

A knowledge-based decision support system in reliability-centered maintenance of HVAC systems

Studies have shown that in large physical systems, it is possible to eliminate or reduce costly machine failures, equipment downtime, lost production and decreased revenues by keeping abreast of the most effective and current maintenance techniques available. -- The purpose of this thesis is to propose a multi-faceted approach to maintenance which can address the short comings of conventional approaches to maintenance. -- The proposed methodology combines the reliability-centered maintenance technique (RCM), a fault tree analysis, a database system, and the Weibull analysis. The integration of these techniques produces an innovative system which increases the reliability and availability of the system. To the author's knowledge, this integrated approach has not been done before. -- As an example, the heating, ventilating and air conditioning (HVAC) of large buildings was used to illustrate this methodology. Failure data was collected from the Biotechnology. Arts and Administration Extension and Earth Resources Buildings of Memorial University of Newfoundland (CERR) over a six year period. The data included the time to failure and failure modes for each component within the central HVAC system. The collected data was used to quantify the reliability of the system. A probabilistic analysis based on the Weibull distribution was used to analyze the time to failure data. -- Using reliability-centered maintenance to identify the causes and impact of failures, the information acquired was used to develop fault trees. Failure modes identified in the fault trees were coded as identifiers to be used in a knowledge-based system for improving the reliability and availability of the system and its components. -- It was shown that system reliability can be improved by increasing the reliability of each component utilizing the proposed multi-faceted approach. Failure data analysis enabled us to quantify the reliability for many sub-components within the major components that constitute the HVAC system. -- It is concluded that the developed knowledge-based system enables us to troubleshoot causes of failure at a much faster rate and this will decrease the down time and increase the availability of the system