Reliable fault diagnosis for low-speed bearings using individually trained support vector machines with kernel discriminative feature analysis
…, J Kim, JM Kim, ACC Tan, EY Kim… - IEEE Transactions on …, 2014 - ieeexplore.ieee.org
This paper proposes a highly reliable fault diagnosis approach for low-speed bearings. The
proposed approach first extracts wavelet-based fault features that represent diverse …
proposed approach first extracts wavelet-based fault features that represent diverse …
A comprehensive survey on deep-learning-based breast cancer diagnosis
Simple Summary Breast cancer was diagnosed in 2.3 million women, and around 685,000
deaths from breast cancer were recorded globally in 2020, making it the most common cancer…
deaths from breast cancer were recorded globally in 2020, making it the most common cancer…
[HTML][HTML] Electricity theft detection in smart grid systems: A CNN-LSTM based approach
Among an electricity provider’s non-technical losses, electricity theft has the most severe
and dangerous effects. Fraudulent electricity consumption decreases the supply quality, …
and dangerous effects. Fraudulent electricity consumption decreases the supply quality, …
A reliable technique for remaining useful life estimation of rolling element bearings using dynamic regression models
Induction motors most often fail due to faults in the rolling element bearings. Such failures
can cause long and unscheduled downtime in a production facility, which can result in huge …
can cause long and unscheduled downtime in a production facility, which can result in huge …
Bearing fault diagnosis of induction motors using a genetic algorithm and machine learning classifiers
RN Toma, AE Prosvirin, JM Kim - Sensors, 2020 - mdpi.com
Efficient fault diagnosis of electrical and mechanical anomalies in induction motors (IMs) is
challenging but necessary to ensure safety and economical operation in industries. Research …
challenging but necessary to ensure safety and economical operation in industries. Research …
A hybrid prognostics technique for rolling element bearings using adaptive predictive models
Rolling element bearings cause the largest number of failures in induction motors. Predicting
an impending failure and estimating the remaining useful life (RUL) of a bearing is …
an impending failure and estimating the remaining useful life (RUL) of a bearing is …
A hybrid feature model and deep-learning-based bearing fault diagnosis
Bearing fault diagnosis is imperative for the maintenance, reliability, and durability of rotary
machines. It can reduce economical losses by eliminating unexpected downtime in industry …
machines. It can reduce economical losses by eliminating unexpected downtime in industry …
Automated bearing fault diagnosis scheme using 2D representation of wavelet packet transform and deep convolutional neural network
MMM Islam, JM Kim - Computers in Industry, 2019 - Elsevier
Bearings are one of the most crucial components in many industrial machines. Effective
bearing fault diagnosis is essential for normal and safe machine operation. Existing fault …
bearing fault diagnosis is essential for normal and safe machine operation. Existing fault …
Acoustic spectral imaging and transfer learning for reliable bearing fault diagnosis under variable speed conditions
Incipient fault diagnosis of a bearing requires robust feature representation for an accurate
condition-based monitoring system. Existing fault diagnosis schemes are mostly confined to …
condition-based monitoring system. Existing fault diagnosis schemes are mostly confined to …
Pipeline leak diagnosis based on leak-augmented scalograms and deep learning
MF Siddique, Z Ahmad, JM Kim - Engineering Applications of …, 2023 - Taylor & Francis
This paper proposes a new framework for leak diagnosis in pipelines using leak-augmented
scalograms and deep learning. Acoustic emission (AE) scalogram images obtained from …
scalograms and deep learning. Acoustic emission (AE) scalogram images obtained from …