A semi-supervised leakage detection method driven by multivariate time series for natural gas gathering pipeline
Real-time and accurate leakage detection of natural gas gathering pipelines is critical to the
safe and reliable operation of the gas and oil industry. Modern data-driven fault detection …
safe and reliable operation of the gas and oil industry. Modern data-driven fault detection …
Efficient adaptive deep gradient RBF network for multi-output nonlinear and nonstationary industrial processes
Due to the complexity of process operation, industrial process data are often nonlinear and
nonstationary, high dimensional, and multivariate with complex interactions between …
nonstationary, high dimensional, and multivariate with complex interactions between …
An RBF online learning scheme for non-stationary environments based on fuzzy means and givens rotations
Learning on non-stationary environments is laden with many challenges, as the procedure
is usually characterized by drifts and data unavailability; on the other hand, it is of great …
is usually characterized by drifts and data unavailability; on the other hand, it is of great …
Adaptive multioutput gradient rbf tracker for nonlinear and nonstationary regression
Multioutput regression of nonlinear and nonstationary data is largely understudied in both
machine learning and control communities. This article develops an adaptive multioutput …
machine learning and control communities. This article develops an adaptive multioutput …
Selective ensemble of classifiers trained on selective samples
Classifier ensembles are characterized by the high quality of classification, thanks to their
generalizing ability. Most existing ensemble algorithms use all learning samples to learn the …
generalizing ability. Most existing ensemble algorithms use all learning samples to learn the …
Lifelong learning meets dynamic processes: an emerging streaming process prediction framework with delayed process output measurement
As an emerging machine learning technique, lifelong learning is capable of solving multiple
consecutive tasks based on previously accumulated knowledge. Although this is highly …
consecutive tasks based on previously accumulated knowledge. Although this is highly …
Leak detection for natural gas gathering pipeline using spatio-temporal fusion of practical operation data
J Liang, S Liang, L Ma, H Zhang, J Dai… - Engineering Applications of …, 2024 - Elsevier
Gathering pipelines are one of the key upstream infrastructures in the gas industry that link
production well to the processing plant. Leak detection is critical for ensuring the safety of …
production well to the processing plant. Leak detection is critical for ensuring the safety of …
Soft Sensor Modeling Based on Vector-Quantized Weighted-Wasserstein VAE for Polyester Polymerization Process
X He, T Liu, Y Zhang, R **e - IEEE Transactions on Industrial …, 2024 - ieeexplore.ieee.org
The uneven distribution of process industrial data poses a significant challenge for soft
sensor modeling. Hence, it is necessary to employ generative models to generate some new …
sensor modeling. Hence, it is necessary to employ generative models to generate some new …
Adaptive real-time exploration and optimization of safety-critical industrial systems with ensemble learning
Real-time optimization plays a key role in improving energy efficiency and the operational
effectiveness of industrial systems. To deal with unknown process characteristics and safety …
effectiveness of industrial systems. To deal with unknown process characteristics and safety …
Diversified kernel latent variable space and multi-objective optimization for selective ensemble learning-based soft sensor
L Peng, L Gu, L He, Y Shi - Applied Sciences, 2023 - mdpi.com
The improvement of data-driven soft sensor modeling methods and techniques for the
industrial process has strongly promoted the development of the intelligent process industry …
industrial process has strongly promoted the development of the intelligent process industry …