The design work-flow of machine learning techniques for continuous monitoring or predictive maintenance in an industrial context is usually a two step procedure: the selection of features to be computed from the observed signals and training of a suitable algorithm with real-life meaningful data, that will be next deployed in the second step. Feature selection is a relevant task since it provides a powerful optimisation of the deployed algorithm performance, for the given training data-set. The paper provides a method for feature ranking and selection that embeds constraints coming from real-life applications, including sensing device specifications, environmental noise, available processing resources, being all these latter aspects not con...
While machine learning has made inroads into many industries, power systems have some unique applica...
Abstract – Condition Based Monitoring of a machine refers to the checking of various parameters and ...
Industrial applications put special demands on machine learning algorithms. Noisy data, outliers, an...
The design work-flow of machine learning techniques for continuous monitoring or predictive maintena...
In order to overcome the complexities encountered in sensing devices with data collection, transmiss...
A feature is a measured property of a monitored system. Feature extraction in condition monitoring r...
The aim of the chapter is to explain the basic concepts of Machine Learning applied to condition mon...
The technique of machinery condition monitoring has been greatly enhanced over recent years with the...
Automatic anomaly detection is a major issue in various areas. Beyond mere detection, the identifica...
In the context of Industry 4.0, an emerging trend is to increase the reliability of industrial proce...
Feature selection and taking into account dynamic environments are two important aspects of modern d...
This chapter details the application of a machine learning condition monitoring tool to an industria...
Data acquisition, storage and processing becomes increasingly affordable and the use of machine lear...
The occurrence of anomalies and unexpected, process-related faults is a major problem for manufactur...
In times of rising energy costs and increasing customer awareness of sustainable production methods,...
While machine learning has made inroads into many industries, power systems have some unique applica...
Abstract – Condition Based Monitoring of a machine refers to the checking of various parameters and ...
Industrial applications put special demands on machine learning algorithms. Noisy data, outliers, an...
The design work-flow of machine learning techniques for continuous monitoring or predictive maintena...
In order to overcome the complexities encountered in sensing devices with data collection, transmiss...
A feature is a measured property of a monitored system. Feature extraction in condition monitoring r...
The aim of the chapter is to explain the basic concepts of Machine Learning applied to condition mon...
The technique of machinery condition monitoring has been greatly enhanced over recent years with the...
Automatic anomaly detection is a major issue in various areas. Beyond mere detection, the identifica...
In the context of Industry 4.0, an emerging trend is to increase the reliability of industrial proce...
Feature selection and taking into account dynamic environments are two important aspects of modern d...
This chapter details the application of a machine learning condition monitoring tool to an industria...
Data acquisition, storage and processing becomes increasingly affordable and the use of machine lear...
The occurrence of anomalies and unexpected, process-related faults is a major problem for manufactur...
In times of rising energy costs and increasing customer awareness of sustainable production methods,...
While machine learning has made inroads into many industries, power systems have some unique applica...
Abstract – Condition Based Monitoring of a machine refers to the checking of various parameters and ...
Industrial applications put special demands on machine learning algorithms. Noisy data, outliers, an...