Construction of health indicators for condition monitoring of rotating machinery: A review of the research
The condition monitoring (CM) of rotating machinery (RM) is an essential operation for
improving the reliability of mechanical systems. For this purpose, an efficient CM method that …
improving the reliability of mechanical systems. For this purpose, an efficient CM method that …
Deep branch attention network and extreme multi-scale entropy based single vibration signal-driven variable speed fault diagnosis scheme for rolling bearing
D Zhao, S Liu, H Du, L Wang, Z Miao - Advanced Engineering Informatics, 2023 - Elsevier
In view of the difficulty in measuring the speed signal and integrating the vibration and
speed information flexibly in actual variable speed bearing fault diagnosis, a single vibration …
speed information flexibly in actual variable speed bearing fault diagnosis, a single vibration …
Self-attention ConvLSTM and its application in RUL prediction of rolling bearings
Traditional long short-term memory (LSTM) neural networks generally face the challenge of
low training efficiency and poor prediction accuracy for the remaining useful life (RUL) …
low training efficiency and poor prediction accuracy for the remaining useful life (RUL) …
Convformer-NSE: A novel end-to-end gearbox fault diagnosis framework under heavy noise using joint global and local information
The application of convolutional neural network (CNN) has greatly promoted the scope and
scenario of intelligent fault diagnosis and brought about a significant improvement of …
scenario of intelligent fault diagnosis and brought about a significant improvement of …
Highly imbalanced fault diagnosis of mechanical systems based on wavelet packet distortion and convolutional neural networks
M Zhao, X Fu, Y Zhang, L Meng, B Tang - Advanced Engineering …, 2022 - Elsevier
The healthy operations of mechanical systems are crucially important for ensuring human
safety and economic benefits, so that there is a high demand on the automatic fault …
safety and economic benefits, so that there is a high demand on the automatic fault …
Entropy measures in machine fault diagnosis: Insights and applications
Entropy, as a complexity measure, has been widely applied for time series analysis. One
preeminent example is the design of machine condition monitoring and industrial fault …
preeminent example is the design of machine condition monitoring and industrial fault …
Automated and adaptive ridge extraction for rotating machinery fault detection
A ridge in a time-frequency graph (TFG) describes the relationship of a signal component's
instantaneous frequencies over time. Accurate ridge extraction from TFGs is beneficial for …
instantaneous frequencies over time. Accurate ridge extraction from TFGs is beneficial for …
An optimized adaptive PReLU-DBN for rolling element bearing fault diagnosis
Rolling element bearings are critical components in industrial rotating machines. Faults and
failures of bearings can cause degradation of machine performance or even a catastrophe …
failures of bearings can cause degradation of machine performance or even a catastrophe …
Few-shot learning under domain shift: Attentional contrastive calibrated transformer of time series for fault diagnosis under sharp speed variation
The domain shift of sample distribution caused by sharp speed variation dissatisfies the
general assumption of stationary conditions, which renders a severe challenge for a majority …
general assumption of stationary conditions, which renders a severe challenge for a majority …
Feature-level SMOTE: Augmenting fault samples in learnable feature space for imbalanced fault diagnosis of gas turbines
A challenge in gas turbine fault diagnosis is that labeled fault samples are relatively rare and
much fewer than normal samples. Conventional data augmentation techniques generate …
much fewer than normal samples. Conventional data augmentation techniques generate …