Machine Learning from Heterogeneous Condition Monitoring Sensor Data for Predictive Maintenance and Smart Industry

in Special Issue   Posted on December 31, 2020 

Information for the Special Issue

Submission Deadline: Fri 30 Apr 2021
Journal Impact Factor : 3.275
Journal Name : Sensors
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Special Issue Call for Papers:

Dear colleagues,

Smart Industry relies on the advanced use of sensor technology as well as the use of data mining techniques based on machine learning algorithms. In fact, machine learning and deep learning applications have been thriving over the last decade in many different domains, including computer vision and natural language understanding. The drivers for this vibrant development have been the availability of abundant data, breakthroughs of algorithms, and advancements in hardware. Recently, complex industrial assets have been extensively monitored by intelligent sensors and large amounts of heterogeneous condition monitoring signals have been collected. However, the application of machine learning approaches in the intelligent maintenance and operation of complex industrial assets so far has been limited. This Special Issue aims at shedding light into the current developments, drivers, challenges, potential solutions, and future research needs in the fields of the use and analysis of heterogeneous condition monitoring sensor data in smart industries, as well as industrial artificial intelligence applied to the intelligent maintenance and operation of complex industrial assets.

Authors of selected high-qualified papers from the 21st International Conference on Intelligent Data Engineering and Automated Learning (IDEAL) welcome to submit extended versions of their original papers (50% extensions of the contents of the conference paper) and contributions.

The topics of the Special Issue include but are not limited to the following:

Sensor technology in smart industry applications;
Analysis of heterogeneous condition monitoring sensor data;
Fault Detection and Diagnosis (FDD);
Estimation of remaining useful life of components and machines;
Early failure and anomaly detection and analysis;
Predictive and prescriptive maintenance;
Hybrid approaches combining physics-based with data-driven approaches;
Self-healing and self-correction;
Self-adaptive time-series-based models for prognostics and forecasting;
Concept drift issues in dynamic predictive maintenance systems;
Active learning and Design of Experiment (DoE) in dynamic predictive maintenance;
Industrial process monitoring and modelling;
Activity recognition in the industrial setting;
Event logs abstraction methods and anomaly detection;
Conformance checking of industrial process models;
Network analysis on event log data;
Supervised and unsupervised methods of log analysis;
Machine learning and deep learning methods in smart industries;
Explainable AI for predictive maintenance.

Prof. Dr. Grzegorz J. Nalepa
Dr. João Gama
Dr. Olga Fink
Dr. Albert Bifet
Prof. Dr. David Camacho
Guest Editors

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