Neural network-based failure rate prediction for De Havilland Dash-8 tires

Al‐Garni, Ahmed Z.; Jamal, Ahmad; Ahmad, Abid M.; Al-Garni, Abdullah M.; Tozan, Mueyyet

doi:10.1016/j.engappai.2006.01.005

Cited by 18 publications

(9 citation statements)

References 15 publications

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“…During work on this project, we learned that one of the airlines, together with a major tire manufacturer, had launched a project to predict tire wear. According to press releases published by the companies [ 5 ], it is planned to use flight data of the airline and tire insights and digital tire wear prediction technologies from manufacturer to determine optimal time to replace tires. The work is aligned with what we are doing with the only difference that in our case the manufacturer is not involved.…”

Section: Tire Wear Prediction Methods Review (Current State)mentioning

confidence: 99%

Predictive Maintenance Model of Refined Aircraft Tires Replacement

Tyncherov

Rozkova

2021

Lecture Notes in Networks and Systems

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Development of new predictive maintenance models in aviation relies on traditional maintenance programs that cost-effectively ensure flight safety. Design individually oriented predictive maintenance programs for each group of units according to the specificity of systems, assemblies and structural elements of modern aircraft requires considering data of behaviour during aircraft operational life cycle (aircraft digital twin). This article describes the current practical approach and proposes a model of predictive maintenance together with airline warehouse stock optimization system and Maintenance Repair and Overhaul (MRO) organization capacity management with particular focus on tires. Analysis of parameters that can potentially affect aircraft nose wheel tires wear and selection of the most relevant factors to build a model for tire replacement forecast using flight data recorders are described in this study. The resulting model is used for the creation of automated production load management system for MRO organization facilities considering their ultimate capacity. The model determines optimal stock or buffer needed for wheel replacement and continuous aircraft fleet operation. The model gives the possibility to calculate the minimum necessary number of spare wheels in the warehouse to ensure continuous operation of a given number of aircraft and a given MRO capacity. As a result of this, I present the Analysis of method’s economic efficiency and evaluate losses associated with early wheel removal and spare wheels capacity.

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Section: Tire Wear Prediction Methods Review (Current State)mentioning

confidence: 99%

Predictive Maintenance Model of Refined Aircraft Tires Replacement

Tyncherov

Rozkova

2021

Lecture Notes in Networks and Systems

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“…Jang (1993) proposed the adaptive network-based fuzzy inference system (ANFIS), a hybrid learning algorithm extensively used in forecasting problems. The ANN model is also used with a back propagation algorithm for predicting failure rates (Al-Garni et al, 2006). Ciarapica and Giacchetta (2006) experimentally used ANN and neuro-fuzzy systems to forecast activities in the rotating machinery preventive maintenance cycles.…”

Section: Use Of Mcdm and Ai Methods In Inspection Planningmentioning

confidence: 99%

Use of MCDM and AI Techniques for Mechanization of In-Service Inspection Planning Process

Seneviratne

Ratnayake

2014

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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The in-service inspection planning process for topside piping equipment of aging oil and gas (O&G) production and process facilities (P&PFs) involves personnel with different kinds of expertise, experience, and knowledge as well as a vast amount of data and information. To simplify the inspection planning process and increase the quality of an inspection program, various industrial organizations as well as researchers have been developing numerous techniques in an isolated fashion to address the challenges pertaining to different activities involved in the inspection planning process. In order to mechanize the overall inspection process, suitable techniques need to be identified for the different activities carried out in a generic inspection planning process. This manuscript discusses the potential use of multi-criteria decision analysis (MCDM) and artificial intelligence (AI) techniques. It also provides evidence about the suitability of AI techniques in relation to fuzzy logic and artificial neural networks for the mechanization of the inspection planning process in a dynamic manner.

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“…Neural networks have been used to fit the nonlinear relationship between input variables and output responses in recent years due to their strong nonlinear fitting capabilities [ 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. Neural networks have been applied in many fields, such as space-based large mirror structure, turbine disks, automotive bushings, etc.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Optical Machine Structure by Backpropagation Neural Network Based on Particle Swarm Optimization and Bayesian Regularization Algorithms

Zhang

Sun

2021

Materials

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Fit of the highly nonlinear functional relationship between input variables and output response is important and challenging for the optical machine structure optimization design process. The backpropagation neural network method based on particle swarm optimization and Bayesian regularization algorithms (called BMPB) is proposed to solve this problem. A prediction model of the mass and first-order modal frequency of the supporting structure is developed using the supporting structure as an example. The first-order modal frequency is used as the constraint condition to optimize the lightweight design of the supporting structure’s mass. Results show that the prediction model has more than 99% accuracy in predicting the mass and the first-order modal frequency of the supporting structure, and converges quickly in the supporting structure’s mass-optimization process. The supporting structure results demonstrate the advantages of the method proposed in the article in terms of high accuracy and efficiency. The study in this paper provides an effective method for the optimized design of optical machine structures.

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Neural network-based failure rate prediction for De Havilland Dash-8 tires

Cited by 18 publications

References 15 publications

Predictive Maintenance Model of Refined Aircraft Tires Replacement

Predictive Maintenance Model of Refined Aircraft Tires Replacement

Use of MCDM and AI Techniques for Mechanization of In-Service Inspection Planning Process

Optimization of Optical Machine Structure by Backpropagation Neural Network Based on Particle Swarm Optimization and Bayesian Regularization Algorithms

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