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Signal based condition monitoring techniques for fault detection and diagnosis of induction motors: A state-of-the-art review
Uninterrupted and trouble-free operation of induction motors (IMs) is the compulsion of the
modern industries. Firstly, the paper reviews the conventional time and spectrum signal …
modern industries. Firstly, the paper reviews the conventional time and spectrum signal …
Deep learning algorithms for bearing fault diagnostics—A comprehensive review
In this survey paper, we systematically summarize existing literature on bearing fault
diagnostics with deep learning (DL) algorithms. While conventional machine learning (ML) …
diagnostics with deep learning (DL) algorithms. While conventional machine learning (ML) …
Bearing fault diagnosis based on vibro-acoustic data fusion and 1D-CNN network
X Wang, D Mao, X Li - Measurement, 2021 - Elsevier
Bearing fault diagnosis is an important part of rotating machinery maintenance. Existing
diagnosis methods based on single-modal signals not only have unsatisfactory accuracy …
diagnosis methods based on single-modal signals not only have unsatisfactory accuracy …
A generic intelligent bearing fault diagnosis system using compact adaptive 1D CNN classifier
Timely and accurate bearing fault detection and diagnosis is important for reliable and safe
operation of industrial systems. In this study, performance of a generic real-time induction …
operation of industrial systems. In this study, performance of a generic real-time induction …
A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load
In recent years, intelligent fault diagnosis algorithms using machine learning technique have
achieved much success. However, due to the fact that in real world industrial applications …
achieved much success. However, due to the fact that in real world industrial applications …
Deep learning and its applications to machine health monitoring
Abstract Since 2006, deep learning (DL) has become a rapidly growing research direction,
redefining state-of-the-art performances in a wide range of areas such as object recognition …
redefining state-of-the-art performances in a wide range of areas such as object recognition …
A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals
Intelligent fault diagnosis techniques have replaced time-consuming and unreliable human
analysis, increasing the efficiency of fault diagnosis. Deep learning models can improve the …
analysis, increasing the efficiency of fault diagnosis. Deep learning models can improve the …
Real-time motor fault detection by 1-D convolutional neural networks
Early detection of the motor faults is essential and artificial neural networks are widely used
for this purpose. The typical systems usually encapsulate two distinct blocks: feature …
for this purpose. The typical systems usually encapsulate two distinct blocks: feature …
Rolling element fault diagnosis based on VMD and sensitivity MCKD
H Cui, Y Guan, H Chen - IEEE Access, 2021 - ieeexplore.ieee.org
In order to improve the diagnosis accuracy and solve the weak fault signal of rolling element
of rolling bearings due to long transmission path, a novel fault diagnosis method based on …
of rolling bearings due to long transmission path, a novel fault diagnosis method based on …
A review of artificial intelligence methods for engineering prognostics and health management with implementation guidelines
The past decade has witnessed the adoption of artificial intelligence (AI) in various
applications. It is of no exception in the area of prognostics and health management (PHM) …
applications. It is of no exception in the area of prognostics and health management (PHM) …