RUL Prediction Using a Fusion of Attention-Based Convolutional Variational AutoEncoder and Ensemble Learning Classifier

Remadna, Ikram; Terrissa, Labib Sadek; Masry, Zeina Al; Zerhouni, Noureddine

doi:10.1109/tr.2022.3190639

Cited by 23 publications

(7 citation statements)

References 81 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A simple architecture of AE is illustrated in Figure 5 . AE can also be divided into standard AE [ 38 , 39 , 40 ], denoising AE [ 41 ], sparse AE [ 42 ], variational AE [ 43 ] and contractive AE [ 44 ], etc.…”

Section: Part I: Unsupervised DL Methods For Intelligent Industrial Fdpmentioning

confidence: 99%

Deep Learning Techniques in Intelligent Fault Diagnosis and Prognosis for Industrial Systems: A Review

Qiu

Cui

Ping

et al. 2023

Sensors

View full text Add to dashboard Cite

Fault diagnosis and prognosis (FDP) tries to recognize and locate the faults from the captured sensory data, and also predict their failures in advance, which can greatly help to take appropriate actions for maintenance and avoid serious consequences in industrial systems. In recent years, deep learning methods are being widely introduced into FDP due to the powerful feature representation ability, and its rapid development is bringing new opportunities to the promotion of FDP. In order to facilitate the related research, we give a summary of recent advances in deep learning techniques for industrial FDP in this paper. Related concepts and formulations of FDP are firstly given. Seven commonly used deep learning architectures, especially the emerging generative adversarial network, transformer, and graph neural network, are reviewed. Finally, we give insights into the challenges in current applications of deep learning-based methods from four different aspects of imbalanced data, compound fault types, multimodal data fusion, and edge device implementation, and provide possible solutions, respectively. This paper tries to give a comprehensive guideline for further research into the problem of intelligent industrial FDP for the community.

show abstract

Section: Part I: Unsupervised DL Methods For Intelligent Industrial Fdpmentioning

confidence: 99%

Deep Learning Techniques in Intelligent Fault Diagnosis and Prognosis for Industrial Systems: A Review

Qiu

Cui

Ping

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Evaluating models requires calculating some measures after training and validation to see if they are ready to use. Performance evaluation included accuracy, precision, recall, and F1-score [34], [35]. This subsection describes evaluation metrics: TP, TN refers to true positive and negative, whereas FP, FN refers to false positive and negative.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Empowering customer satisfaction chatbot using deep learning and sentiment analysis

Merizig,

Belouaar,

Mghezzi Bakhouche

et al. 2024

Bulletin EEI

View full text Add to dashboard Cite

The rapid advancement of technology holds great promise for various types of users, clients, or service providers. Intelligent robots, whether virtual or physical, can simplify the reservation process. With the development of machines and processing tools, natural language processing (NLP) and natural language understanding (NLU) have emerged to help people comprehend spoken language through machines. In order to facilitate seamless human-machine interaction, we aim to address customer needs through a chatbot. The objective of this paper is to incorporate sentiment analysis techniques with deep learning algorithms to cater to customers’ needs during message exchanges. This study aims to create an intelligent chatbot to engage customers during their routine operations and offer support. In addition, it offers to companies a manner to detect sarcastic messages. The proposed chatbot utilizes deep learning techniques to predict users’ intentions based on the questions asked and provide a helpful and convenient answer. A new chatbot for the customer is presented to overcome with challenges related to a wrong statement like sarcastic one and feedback towards user messages. A comparison between deep and transfer learning gives a new insight to include sentiments and sarcasm detection in the conversion process.

show abstract

“…The method used in this section for modelling and analyzing the data is the Variational Autoencoder with Convolutional Neural Network (VAE CNN). The VAE CNN is a powerful model that combines the advantages of variational autoencoders and convolutional neural networks to learn a latent representation of the data and generate accurate reconstructions [27,28].…”

Section: Variational Autoencoder With Convolutional Neural Network (V...mentioning

confidence: 99%

Enhancing Fault Detection in CNC Machinery: A Deep Learning and Genetic Algorithm Approach

Mbilong,

Aarab,

Belouadha

et al. 2023

ISI

View full text Add to dashboard Cite

This paper introduces a deep learning-based approach (DLBA) tailored for fault detection and condition monitoring in industrial machinery. The presented DLBA architecture is assessed utilizing a dataset derived from a CNC milling machine, as part of the University of Michigan's System-level Manufacturing and Automation Research Testbed (SMART). The results underscore the substantial efficacy of the LSTM model, evidenced by a precision of 94% and an F1 score of 95%. These findings serve as a robust foundation for the identification of CNC machining failures within the manufacturing industry. The DLBA architecture constitutes a comprehensive framework for efficient fault detection, incorporating a variety of network models, including MLP, CNN, CNN auto-encoder, LSTM, and ResNet. Each model leverages its unique strengths in the analysis of complex data. Genetic Algorithm (GA) optimization is employed for parameter tuning across all models, enhancing their performance. The findings from this study contribute significantly to the development of more reliable and cost-effective predictive maintenance systems, enabling manufacturers to detect faults early on, thus preventing expensive downtime and mitigating safety risks.

show abstract

RUL Prediction Using a Fusion of Attention-Based Convolutional Variational AutoEncoder and Ensemble Learning Classifier

Cited by 23 publications

References 81 publications

Deep Learning Techniques in Intelligent Fault Diagnosis and Prognosis for Industrial Systems: A Review

Deep Learning Techniques in Intelligent Fault Diagnosis and Prognosis for Industrial Systems: A Review

Empowering customer satisfaction chatbot using deep learning and sentiment analysis

Enhancing Fault Detection in CNC Machinery: A Deep Learning and Genetic Algorithm Approach

Contact Info

Product

Resources

About