Real-time safety monitoring in the induction motor using deep hierarchic long short-term memory

Kerboua, Adlen; Metatla, Abderrezak; Kelaiaia, Ridha; Batouche, Mohamed

doi:10.1007/s00170-018-2607-4

Cited by 11 publications

(3 citation statements)

References 16 publications

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“…So, we choose to design an LSTM [20] based RNN. An LSTM unit can learn dependencies in long time series data, to a deeper version of LSTM obtained by stacking two or more LSTM layers [21] to effectively deal with more longterm temporal dependencies inside the data. To prevent overfitting, we insert dropout layers after each of the LSTM layers.…”

Section: Define the Rnns Architecturesmentioning

confidence: 99%

Recurrent neural network optimization for wind turbine condition prognosis

Kerboua¹,

Kelaiaia²

2022

Diagnostyka

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This research focuses on employing Recurrent Neural Networks (RNN) to prognosis a wind turbine operation's health from collected vibration time series data, by using several memory cell variations, including Long Short Time Memory (LSTM), Bilateral LSTM (BiLSTM), and Gated Recurrent Unit (GRU), which are integrated into various architectures. We tune the training hyperparameters as well as the adapted depth and recurrent cell number of the proposed networks to obtain the most accurate predictions. Tuning those parameters is a hard task and depends widely on the experience of the designer. This can be resolved by integrating the training process in a Bayesian optimization loop where the loss is considered as the objective function to minimize. The obtained results show the effectiveness of the proposed method, which generates more accurate recurrent models with a more accurate prognosis of the operating state of the wind turbine than those generated using trivial training parameters.

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Section: Define the Rnns Architecturesmentioning

confidence: 99%

Recurrent neural network optimization for wind turbine condition prognosis

Kerboua¹,

Kelaiaia²

2022

Diagnostyka

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“…The experimental results show that it has good robustness and real-time. The experimental results show that under different speeds and loads, the method can accurately detect fault types, which is feasible and effective [ 19 ]. Zhuang input four features with high classification rate into the RNN network.…”

Section: Introductionmentioning

confidence: 99%

Research on Voltage Waveform Fault Detection of Miniature Vibration Motor Based on Improved WP-LSTM

et al. 2020

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To solve the problem of vibration motor fault detection accuracy and inefficiency in smartphone components, this paper proposes a fault diagnosis method based on the wavelet packet and improves long and short-term memory network. First, the voltage signal of the vibration motor is decomposed by a wavelet packet to reconstruct the signal. Secondly, the reconstructed signal is input into the improved three-layer LSTM network as a feature vector. The memory characteristics of the LSTM network are used to fully learn the time-series fault feature information in the unsteady state signal, and then, the model is used to diagnose the motor fault. Finally, the feasibility of the proposed method is verified through experiments and can be applied to engineering practice. Compared with the existing motor fault diagnosis method, the improved WP-LSTM diagnosis method has a better diagnosis effect and improves fault diagnosis.

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“…In other words, the LSTM NN is very suitable for dealing with the serially correlated or autocorrelated data. It has been successfully applied to various sequence recognition and sequence prediction tasks, such as handwriting recognition [25], [26], large-scale acoustic modeling and vocabulary speech recognition [27]- [29], document analysis and recognition [30], [31], image recognition [32], [33], machine translation [34], traffic speed prediction [35], realtime safety monitoring in the induction motor [36], and so on. To the best of our knowledge, there is no application of LSTM NN in the domain of the variation pattern recognition for the BIW OCMM data.…”

Section: Introductionmentioning

confidence: 99%

Variation Pattern Recognition of the BIW OCMM Online Measurement Data Based on LSTM NN

et al. 2019

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An accurate recognition of a dimensional variation pattern is very important for producing highquality body-in-white (BIW). The wide application of optical coordination measurement machines (OCMM) in vehicle factory provided massive online dimensional data for the variation pattern recognition. However, the massive serially correlated or autocorrelated and 100% measurement data generated from the OCMM challenge the traditional statistical process control (SPC) technology and the common variation recognition approaches. This paper presents a novel deep-learning method, long short-term memory neural network (LSTM NN), to recognize the variation pattern of the BIW OCMM online measurement data. A comparative study between the backpropagation neural network (BP NN) and the LSTM NN was implemented, and the practicability of the proposed intelligent method was demonstrated by a case study. With the efficient use of time series information, the LSTM NN has a good performance in variation patterns' recognition and high practicability in improving the quality of the BIW.INDEX TERMS Variation pattern recognition, long short-term memory neural network (LSTM NN), bodyin-white (BIW), online measurement data, deep learning.

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Real-time safety monitoring in the induction motor using deep hierarchic long short-term memory

Cited by 11 publications

References 16 publications

Recurrent neural network optimization for wind turbine condition prognosis

Recurrent neural network optimization for wind turbine condition prognosis

Research on Voltage Waveform Fault Detection of Miniature Vibration Motor Based on Improved WP-LSTM

Variation Pattern Recognition of the BIW OCMM Online Measurement Data Based on LSTM NN

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