[HTML][HTML] Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0
Recently, with the emergence of Industry 4.0 (I4. 0), smart systems, machine learning (ML)
within artificial intelligence (AI), predictive maintenance (PdM) approaches have been …
within artificial intelligence (AI), predictive maintenance (PdM) approaches have been …
Applications of machine learning to machine fault diagnosis: A review and roadmap
Intelligent fault diagnosis (IFD) refers to applications of machine learning theories to
machine fault diagnosis. This is a promising way to release the contribution from human …
machine fault diagnosis. This is a promising way to release the contribution from human …
MAD-GAN: Multivariate anomaly detection for time series data with generative adversarial networks
Many real-world cyber-physical systems (CPSs) are engineered for mission-critical tasks
and usually are prime targets for cyber-attacks. The rich sensor data in CPSs can be …
and usually are prime targets for cyber-attacks. The rich sensor data in CPSs can be …
A review on fault detection and diagnosis techniques: basics and beyond
Safety and reliability are absolutely important for modern sophisticated systems and
technologies. Therefore, malfunction monitoring capabilities are instilled in the system for …
technologies. Therefore, malfunction monitoring capabilities are instilled in the system for …
Effectively detecting operational anomalies in large-scale IoT data infrastructures by using a GAN-based predictive model
Quality of data services is crucial for operational large-scale internet-of-things (IoT) research
data infrastructure, in particular when serving large amounts of distributed users. Effectively …
data infrastructure, in particular when serving large amounts of distributed users. Effectively …
A transfer convolutional neural network for fault diagnosis based on ResNet-50
With the rapid development of smart manufacturing, data-driven fault diagnosis has attracted
increasing attentions. As one of the most popular methods applied in fault diagnosis, deep …
increasing attentions. As one of the most popular methods applied in fault diagnosis, deep …
A new convolutional neural network-based data-driven fault diagnosis method
Fault diagnosis is vital in manufacturing system, since early detections on the emerging
problem can save invaluable time and cost. With the development of smart manufacturing …
problem can save invaluable time and cost. With the development of smart manufacturing …
Tadgan: Time series anomaly detection using generative adversarial networks
Time series anomalies can offer information relevant to critical situations facing various
fields, from finance and aerospace to the IT, security, and medical domains. However …
fields, from finance and aerospace to the IT, security, and medical domains. However …
Understanding and learning discriminant features based on multiattention 1DCNN for wheelset bearing fault diagnosis
Recently, deep-learning-based fault diagnosis methods have been widely studied for rolling
bearings. However, these neural networks are lack of interpretability for fault diagnosis …
bearings. However, these neural networks are lack of interpretability for fault diagnosis …
A new deep transfer learning based on sparse auto-encoder for fault diagnosis
Fault diagnosis plays an important role in modern industry. With the development of smart
manufacturing, the data-driven fault diagnosis becomes hot. However, traditional methods …
manufacturing, the data-driven fault diagnosis becomes hot. However, traditional methods …