Artificial neural network application of modeling failure rate for Boeing 737 tires

Al‐Garni, Ahmed Z.; Jamal, Ahmad

doi:10.1002/qre.1114

Cited by 26 publications

(22 citation statements)

References 12 publications

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“…Briefly, forecasting of failure time series covers many domains and methods, such as artificial neural network in the field of water conservancy [2] and software engineering [3]; singular spectrum analysis [1], support vector regression machine [4], artificial neural network [5][6][7]in the field of reliability; vector autoregressive moving average model in the field of road traffic [8,9]. These methods are all based on the original data as input, and can be roughly divided into two categories: statistics and artificial intelligence.…”

Section: Introductionmentioning

confidence: 99%

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A failure time series prediction method based on UML model

Wang

Liu

Xiong³

et al. 2015

2015 4th International Conference on Computer Science and Network Technology (ICCSNT)

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Due to significant industrial demands toward flight safety and airplane maintenance quality, improving airplane's reliability in usage stage has become an important activity and the research domain is rapidly evolving. In this paper eighteen years' field data, gathered from the maintenance phase of a Boeing 737 aircraft, is prepared as time-to-failure series. Then automatic processing models based on unified modeling language (UML) are presented to cope with this data, which incorporate three methods of Holt-Winters, autoregressive integrated moving average (ARIMA), and singular spectrum analysis (SSA). Each method's modeling and forecasting process is analyzed, as well as SSA's parameter optimization. Furthermore, a hybrid processing model is built to take advantage of each method. The results are compared and evaluated by root mean square error (RMSE) and show that hybrid methods are more adaptive than single methods, and valid that the proposed processing models are feasible and efficient to deal with the failure time series.

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Section: Introductionmentioning

confidence: 99%

“…The research object is also very limited, such as artificial neural network method to Boeing 737 tires failure rate modeling and forecasting [7]. In addition, previous studies tend to focus on the merits of the method itself, which usually depend on the forecast accuracy evaluation.…”

Section: Introductionmentioning

confidence: 99%

A failure time series prediction method based on UML model

Wang

Liu

Xiong³

et al. 2015

2015 4th International Conference on Computer Science and Network Technology (ICCSNT)

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“…Neural networks can be, generally, applied to classical reliability problems, such as estimating the failure rate [5], time-to-failure or cumulative time-to-failure [12]. These predictions, then, are valid population wide.…”

Section: Introductionmentioning

confidence: 99%

Predicting component reliability and level of degradation with complex-valued neural networks

Fink

Zio

Weidmann

2014

Reliability Engineering & System Safety

104

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. Predicting component reliability and level of degradation with complex-valued neural networks. Reliability Engineering and System Safety, Elsevier, 2014, 121, pp.198-206 AbstractIn this paper, multilayer feedforward neural networks based on multi-valued neurons (MLMVN), a specific type of complex valued neural networks, are proposed to be applied to reliability and degradation prediction problems, formulated as time series. MLMVN have demonstrated their ability to extract complex dynamic patterns from time series data for mid-and long-term predictions in several applications and benchmark studies. To the authors' knowledge, it is the first time that MLMVN are applied for reliability and degradation prediction. MLMVN is applied to a case study of predicting the level of degradation of railway track turnouts using real data. The performance of the algorithms is first evaluated using benchmark study data. The results obtained in the reliability prediction study of the benchmark data show that MLMVN outperforms other machine learning algorithms in terms of prediction precision and is also able to perform multi-step ahead predictions, as opposed to the previously best performing benchmark studies which only performed up to two-step ahead predictions. For the railway turnout application, MLMVN confirm the good performance in the long-term prediction of degradation and does not show accumulating errors for multi-step ahead predictions.

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“…Chen, 2007;Xue et al, 2003, Al-Garni andJamal, 2011). Nowadays, the above mentioned techniques are widely used as mathematical tools that offer an alternative way to deal with complex systems.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Failure Rates of Electronic Goods by Using Decomposition and Fuzzy Clustering of Empirical Failure Rate Curves

Jónás

Árva

Tóth

2017

Period. Polytech. Soc. Man. Sci.

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In this paper a novel methodology founded on the joint application of analytic decomposition of empirical failure rate time series and soft computational techniques is introduced in order to predict bathtub-shaped failure rate curves of consumer electronic goods. Empirical failure rate time series are modeled by a flexible function the parameters of which have geometric interpretations, and so the model parameters grab the characteristics of bathtub-shaped failure rate curves. The so-called typical standardized failure rate curve models, which are derived from the model functions through standardization and fuzzy clustering processes, are applied to predict failure rate curves of consumer electronics in a method that combines analytic curve fitting and soft computing techniques. The forecasting capability of the introduced method was tested on real-life data. Based on the empirical results from practical applications, the introduced method can be considered as a new, alternative reliability prediction technique the application of which can support the electronic repair service providers to plan their resources in the long run.

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Artificial neural network application of modeling failure rate for Boeing 737 tires

Cited by 26 publications

References 12 publications

A failure time series prediction method based on UML model

A failure time series prediction method based on UML model

Predicting component reliability and level of degradation with complex-valued neural networks

Forecasting Failure Rates of Electronic Goods by Using Decomposition and Fuzzy Clustering of Empirical Failure Rate Curves

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