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- Al-Douri, Yamur K.
(författare)
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Two-Level Multi-Objective Genetic Algorithm for Risk-Based Life Cycle Cost Analysis
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Artificial intelligence (AI) is one of the fields in science and engineering and encompasses a wide variety of subfields, ranging from general areas (learning and perception) to specific topics, such as mathematical theorems. AI and, specifically, multi-objective genetic algorithms (MOGAs) for risk-based life cycle cost (LCC) analysis should be performed to estimate the optimal replacement time of tunnel fan systems, with a view towards reducing the ownership cost and the risk cost and increasing company profitability from an economic point of view. MOGA can create systems that are capable of solving problems that AI and LCC analyses cannot accomplish alone.The purpose of this thesis is to develop a two-level MOGA method for optimizing the replacement time of reparable system. MOGA should be useful for machinery in general and specifically for reparable system. This objective will be achieved by developing a system that includes a smart combination of techniques by integrating MOGA to yield the optimized replacement time. Another measure to achieve this purpose is implementing MOGA in clustering and imputing missing data to obtain cost data, which could help to provide proper data to forecast cost data for optimization and to identify the optimal replacement time.In the first stage, a two-level MOGA is proposed to optimize clustering to reduce and impute missing cost data. Level one uses a MOGA based on fuzzy c-means to cluster cost data objects based on three main indices. The first is cluster centre outliers; the second is the compactness and separation ( ) of the data points and cluster centres; the third is the intensity of data points belonging to the derived clusters. Level two uses MOGA to impute the missing cost data by using a valid data period from that are reduced data in size. In the second stage, a two-level MOGA is proposed to optimize time series forecasting. Level one implements MOGA based on either an autoregressive integrated moving average (ARIMA) model or a dynamic regression (DR) model. Level two utilizes a MOGA based on different forecasting error rates to identify proper forecasting. These models are applied to simulated data for evaluation since there is no control of the influenced parameters in all of the real cost data. In the final stage, a two-level MOGA is employed to optimize risk-based LCC analysis to find the optimal replacement time for reparable system. Level one uses a MOGA based on a risk model to provide a variation of risk percentages, while level two uses a MOGA based on an LCC model to estimate the optimal reparable system replacement time.The results of the first stage show the best cluster centre optimization for data clustering with low and high intensity. Three cluster centres were selected because these centres have a geometry that is suitable for the highest data reduction of 27%. The best optimized interval is used for imputing missing data. The results of the second stage show the drawbacks of time series forecasting using a MOGA based on the DR model. The MOGA based on the ARIMA model yields better forecasting results. The results of the final stage show the drawbacks of the MOGA based on a risk-based LCC model regarding its estimation. However, the risk-based LCC model offers the possibility of optimizing the replacement schedule.However, MOGA is highly promising for allowing optimization compared with other methods that were investigated in the present thesis.
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| 2. |
- Gerdes, Mike, 1970-
(författare)
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Health Monitoring for Aircraft Systems using Decision Trees and Genetic Evolution
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Reducing unscheduled maintenance is important for aircraft operators. There are significant costs if flights must be delayed or cancelled, for example, if spares are not available and have to be shipped across the world. This thesis describes three methods of aircraft health condition monitoring and prediction; one for system monitoring, one for forecasting and one combining the two other methods for a complete monitoring and prediction process. Together, the three methods allow organizations to forecast possible failures. The first two use decision trees for decision-making and genetic optimization to improve the performance of the decision trees and to reduce the need for human interaction. Decision trees have several advantages: the generated code is quickly and easily processed, it can be altered by human experts without much work, it is readable by humans, and it requires few resources for learning and evaluation. The readability and the ability to modify the results are especially important; special knowledge can be gained and errors produced by the automated code generation can be removed.A large number of data sets is needed for meaningful predictions. This thesis uses two data sources: first, data from existing aircraft sensors, and second, sound and vibration data from additionally installed sensors. It draws on methods from the field of big data and machine learning to analyse and prepare the data sets for the prediction process.
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