A survey on data-driven predictive maintenance for the railway industry
In the last few years, many works have addressed Predictive Maintenance (PdM) by the use
of Machine Learning (ML) and Deep Learning (DL) solutions, especially the latter. The …
of Machine Learning (ML) and Deep Learning (DL) solutions, especially the latter. The …
[HTML][HTML] RADIS: A real-time anomaly detection intelligent system for fault diagnosis of marine machinery
C Velasco-Gallego, I Lazakis - Expert Systems with Applications, 2022 - Elsevier
By enhancing data accessibility, the implementation of data-driven models has been made
possible to empower strategies in relation to O&M activities. Such models have been …
possible to empower strategies in relation to O&M activities. Such models have been …
Explainable anomaly detection framework for maritime main engine sensor data
In this study, we proposed a data-driven approach to the condition monitoring of the marine
engine. Although several unsupervised methods in the maritime industry have existed, the …
engine. Although several unsupervised methods in the maritime industry have existed, the …
A comparative investigation of data-driven approaches based on one-class classifiers for condition monitoring of marine machinery system
Y Tan, H Tian, R Jiang, Y Lin, J Zhang - Ocean Engineering, 2020 - Elsevier
The safety and reliability of ship navigation depend heavily on the performance of the
marine machinery system, which can be maintained at a high level by condition based …
marine machinery system, which can be maintained at a high level by condition based …
[HTML][HTML] Predictive anomaly detection for marine diesel engine based on echo state network and autoencoder
C Qu, Z Zhou, Z Liu, S Jia - Energy Reports, 2022 - Elsevier
Marine diesel engine with high thermal efficiency and good economy has become the main
power of ships. Anomaly detection is an important method to improve the operation reliability …
power of ships. Anomaly detection is an important method to improve the operation reliability …
Anomaly process detection using negative selection algorithm and classification techniques
S Hosseini, H Seilani - Evolving Systems, 2021 - Springer
Artificial immune system is derived from the biological immune system. This system is an
important method for generating detectors that include self-adaption, self-regulation and self …
important method for generating detectors that include self-adaption, self-regulation and self …
Sustainable ensemble learning driving intrusion detection model
Nowadays, in machine learning based intrusion detection systems, ensemble learning is a
commonly adopted method to improve the detection accuracy. Unfortunately, the existing …
commonly adopted method to improve the detection accuracy. Unfortunately, the existing …
Time-series anomaly detection using dynamic programming based longest common subsequence on sensor data
This study proposes a novel approach to time-series anomaly detection by solving the
longest common subsequence (LCS) problem of two time-series data. The conventional …
longest common subsequence (LCS) problem of two time-series data. The conventional …
[HTML][HTML] Integrated machine learning and probabilistic degradation approach for vessel electric motor prognostics
In the transition towards more sustainable ships, electric motors (EM) are being used in ship
propulsion systems to reduce emissions and increase efficiency. The safe operation of ships …
propulsion systems to reduce emissions and increase efficiency. The safe operation of ships …
An integrated LSTM-AM and SPRT method for fault early detection of forced-oxidation system in wet flue gas desulfurization
Safe and efficient operation of the forced-oxidation system is of importance to the wet flue
gas desulfurization (WFGD). However, equipment and system failures are commonly found …
gas desulfurization (WFGD). However, equipment and system failures are commonly found …