Combination bidirectional long short-term memory and capsule network for rotating machinery fault diagnosis

Han, Tian; Ma, Ruiyi; Zheng, Jian

doi:10.1016/j.measurement.2021.109208

Cited by 55 publications

(20 citation statements)

References 23 publications

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“…Due to the advantages of small samples, strong anti-noise ability, and simple network structure, the long short-term memory network has been widely used in the field of defect detection. 56 , 57 , 58 To improve the accuracy of the cantilever defect identification model, the electric signals of the CSF-TENG were first decomposed by using the wavelet packet algorithm. As depicted in Figure 4 B, the decomposed signals were used as training sets and test sets to establish a defect classification model based on the long short-term memory algorithm.…”

Section: Resultsmentioning

confidence: 99%

A cantilever-structure triboelectric nanogenerator for energy harvesting and defect detection via long short-term memory network

Xin¹,

Xu²,

Gong³

et al. 2022

iScience

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Section: Resultsmentioning

confidence: 99%

A cantilever-structure triboelectric nanogenerator for energy harvesting and defect detection via long short-term memory network

Xin¹,

Xu²,

Gong³

et al. 2022

iScience

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“…Both layers are connected to the same output layer. Thus, BiLSTM network captures the total information about preceding and future sequence of data points [26], [27], [28], [29].…”

Section: ) Fcnnmentioning

confidence: 99%

Performance Comparison of Multiple Neural Networks for Fault Detection of Sensors Array in Oil Heating Reactor

Mustafa¹,

Gharghory²,

Kamal³

2022

IJACSA

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Fault detection is an important issue for early failure revelation and machine components preserving before the damage. The processes of fault detection, diagnosis and correction especially in oil heating reactor sensors are among the most crucial steps for reliable and proper operation inside the reactor.The fault detection in sensors array of heating reactor is considered as an important tool to guarantee that the controller can take the best possible action to insure the quality of the output. In this paper, fault detection for the temperature sensor in oil heating reactor using different types of faults with different levels is addressed. Multiple approaches based on Neural Network (NN)s such as the classical Fully Connected Neural Network (FCNN), Bidirectional Long Short Term Memory network (BiLSTM) based on Recurrent Neural Network (R.N.N.) and Convolutional Neural Network (CNN) are suggested for this purpose. The suggested networks are trained and tested on real dataset sequences taken from sensors array readings of real heating reactor in Egypt. The performance comparison of the suggested networks is evaluated using different metrics such as "confusion matrix", accuracy, precision, etc. The various NN are simulated, trained and tested in this paper using MATLAB software 2021 and the advanced tool of "DeepNetworkDesigner". The simulation results prove that CNN outperforms the other comparative networks with classification accuracy reached to 100% with different levels and different types of faults.

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“…In the RNNs family both LSTM and biLSTM have been widely utilized [117]- [120]. For instance, Xiang et al [121] employed LSTM with an attention-based mechanism for fault detection in wind turbines.…”

Section: Neural Network (Nns)mentioning

confidence: 99%

Artificial Intelligence Based Prognostic Maintenance of Renewable Energy Systems: A Review of Techniques, Challenges, and Future Research Directions

Afridi¹,

Ahmad²,

Hassan³

2021

Preprint

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Combination bidirectional long short-term memory and capsule network for rotating machinery fault diagnosis

Cited by 55 publications

References 23 publications

A cantilever-structure triboelectric nanogenerator for energy harvesting and defect detection via long short-term memory network

A cantilever-structure triboelectric nanogenerator for energy harvesting and defect detection via long short-term memory network

Performance Comparison of Multiple Neural Networks for Fault Detection of Sensors Array in Oil Heating Reactor

Artificial Intelligence Based Prognostic Maintenance of Renewable Energy Systems: A Review of Techniques, Challenges, and Future Research Directions

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