Failure Rate Analysis of Boeing 737 Brakes Employing Neural Network (original) (raw)
Related papers
2015
The failure rate analysis of brake assemblies of a commercial airplane, i.e., Boeing 737, is analyzed using the artificial neural network and Weibull regression models. One-layered feed-forward back-propagation algorithm for artificial neural network whereas three parameters model for Weibull are used for the analysis. Three years of data are used for model building and validation. The results show that the failure rate predicted by neural network is closer in agreement with the actual data than the failure rate predicted by the Weibull model. Results also indicate that neural network can be effectively integrated into an aviation maintenance facility computerized material requirement planning system to forecast the number of brake assemblies needed for a given planning horizon.
Neural Network Model for Planned Replacement of Boeing 737 Brakes
2007
A bstract The failure rate analysis of brake assemblies of a commercial airplane, i.e., Boeing 737, is analyzed using the Artificial Neural Network and Weibull regression models. One-layered feed-forward back-propagation algorithm for artificial neural network whereas three parameters model for Weibull are used for the analysis. Three years of data are used for model building and validation. The results show that the failure rate predicted by neural network is closer in agreement with the actual data than the failure rate predicted by the Weibull model. Results also indicate that neural network can be effectively integrated into aviation cost effective maintenance facility computerized material requirement planning system to forecast the number of brake assemblies needed for a given planning horizon.
Reliability analysis of aeroplane brakes
Quality and Reliability Engineering International, 1999
The reliability of wear/failure data of brake assemblies of a commercial aeroplane (Boeing 737) is analysed using the Weibull model. This model can be effectively integrated into an aviation maintenance facility computerized material requirement planning system to forecast the number of brake assemblies needed for a given planning horizon.
Neural network prognostics model for industrial equipment maintenance
… Systems (HIS), 2011 …, 2011
This paper presents a new prognostics model based on neural network technique for supporting industrial maintenance decision. In this study, the probabilities of failure based on the real condition equipment are initially calculated by using logistic regression method. The failure probabilities are subsequently utilized as input for prognostics model to predict the future value of failure condition and then used to estimate remaining useful lifetime of equipment. By having a time series of predicted failure probability, the failure distribution can be generated and used in the maintenance cost model to decide the optimal time to do maintenance. The proposed prognostic model is implemented in the industrial equipment known as autoclave burner. The result from the model reveals that it can give prior warnings and indication to the maintenance department to take an appropriate decision instead of dealing with the failures while the autoclave burner is still operating. This significant contribution provides new insights into the maintenance strategy which enables the use of existing condition data from industrial equipment and prognostics approach
Sensors
Predictive maintenance of production lines is important to early detect possible defects and thus identify and apply the required maintenance activities to avoid possible breakdowns. An important concern in predictive maintenance is the prediction of remaining useful life (RUL), which is an estimate of the number of remaining years that a component in a production line is estimated to be able to function in accordance with its intended purpose before warranting replacement. In this study, we propose a novel machine learning-based approach for automating the prediction of the failure of equipment in continuous production lines. The proposed model applies normalization and principle component analysis during the pre-processing stage, utilizes interpolation, uses grid search for parameter optimization, and is built with multilayer perceptron neural network (MLP) machine learning algorithm. We have evaluated the approach using a case study research to predict the RUL of engines on NASA ...
Regression and Classification Model Based Predictive Maintenance of Aircrafts Using Neural Network
International Journal of Innovative Research in Computer Science & Technology, 2022
One of the key objectives of today's businesses and mills is to predict machine problems. Failures must be avoided, because downtimes represent expensive expenses and a loss of productivity. This is why the number of remaining cycles (RULs) until the failure occurs is vital in machine maintenance. The estimations of the RUL should be based on earlier observations, whenever possible under the same conditions. In the research of RUL estimates, the creation of systems that monitor current equipment conditions is becoming crucial. I employed Long Short Term Memory (LSTM) in my project to determine an aircraft's remaining usable lives. The aircraft's functioning condition is also forecast. The former is done by a regression method, using a classification methodology predicted by working circumstances. In order to estimate operating conditions and remaining usable life of the aircraft, data utilized for LSTM models training are derived from 21 aircraft sensor readings located ...
Neural networks for aeronautical components maintenance and management
Aeronautic systems maintenance methodologies and management criteria for the substitution of components are continuously evolving. At first the so called hard time management procedures were utilized for establishing time based components substitutions. Even if safety conditions appeared conservative, costs grew up, both for the anticipated substitution of still good components, and for the stocking critic conditions. Operational reliability techniques were therefore introduced in order to permit components constant monitoring for evaluating the effective capacity to ensure the mission. Those techniques are statistically based. The paper attempts to demonstrate existing possibilities for the application in reliability of neural networks instead of statistical more traditional tools in order to preview the number of faults that might verify on aeronautical on board components in a certain time lapse.
Mathematical Method of Artificial Neural Networks in Aircraft Maintenance, Repair and Overhaul
TEM Journal, 2020
Aircraft Maintenance, Repair and Overhaul (MRO) is one of the major components of the Aircraft Life Cycle Cost (LCC). Increasing the efficiency of MRO, as well as reducing MRO cost, is one of the main ways to reduce LCC. In modern aviation technology complexity of Avionics and its maintenance increase. Traditional methods of failure prediction are difficult to apply in complex technical systems which make it necessary to reduce MRO interval. This research proposed the mathematical method of Artificial Neural Networks (ANN) as a possible solution to this problem. The avionics of Unmanned Aerial Vehicle (UAV) is the research object. The reliability and forecasting of failures by traditional and ANN methods have been analyzed, and results comparison are received. The study suggests that the method used is suitable for solving this problem. The obtained results show a high degree of reliability. Further research is proposed to scale to more complex avionics aircraft. The introduction of...
A reliability study of Fokker F-27 airplane brakes
Reliability Engineering & System Safety, 1997
The wear/failure data of brake assemblies of a commercial type airplane (Fokker F-27) is statistically analyzed, and interpreted in a reliability framework. A three parameter Weibull model is used for reliability characterization of the brake assemblies. A spreadsheet format of analysis is proposed to analyze the data. This reliability model can be effectively integrated into an aviation facility computerized material requirement planning system to forecast the number of brake assemblies needed for a given planning horizon.
Artificial neural network application of modeling failure rate for Boeing 737 tires
Quality and Reliability Engineering International, 2011
This paper presents an application of artificial neural network technique for predicting the failure rate of Boeing 737 tires. For this purpose, an artificial neural network model utilizing the feed-forward backpropagation algorithm as a learning rule is developed. The inputs to the neural network are the independent variables and the output is the failure rate of the tires. Two years of data is used for failure rate prediction model and validation. Model validation, which reflects the suitability of the model for future predictions, is performed by comparing the predictions of the model with that of Weibull regression model. The results show that the failure rate predicted by the artificial neural network is closer in agreement with the actual data than the failure rate predicted by the Weibull model. The present work also identifies some of the common tire failures and presents representative results based on the established model for the most frequently occurring tire failure.