The WWTP fault diagnosis is based on a deep convolutional neural network (DCNN) with linguistic Ordered Weighted Average (OWA) pooling.
This approach, based on a CNN pooling layer using linguistic OWA quantifiers, permits an easier and more intuitive performance of these operations, achieving.
The WWTP fault diagnosis is based on a deep convolutional neural network (DCNN) with linguistic Ordered Weighted Average (OWA) pooling, which shows a better ...
The multivariate statistical approaches for fault detection based on data have been very useful. However, they are known to be less powerful for fault diagnosis ...
In process industries, early detection and diagnosis of faults is crucial for timely identification of process upsets, equipment and/or sensor malfunctions.
The WWTP fault diagnosis is based on a deep convolutional neural network (DCNN) with linguistic Ordered Weighted Average (OWA) pooling, which shows a better ...
Fault detection and diagnosis (FDD) can help operators timely detect and diagnose abnormal situations, and take right actions to avoid adverse consequences.
ETFA24-000056 Faster WWTP fault diagnosis based on linguistic-OWA pooling DCNN ... ETFA24-000166 Evaluation Metrics for Collaborative Fault Detection and ...
This paper investigates the fault detection problem in wastewater treatment process based on an improved kernel extreme learning machine method.
Missing: Linguistic- | Show results with:Linguistic-
Faster WWTP Fault Diagnosis Based on Linguistic-OWA Pooling DCNN. Authors: Beneyto-Rodriguez, Alicia, Sainz-Palmero, Gregorio I., Galende-Hernandez, Marta ...