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- Aerospace (2)
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- Automatic speech recognition (1)
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- Data-Driven Intelligent Manufacturing (1)
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- Predictive maintenance (1)
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Articles 1 - 4 of 4
Full-Text Articles in Maintenance Technology
Predictive Maintenance Analysis Of Turbofan Engine Sensor Data, Stanley A. Melkumian
Predictive Maintenance Analysis Of Turbofan Engine Sensor Data, Stanley A. Melkumian
The Journal of Purdue Undergraduate Research
Predictive maintenance in aviation and aerospace applications is among the most explored problems in machine learning (ML) and artificial intelligence (AI), and datasets such as NASA’s C-MAPPS turbofan engine degradation simulation data have proven invaluable, helping researchers explore numerous questions on engine performance, maintenance, and failure. The purpose of this study was to extend the current research on predicting the remaining useful life (RUL) of engines and their risk classification. Starting with simple yet under-investigated nonlinear survival and random forest models, the analysis implemented eXtreme Gradient Boosting (XGBoost) and long short-term memory (LSTM) from TensorFlow’s Keras library. For both regression …
Low-Resource Automatic Speech Recognition Domain Adaptation – A Case-Study In Aviation Maintenance, Nadine Amin, Tracy L. Yother, Julia Rayz
Low-Resource Automatic Speech Recognition Domain Adaptation – A Case-Study In Aviation Maintenance, Nadine Amin, Tracy L. Yother, Julia Rayz
Journal of Aviation/Aerospace Education & Research
With timeliness and efficiency being critical in the aviation maintenance industry, the need has been growing for smart technological solutions that optimize and streamline the different underlying tasks (Bergkvist & Sabbagh, 2021). One such task is the technical documentation of the performed maintenance operations (Chandola et al., 2022). Instead of manual documentation, voice tools that transcribe spoken logbook entries allow technicians to document their work right away in a hands-free and time efficient manner. However, an accurate automatic speech recognition (ASR) model requires large training corpora (Siyaev & Jo, 2021a), which are lacking in the domain of aviation maintenance. In …
Generalizable And Adaptable Data-Driven Methods For Overcoming Barriers To Practical Industrial Condition Monitoring, Matthew B. Russell
Generalizable And Adaptable Data-Driven Methods For Overcoming Barriers To Practical Industrial Condition Monitoring, Matthew B. Russell
Theses and Dissertations--Electrical and Computer Engineering
The future of smart manufacturing relies on predictive maintenance systems that intelligently minimize expensive downtime through timely assessment of machine condition. Deep Learning (DL) has achieved excellent performance in industrial condition monitoring experiments, but the constraints of the manufacturing environment prevent many algorithms from being practically deployed on the factory floor. Ubiquitous sensing from online machines generates high velocity data streams that require new techniques for efficient transmission and storage. Despite these ever-increasing data lakes, many applications still lack the data needed for training DL fault diagnosis and wear tracking models since most data is unlabeled and only from nominal …
Aessa Young Professionals Forum Webinar “Technologies And Skills That Will Gearup The Aerospace Industry Post Pandemic” - A Global Perspective With An Emphasis On South Africa October 2021, Linda Vee Weiland
Publications
A webinar presentation for AeSSA Young Professionals.