Machine learning and deep learning in smart manufacturing: The smart grid paradigm
Industry 4.0 is the new industrial revolution. By connecting every machine and activity
through network sensors to the Internet, a huge amount of data is generated. Machine …
through network sensors to the Internet, a huge amount of data is generated. Machine …
[HTML][HTML] Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals
Vibration measurement and monitoring are essential in a wide variety of applications.
Vibration measurements are critical for diagnosing industrial machinery malfunctions …
Vibration measurements are critical for diagnosing industrial machinery malfunctions …
FGDAE: A new machinery anomaly detection method towards complex operating conditions
Recent studies on machinery anomaly detection only based on normal data training models
have yielded good results in improving operation reliability. However, most of the studies …
have yielded good results in improving operation reliability. However, most of the studies …
Hybrid robust convolutional autoencoder for unsupervised anomaly detection of machine tools under noises
Anomaly detection of machine tools plays a vital role in the machinery industry to sustain
efficient operation and avoid catastrophic failures. Compared to traditional machine learning …
efficient operation and avoid catastrophic failures. Compared to traditional machine learning …
Research review for broad learning system: Algorithms, theory, and applications
In recent years, the appearance of the broad learning system (BLS) is poised to
revolutionize conventional artificial intelligence methods. It represents a step toward building …
revolutionize conventional artificial intelligence methods. It represents a step toward building …
Highly accurate machine fault diagnosis using deep transfer learning
We develop a novel deep learning framework to achieve highly accurate machine fault
diagnosis using transfer learning to enable and accelerate the training of deep neural …
diagnosis using transfer learning to enable and accelerate the training of deep neural …
A survey of predictive maintenance: Systems, purposes and approaches
This paper provides a comprehensive literature review on Predictive Maintenance (PdM)
with emphasis on system architectures, purposes and approaches. In industry, any outages …
with emphasis on system architectures, purposes and approaches. In industry, any outages …
Remaining useful life estimation in prognostics using deep convolution neural networks
Traditionally, system prognostics and health management (PHM) depends on sufficient prior
knowledge of critical components degradation process in order to predict the remaining …
knowledge of critical components degradation process in order to predict the remaining …
Data-driven methods for predictive maintenance of industrial equipment: A survey
W Zhang, D Yang, H Wang - IEEE systems journal, 2019 - ieeexplore.ieee.org
With the tremendous revival of artificial intelligence, predictive maintenance (PdM) based on
data-driven methods has become the most effective solution to address smart manufacturing …
data-driven methods has become the most effective solution to address smart manufacturing …
Autoencoder-based representation learning and its application in intelligent fault diagnosis: A review
Z Yang, B Xu, W Luo, F Chen - Measurement, 2022 - Elsevier
With the increase of the scale and complexity of mechanical equipment, traditional intelligent
fault diagnosis (IFD) based on shallow machine learning methods is unable to meet the …
fault diagnosis (IFD) based on shallow machine learning methods is unable to meet the …