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Predictive Health Monitoring for Aircraft Systems using Decision Trees
Linköping University, Department of Management and Engineering, Fluid and Mechatronic Systems. Linköping University, The Institute of Technology.
2014 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Unscheduled aircraft maintenance causes a lot problems and costs for aircraft operators. This is due to the fact that aircraft cause significant costs if flights have to be delayed or canceled and because spares are not always available at any place and sometimes have to be shipped across the world. Reducing the number of unscheduled maintenance is thus a great costs factor for aircraft operators. This thesis describes three methods for aircraft health monitoring and prediction; one method for system monitoring, one method for forecasting of time series and one method that combines the two other methods for one complete monitoring and prediction process. Together the three methods allow the forecasting of possible failures. The two base methods use decision trees for decision making in the processes and genetic optimization to improve the performance of the decision trees and to reduce the need for human interaction. Decision trees have the advantage that the generated code can be fast and easily processed, they can be altered by human experts without much work and they are readable by humans. The human readability and modification of the results is especially important to include special knowledge and to remove errors, which the automated code generation produced.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2014. , 79 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1655
Keyword [en]
Condition Monitoring, Condition Prediction, Failure Prediction, Decision Trees, Genetic Algorithm, Fuzzy Decision Tree Evaluation, System Monitoring, Aircraft Health Monitoring
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-105843Local ID: LIU-TEK-LIC-2014:88ISBN: 978-91-7519-346-5 (print)OAI: oai:DiVA.org:liu-105843DiVA: diva2:711210
Presentation
2014-04-11, A38, Hus A, Campus Valla, Linköpings universitet, Linköping, 10:15 (English)
Opponent
Supervisors
Available from: 2014-04-09 Created: 2014-04-09 Last updated: 2015-07-10Bibliographically approved
List of papers
1. Reducing Delays Caused by Unscheduled Maintenance and Cabin Reconfiguration
Open this publication in new window or tab >>Reducing Delays Caused by Unscheduled Maintenance and Cabin Reconfiguration
2009 (English)In: Proceedings of the 2nd International Workshop on Aircraft System Technologies, 2009 / [ed] Otto von Estorff and Frank Thielecke, Shaker Verlag, 2009, 109-119 p.Conference paper, Published paper (Refereed)
Abstract [en]

With respect to future trends in cabin design, this paper takes a close look at two problem areas that produce delays and reduce the time an aircraft is in the air. The first is related to unscheduled maintenance and  the second is related to necessary cabin reconfigurations. Each of the two problem areas are analyzed for time consumption and costs. The research project PAHMIR (Preventive Aircraft Health Monitoring for Integrated Reconfiguration, a cooperation project between Airbus Germany and Hamburg University of Applied Sciences) ads “intelligence” to aircraft components and novel technologies to the aircraft system. These measures will be able to reduce time consumption and costs for unscheduled maintenance caused by the air conditioning system in the order of 8 %. These measures will be able to reduce time consumption for cabin reconfiguration by about 36+%. The upper price limit for economic intelligent quick fasteners seems to be around 500 USD.

Place, publisher, year, edition, pages
Shaker Verlag, 2009
Keyword
Aircraft Delays, Cost Reduction, A340, Condition Monitoring, Reconfiguration
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-105836 (URN)978-3-8322-8071-0 (ISBN)
Conference
2nd International Workshop on Aircraft System Technologies (AST 2009) March 26-27, 2013, Hamburg, Germany
Available from: 2014-04-09 Created: 2014-04-09 Last updated: 2014-04-14Bibliographically approved
2. Feature Extraction and Sensor Optimization for Condition Monitoring of Recirculation Fans and Filters
Open this publication in new window or tab >>Feature Extraction and Sensor Optimization for Condition Monitoring of Recirculation Fans and Filters
2009 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Complex systems are commonly monitored by many sensors. When one of the sensors fails, the current state of the system can not be calculated or the information about the current state is not complete. For that reason sensor failures are one of the main error sources of a system. Thus sensors that deliver significant information about the system state need to be redundant. This paper shows how to calculate the significance of the information that a sensor gives about a system by using modern signal processing and artificial intelligence technologies. It also shows how significant features can be extracted, evaluated from a set of data samples, how difficult it is to find an optimal parameter and sensor set and that it is possible to reduce the size of needed data by 97%. The paper concludes analyzing the results of experiments that show that the methods can classify different errors with a 75% probability.

Keyword
Feature Extraction, Decision Trees, Air Conditioning, A340
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-105838 (URN)
Conference
Deutscher Luft- und Raumfahrtkongress, 8-10 September 2009, Aachen, Germany
Available from: 2014-04-09 Created: 2014-04-09 Last updated: 2014-04-14
3. Parameter Optimization for Automated Signal Analysis for Condition Monitoring of Aircraft Systems
Open this publication in new window or tab >>Parameter Optimization for Automated Signal Analysis for Condition Monitoring of Aircraft Systems
2011 (English)In: Proceedings of the 3rd International Workshop on Aircraft System Technologies, 2011 / [ed] Otto von Estorff and Frank Thielecke, Shaker Verlag, 2011Conference paper, Published paper (Refereed)
Abstract [en]

In the PAHMIR (Preventive Aircraft and Health Monitoring) project pattern recognition and signal analysis is used to support and simplify the monitoring of complex aircraft systems. The parameters of the signal analysis need to be chosen specifically for the monitored system to get the best pattern recognition accuracy. An optimization process was developed that uses global heuristic search and optimization to find a good parameter set for the signal analysis. The computed parameters deliver slightly (one to three percent) better results than the ones found by hand. In addition it is shown that not a full set of data samples is needed. Genetic optimization showed the best performance.

Place, publisher, year, edition, pages
Shaker Verlag, 2011
Series
Berichte Aus Der Luft- Und Raumfahrttechnik, ISSN 0945-2214
Keyword
Genetic Algorithm, Optimization, Feature Extration, Signal Analysis, Global Heuristic Search
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-105839 (URN)38-3229-904-1 (ISBN)978-38-3229-904-0 (ISBN)
Conference
3rd International Workshop on Aircraft System Technologies (AST 2011), March 31 - April 1, Hamburg, Germany
Available from: 2014-04-09 Created: 2014-04-09 Last updated: 2014-04-14Bibliographically approved
4. Fuzzy Condition Monitoring of Recirculation Fans and Filters
Open this publication in new window or tab >>Fuzzy Condition Monitoring of Recirculation Fans and Filters
2011 (English)In: Aeronautical Journal, ISSN 0001-9240, Vol. 2, no 1-4, 81-87 p.Article in journal (Refereed) Published
Abstract [en]

A reliable condition monitoring is needed to predict faults. Pattern recognition technologies are often used for finding patterns in complex systems. Condition monitoring can also benefit from pattern recognition. Many pattern recognition technologies, however, only output the classification of the data sample but do not output any information about classes that are also very similar to the input vector. This paper presents a concept for pattern recognition that output similarity values for decision trees. The concept can be used on top of any normal decision tree algorithms and is independent of the learning algorithm. Performed experiments showed that the concept is reliable and it also works with decision tree forests to increase the classification accuracy.

Place, publisher, year, edition, pages
Vienna: Springer, 2011
Keyword
Decision Trees, Fuzzy Decision Tree Evaluation, Air Conditioning, A340
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-105841 (URN)10.1007/s13272-011-0021-9 (DOI)
Available from: 2014-04-09 Created: 2014-04-09 Last updated: 2017-12-05Bibliographically approved
5. Decision trees and genetic algorithms for condition monitoring forecasting of aircraft air conditioning
Open this publication in new window or tab >>Decision trees and genetic algorithms for condition monitoring forecasting of aircraft air conditioning
2013 (English)In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 40, no 12, 5021-5026 p.Article in journal (Refereed) Published
Abstract [en]

Unscheduled maintenance of aircraft can cause significant costs. The machine needs to be repaired before it can operate again. Thus it is desirable to have concepts and methods to prevent unscheduled maintenance. This paper proposes a method for forecasting the condition of aircraft air conditioning system based on observed past data. Forecasting is done in a point by point way, by iterating the algorithm. The proposed method uses decision trees to find and learn patterns in past data and use these patterns to select the best forecasting method to forecast future data points. Forecasting a data point is based on selecting the best applicable approximation method. The selection is done by calculating different features/attributes of the time series and then evaluating the decision tree. A genetic algorithm is used to find the best feature set for the given problem to increase the forecasting performance. The experiments show a good forecasting ability even when the function is disturbed by noise.

Place, publisher, year, edition, pages
Elsevier, 2013
Keyword
Decision tree; Forecasting; Expert system; Machine learning; Time series; Maintenance; Genetic algorithm
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-105842 (URN)10.1016/j.eswa.2013.03.025 (DOI)
Available from: 2014-04-09 Created: 2014-04-09 Last updated: 2017-12-05Bibliographically approved

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