Challenges and opportunities of deep learning models for machinery fault detection and diagnosis: A review
In the age of industry 4.0, deep learning has attracted increasing interest for various
research applications. In recent years, deep learning models have been extensively …
research applications. In recent years, deep learning models have been extensively …
A deep and scalable unsupervised machine learning system for cyber-attack detection in large-scale smart grids
Smart grid technology increases reliability, security, and efficiency of the electrical grids.
However, its strong dependencies on digital communication technology bring up new …
However, its strong dependencies on digital communication technology bring up new …
Multi-Sensor data fusion in intelligent fault diagnosis of rotating machines: A comprehensive review
Rotating machines are extensively utilized in diverse industries, and their malfunctions can
result in significant financial consequences and safety risks. Consequently, there has been …
result in significant financial consequences and safety risks. Consequently, there has been …
An adaptive spatiotemporal feature learning approach for fault diagnosis in complex systems
The machine fault diagnosis is being considered in a larger-scale complex system with
numerous measurements from diverse subsystems or components, where the collected data …
numerous measurements from diverse subsystems or components, where the collected data …
Anomaly detection and fault disambiguation in large flight data: A multi-modal deep auto-encoder approach
Flight data recorders provide large volumes of heterogeneous data from arrays of sensors
on-board to perform fault diagnosis. Challenges such as large data volumes, lack of labeled …
on-board to perform fault diagnosis. Challenges such as large data volumes, lack of labeled …
An unsupervised spatiotemporal graphical modeling approach for wind turbine condition monitoring
W Yang, C Liu, D Jiang - Renewable energy, 2018 - Elsevier
The vast installment of wind turbines and the development of condition monitoring system
provides large amounts of operational data for condition monitoring and health …
provides large amounts of operational data for condition monitoring and health …
Intelligent anomaly detection for large-scale smart grids
H Karimipour, S Geris… - 2019 IEEE Canadian …, 2019 - ieeexplore.ieee.org
This paper proposes an unsupervised anomaly detection scheme based on statistical
correlation between measurements. The goal is to design a scalable anomaly detection …
correlation between measurements. The goal is to design a scalable anomaly detection …
An improved extended Kalman filter with inequality constraints for gas turbine engine health monitoring
F Lu, H Ju, J Huang - Aerospace Science and Technology, 2016 - Elsevier
Various Kalman filter approaches have been proposed for the state estimation of gas turbine
engines, among which Linear Kalman Filter (LKF) is the most common one. Kalman filters …
engines, among which Linear Kalman Filter (LKF) is the most common one. Kalman filters …
Data-driven root-cause fault diagnosis for multivariate non-linear processes
B Rashidi, DS Singh, Q Zhao - Control Engineering Practice, 2018 - Elsevier
In a majority of multivariate processes, propagating nature of malfunctions makes the fault
diagnosis a challenging task. This paper presents a novel data-driven strategy for real-time …
diagnosis a challenging task. This paper presents a novel data-driven strategy for real-time …
Refined composite multivariate multiscale symbolic dynamic entropy and its application to fault diagnosis of rotating machine
Y Yang, H Zheng, J Yin, M Xu, Y Chen - Measurement, 2020 - Elsevier
Accurate and efficient identification of various fault categories, especially for the big data and
multisensory system, is a challenge in rotating machinery fault diagnosis. For the diagnosis …
multisensory system, is a challenge in rotating machinery fault diagnosis. For the diagnosis …