Small data challenges for intelligent prognostics and health management: a review
Prognostics and health management (PHM) is critical for enhancing equipment reliability
and reducing maintenance costs, and research on intelligent PHM has made significant …
and reducing maintenance costs, and research on intelligent PHM has made significant …
Conditional feature disentanglement learning for anomaly detection in machines operating under time-varying conditions
Anomaly detection (AD) is an important task of machines' condition monitoring (CM). Data-
driven policies can be used in a more intelligent way to achieve anomaly detection and …
driven policies can be used in a more intelligent way to achieve anomaly detection and …
Matching Pursuit Network: An Interpretable Sparse Time–Frequency Representation Method Toward Mechanical Fault Diagnosis
Rotatory machinery commonly operates in complex environments with strong noise and
variable working conditions. Time–frequency representation offers a valuable method for …
variable working conditions. Time–frequency representation offers a valuable method for …
Scalable anomaly-based intrusion detection for secure Internet of Things using generative adversarial networks in fog environment
W Yao, H Shi, H Zhao - Journal of Network and Computer Applications, 2023 - Elsevier
The data generated exponentially by a massive number of devices in the Internet of Things
(IoT) are extremely high-dimensional, large-scale, non-labeled, which poses great …
(IoT) are extremely high-dimensional, large-scale, non-labeled, which poses great …
A critical review on system architecture, techniques, trends and challenges in intelligent predictive maintenance
Traditional maintenance strategies risk unforeseen failure, sophisticated physics-based
modeling, and manual feature extraction. Early detection and accurate predictions of …
modeling, and manual feature extraction. Early detection and accurate predictions of …
A multivariate anomaly detector for satellite telemetry data using temporal attention-based lstm autoencoder
Z Xu, Z Cheng, B Guo - IEEE Transactions on Instrumentation …, 2023 - ieeexplore.ieee.org
Telemetry anomaly detection is a prominent health condition monitoring task that plays an
increasingly crucial role in identifying unexpected events and improving satellite's overall …
increasingly crucial role in identifying unexpected events and improving satellite's overall …
A component diagnostic and prognostic framework for pump bearings based on deep learning with data augmentation
A Rivas, GK Delipei, I Davis, S Bhongale, J Yang… - Reliability Engineering & …, 2024 - Elsevier
To support the mission of providing safe electricity generation with a high capacity factor, a
Predictive Maintenance (PdM) framework using Machine Learning Models (MLM) to …
Predictive Maintenance (PdM) framework using Machine Learning Models (MLM) to …
[HTML][HTML] Early outlier detection in three-phase induction heating systems using clustering algorithms
Induction heating (IH) devices transfer the electric power to the contactless cookware via the
electromagnetic field. Therefore, the temperature of cookware is measured remotely, and the …
electromagnetic field. Therefore, the temperature of cookware is measured remotely, and the …
LSTM-based stacked autoencoders for early anomaly detection in induction heating systems
Due to the contactless operation of cookware on induction heating systems, the temperature
of the cookware is measured remotely using thermal sensors placed on the center of the …
of the cookware is measured remotely using thermal sensors placed on the center of the …
Abnormal operating condition identification of industrial processes based on deep learning with global-local slow feature analysis
Ensuring long-term safe and efficient operation of industrial processes relies on real-time
identification of abnormal operating conditions. However, industrial processes frequently …
identification of abnormal operating conditions. However, industrial processes frequently …