Enabling predictive maintenance integrated production scheduling by operation-specific health prognostics with generative deep learning
TLDR
A generative deep learning model based on the conditional variational autoencoder (CVAE) that can derive an operation-specific health indicator (HI) from large-scale industrial condition monitoring (CM) data is proposed that enables PdM-IPS.About:
This article is published in Journal of Manufacturing Systems.The article was published on 2021-03-02 and is currently open access. It has received 27 citations till now. The article focuses on the topics: Prognostics & Predictive maintenance.read more
Citations
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Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review
TL;DR: In this paper , a systematic review of the recent advancements in mechanical fault diagnosis and prognosis in the manufacturing industry using machine learning methods is presented, and the main advantages of these algorithms include high performance, the ability to uncover complex nonlinear relationships and computational efficiency, while the most important limitation is the reduction in model performance in the presence of concept drift.
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Real-time integrated production-scheduling and maintenance-planning in a flexible job shop with machine deterioration and condition-based maintenance
TL;DR: In this paper, a real-time optimization-based system that utilizes a modified hybrid genetic algorithm, an integrated proactive-reactive optimization model, and hybrid rescheduling policies was proposed.
Journal ArticleDOI
Generative adversarial network for early-stage design flexibility in topology optimization for additive manufacturing
TL;DR: A deep learning-based framework that learns latent similarities between runs in a training set to predict near optimal designs is introduced, enabling efficient wholistic understanding of the problem setup space, which includes both loading conditions and, for the first time in this study, manufacturing process configuration.
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Degradation stage classification via interpretable feature learning
TL;DR: This work proposes a feature learning approach able to effectively extract high-quality features by processing different input signals, and provide useful insights about the most informative domain transformations of the input signals.
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Recent Advances on Machine Learning Applications in Machining Processes
TL;DR: This study aims to present an overall review of the recent research status regarding Machine Learning applications in machining processes, classifying the main problems that may be solved using ML related to the machining quality, energy consumption and conditional monitoring.
References
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Auto-Encoding Variational Bayes
Diederik P. Kingma,Max Welling +1 more
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Xavier Glorot,Yoshua Bengio +1 more
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Proceedings Article
Learning structured output representation using deep conditional generative models
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Semi-Supervised Learning with Deep Generative Models
TL;DR: It is shown that deep generative models and approximate Bayesian inference exploiting recent advances in variational methods can be used to provide significant improvements, making generative approaches highly competitive for semi-supervised learning.