Application of Rough Sets Algorithms to Prediction of Aircraft Component Failure

Peña, José M.; Létourneau, Sylvain; Famili, Fazel

doi:10.1007/3-540-48412-4_40

Cited by 17 publications

(9 citation statements)

References 9 publications

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“…Consequently, this fault cannot be diagnosed through only a single variable. The existing fault diagnosis and prediction methods for the non‐periodic fault can be divided into four classes: the time series based method , early fault detection based method , qualitative analysis based method , and other methods. All of these methods imply that one can deduce the future operation condition of a piece of equipment according to its current operation status.…”

Section: Introductionmentioning

confidence: 99%

Fault Prediction and Maintenance of Non‐Periodic Incipient Fault for Hydraulic Tube Tester

Zhao¹,

Zhang²,

Wang³

et al. 2014

Asian Journal of Control

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This paper is concerned with fault prediction and maintenance for the hydraulic tube tester according to the process variables. For the two types of incipient faults of this machine, it is more difficult to predict the non-periodic incipient fault and make a predictive maintenance decision in comparison with the periodic incipient fault. For the non-periodic incipient fault, a new probabilistic fault prediction method is proposed using a multivariate principle component analysis and Bayesian auto-regression model. Then, a new predictive maintenance policy is developed, based on the results of the probabilistic fault prediction approach, which implies the evolvement of system deterioration caused by the non-periodic incipient fault. Simulation results demonstrate the effectiveness of the method proposed in this paper.

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Section: Introductionmentioning

confidence: 99%

Fault Prediction and Maintenance of Non‐Periodic Incipient Fault for Hydraulic Tube Tester

Zhao¹,

Zhang²,

Wang³

et al. 2014

Asian Journal of Control

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“…However, relational research references are still deficient [1] . General speaking, fault prediction methods can be divided into four types, i.e., methods based on time series prediction [ 3 , 4 , 5 , 6 ] , methods based on incipient fault prediction [7] , methods based on qualitative analysis [8] , and other methods. All these methods are proposed to solve incipient fault prediction which happens following time slowly and shares some useful information reflecting system running conditions.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic fault prediction of incipient fault

Zhao

Wang

Jia

et al. 2010

2010 Chinese Control and Decision Conference

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In this work, a probabilistic fault prediction approach is presented for prediction of incipient fault in an uncertain way. The approach has two stages. In the first stage, normal data is analyzed by principle component analysis (PCA) to get control limits of the statistics of 2 T and SPE . In the second stage, fault data starts by PCA so as to derive the statistics of 2 T and SPE . Then, the samplings of these two statistics obeying some certain prediction distribution are obtained using Bayesian AR model on the basis of the Winbugs software. At last, one-step prediction fault probabilities are estimated by kernel density estimation method according to the statistics' corresponding control limits. The prediction performance of this approach is illustrated using the data from the simulator of the Tennessee Eastman process.

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“…Rough sets have been used in various biomedical applications [9,10,11], other applications of RST include the prediction of aircraft component failure, fault diagnosis and stock market analysis [12,13,14]. But in most applications, RST is used primarily for prediction.…”

Section: Introductionmentioning

confidence: 99%

Using Genetic Algorithms to Optimise Rough Set Partition Sizes for HIV Data Analysis

Crossingham

Marwala

2008

Advances in Intelligent and Distributed Computing

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In this paper, we present a method to optimise rough set partition sizes, to which rule extraction is performed on HIV data. The genetic algorithm optimisation technique is used to determine the partition sizes of a rough set in order to maximise the rough sets prediction accuracy. The proposed method is tested on a set of demographic properties of individuals obtained from the South African antenatal survey. Six demographic variables were used in the analysis, these variables are; race, age of mother, education, gravidity, parity, and age of father, with the outcome or decision being either HIV positive or negative. Rough set theory is chosen based on the fact that it is easy to interpret the extracted rules. The prediction accuracy of equal width bin partitioning is 57.7% while the accuracy achieved after optimising the partitions is 72.8%. Several other methods have been used to analyse the HIV data and their results are stated and compared to that of rough set theory (RST).

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Application of Rough Sets Algorithms to Prediction of Aircraft Component Failure

Cited by 17 publications

References 9 publications

Fault Prediction and Maintenance of Non‐Periodic Incipient Fault for Hydraulic Tube Tester

Fault Prediction and Maintenance of Non‐Periodic Incipient Fault for Hydraulic Tube Tester

Probabilistic fault prediction of incipient fault

Using Genetic Algorithms to Optimise Rough Set Partition Sizes for HIV Data Analysis

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