Aviation Maintenance Instructional Design: How to Teach the Millennial and Gen-Z Cohorts

The next two decades of air travel will see an increase in passenger demand and advances in aircraft technologies and infrastructure. Future aviation maintenance technicians will work in a highly technical and demanding environment, and their training must account for these challenges and generational characteristics. This paper discusses trends that will shape the work environment and characteristics of millennials and Gen-Z and makes recommendations for training the next cohort of aviation maintenance technicians

Predictive model for the degradation state of a hydraulic system with dimensionality reduction

In recent years, the optimization in the use of resources has a key role in achieving a bigger marginality, reducing the operative costs. Due to the advances in the data science field, even the maintenance context is living important changes. The predictive maintenance and the condition-based maintenance can overcome the classic traditional maintenance methods, like the time-based maintenance or the corrective maintenance, with respect to the first intervention, reducing the costs for unscheduled maintenance, manpower, or loss of production and extending the useful life of the components. Based on these presuppositions, the paper proposes the development of a predictive model for the degradation state of the components of a complex hydraulic system, with some tests and some suggestions about the dimensionality reduction. The system has four known types of breakdown, with different degrees of severity; moreover, a fifth parameter represents whether the cycle has reached stable conditions or not

Predictive Maintenance in Industry 4.0: Current Themes

Copyright © 2021 The Authors. The Fourth Industrial Revolution (Industry 4.0) has created significant technological growth for manufacturing organizations worldwide, attracting important attention from the research community. Industrial automation and the introduction of smart digital technologies to traditional manufacturing processes has led to a generation of intelligent production methods to engineer smart products. In the last few decades, the term ‘maintenance’ has evolved with researchers offering various perspectives. The aim of this paper is to identify the issues related to industrial maintenance, uncovering its historical evolution, and providing a perspective for new types of industrial maintenance linked to Industry 4.0

Vibration analysis approach to model incremental wear and associated sound in multi-contact sliding friction mechanisms

This paper proposes a simplistic approach toward estimating incremental wear in a multi-contact scenario using a vibrational analysis approach and in turn goes a step forward to model its associated sound. Predicted wear depth and frictional sound are compared to the experimental values obtained using a standardized pin-on-disc tribometer setup affixed with a free-field microphone to capture air-borne noise. The results show good conformity between the proposed analytical model values and the standardized experiments, hence ensuing that within certain limitations, the proposed model and the intended approach can effectively be used as a good estimator of wear and its sound in a multi-contact scenario

Manutenção aeronáutica preditiva: procedimentos, técnicas e business models

A necessidade de optimização do tempo de imobilização das aeronaves para acções de manutenção, fruto da concorrência para uma constante disponibilidade dos recursos, a par das oportunidades resultantes da big data e da IoT, exorta a reflexão acerca da abordagem mais eficiente a adoptar na resolução antecipada de avarias. O presente trabalho discute a premência da manutenção preditiva entre os agentes da aviação civil, propondo um conjunto de procedimentos, técnicas e business models a aplicar pelos decisores de planeamento e estratégias de manutenção dentro de uma companhia aérea. A metodologia utilizada parte da análise de artigos científicos e de revistas da especialidade. Devido ao carácter exploratório do tema, foram realizadas entrevistas estruturadas e nãoestruturadas a profissionais e investigadores especialistas nesta temática para compreender o problema em análise e validar as sugestões apresentadas. Como técnicas de manutenção preditiva são propostas: 1) estipulação de prognósticos quanto ao tempo estimado de operacionalidade de um componente com base no desempenho esperado e nas condições de funcionalidade; 2) classificação dos prognósticos por estratégias opportunity-based e on-condition de acordo com métodos data-driven e model-based; 3) definição do teor de dados a alocar e o papel da IoT na recolha e transmissão destes; 4) as-aservice como hipóteses genéricas e extensíveis de business models; e discutidas práticas relevantes em curso. As sugestões apresentadas permitem a criação de valor através da manutenção preditiva, ponderando os desafios associados à partilha de dados, procedimentos legais e impacto financeiro. É sugerida para pesquisa o desenvolvimento da manutenção prescritiva através da inteligência artificial.The need to optimize immobilization time of aircraft maintenance actions, due to increased competition for constant availability of resources, together with the opportunities created by big data analysis and the IoT, is leading to a reflection on the most efficient way to proceed with respect to premature termination of mechanics faults. This study discusses the relevance of predictive approach to players of civil aviation and proposes a set of procedures, techniques and business models to be applied by decision-makers regarding maintenance planning and strategies within airline companies. The choice for the methodology applied is based on scientific articles and specialty magazines. Due to the exploratory content, a set of structured and non-structured interviews are performed with industry experts and researchers specialized on this topic in order to understand the problem and validate the suggestions. The following predictive maintenance techniques are proposed: 1) definition of prognostics regarding the estimated time of operability of a given component according to its expected performance and its functional conditions; 2) classification of prognostics by opportunity-based and on-condition strategies according to data-driven and model-based methods; 3) definition of content of data to be allocated and the role of IoT in data collection and transmission; 4) as-aservice as generic and extendable hypotheses of business models; and are discussed ongoing practices. These proposals represent a step forward towards value-creation through predictive maintenance, considering the challenges associated with data sharing, legal procedures and financial impact. For future research is proposed the development of prescriptive maintenance through artificial intelligence

Application of data analytics for predictive maintenance in aerospace: an approach to imbalanced learning.

The use of aircraft operational logs to predict potential failure that may lead to disruption poses many challenges and has yet to be fully explored. These logs are captured during each flight and contain streamed data from various aircraft subsystems relating to status and warning indicators. They may, therefore, be regarded as complex multivariate time-series data. Given that aircraft are high-integrity assets, failures are extremely rare, and hence the distribution of relevant data containing prior indicators will be highly skewed to the normal (healthy) case. This will present a significant challenge in using data-driven techniques to 'learning' relationships/patterns that depict fault scenarios since the model will be biased to the heavily weighted no-fault outcomes. This thesis aims to develop a predictive model for aircraft component failure utilising data from the aircraft central maintenance system (ACMS). The initial objective is to determine the suitability of the ACMS data for predictive maintenance modelling. An exploratory analysis of the data revealed several inherent irregularities, including an extreme data imbalance problem, irregular patterns and trends, class overlapping, and small class disjunct, all of which are significant drawbacks for traditional machine learning algorithms, resulting in low-performance models. Four novel advanced imbalanced classification techniques are developed to handle the identified data irregularities. The first algorithm focuses on pattern extraction and uses bootstrapping to oversample the minority class; the second algorithm employs the balanced calibrated hybrid ensemble technique to overcome class overlapping and small class disjunct; the third algorithm uses a derived loss function and new network architecture to handle extremely imbalanced ratios in deep neural networks; and finally, a deep reinforcement learning approach for imbalanced classification problems in log- based datasets is developed. An ACMS dataset and its accompanying maintenance records were used to validate the proposed algorithms. The research's overall finding indicates that an advanced method for handling extremely imbalanced problems using the log-based ACMS datasets is viable for developing robust data-driven predictive maintenance models for aircraft component failure. When the four implementations were compared, deep reinforcement learning (DRL) strategies, specifically the proposed double deep State-action-reward-state-action with prioritised experience reply memory (DDSARSA+PER), outperformed other methods in terms of false-positive and false-negative rates for all the components considered. The validation result further suggests that the DDSARSA+PER model is capable of predicting around 90% of aircraft component replacements with a 0.005 false-negative rate in both A330 and A320 aircraft families studied in this researchPhD in Transport System