A review of artificial intelligence methods for condition monitoring and fault diagnosis of rolling element bearings for induction motor
The fault detection and diagnosis (FDD) along with condition monitoring (CM) and of rotating
machinery (RM) have critical importance for early diagnosis to prevent severe damage of …
machinery (RM) have critical importance for early diagnosis to prevent severe damage of …
Damage detection techniques for wind turbine blades: A review
Blades play a vital role in wind turbine system performances. However, they are susceptible
to damage arising from complex and irregular loading or even cause catastrophic collapse …
to damage arising from complex and irregular loading or even cause catastrophic collapse …
Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning
Digital twin (DT) is emerging as a key technology for smart manufacturing. The high fidelity
DT model of the physical assets can produce system performance data that is close to …
DT model of the physical assets can produce system performance data that is close to …
Trends in audio signal feature extraction methods
G Sharma, K Umapathy, S Krishnan - Applied Acoustics, 2020 - Elsevier
Audio signal processing algorithms generally involves analysis of signal, extracting its
properties, predicting its behaviour, recognizing if any pattern is present in the signal, and …
properties, predicting its behaviour, recognizing if any pattern is present in the signal, and …
A physics-informed deep learning approach for bearing fault detection
In recent years, advances in computer technology and the emergence of big data have
enabled deep learning to achieve impressive successes in bearing condition monitoring …
enabled deep learning to achieve impressive successes in bearing condition monitoring …
A hybrid deep-learning model for fault diagnosis of rolling bearings
Detection accuracy of bearing faults is crucial in saving economic loss for industrial
applications. Deep learning is capable of producing high accuracy for bearing fault …
applications. Deep learning is capable of producing high accuracy for bearing fault …
Fault diagnosis of angle grinders and electric impact drills using acoustic signals
A Glowacz, R Tadeusiewicz, S Legutko… - Applied Acoustics, 2021 - Elsevier
Electric motors use about 68% of total generated electricity. Fault diagnosis of electrical
motors is an important task, because it allows saving a large amount of money and time. An …
motors is an important task, because it allows saving a large amount of money and time. An …
Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study
O AlShorman, F Alkahatni, M Masadeh… - Advances in …, 2021 - journals.sagepub.com
Nowadays, condition-based maintenance (CBM) and fault diagnosis (FD) of rotating
machinery (RM) has a vital role in the modern industrial world. However, the remaining …
machinery (RM) has a vital role in the modern industrial world. However, the remaining …
Review on machine learning algorithm based fault detection in induction motors
Fault detection prior to their occurrence or complete shut-down in induction motor is
essential for the industries. The fault detection based on condition monitoring techniques …
essential for the industries. The fault detection based on condition monitoring techniques …
A novel hybrid model based on VMD-WT and PCA-BP-RBF neural network for short-term wind speed forecasting
Y Zhang, B Chen, G Pan, Y Zhao - Energy Conversion and Management, 2019 - Elsevier
Accurate short-term wind power forecasting is significant for rational dispatching of the
power grid and ensuring the power supply quality. In order to enhance the accuracy of short …
power grid and ensuring the power supply quality. In order to enhance the accuracy of short …