Using LSTM recurrent neural networks to predict excess vibration events in aircraft engines

ElSaid, AbdElRahman; Wild, Brandon; Higgins, James; Desell, Travis

doi:10.1109/escience.2016.7870907

Cited by 49 publications

(26 citation statements)

References 4 publications

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“…Fault signals generated by rolling bearings are inherently periodic feature [26]. LSTM network, one of the sequence-to-sequence models, has been successfully applied to predict vibration sequences [23]. Based on this, a novel self-adaptive waveform point extended method would be developed in this section and then incorporated into the LMD algorithm.…”

Section: Waveform Point Extension Methods and An Improvedmentioning

confidence: 99%

“…e elementary unit of the LSTM network is memory cell, which not only prevents the explosion or disappearance of gradients, but also controls the memory length of the sequences to avoid memory loss after a long time [23,25]. is network has ability of short-term sensitive memory because the unit cell possesses three gates: the forget, input, and output gates.…”

Section: Lstm Architecturementioning

confidence: 99%

“…e LSTM elementary unit is memory cell, which can avoid the aforementioned problem. Additionally, the LSTM network has been successfully used in circuit current sequence predication [22], predicted excess vibration events [23], and machine vibration sequence predication [24,25]. us, it has a great potential to complete signal extension by LSTM network.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Elimination of End effects in LMD Based on LSTM Network and Applications for Rolling Bearing Fault Feature Extraction

Liang

Wang

et al. 2020

Mathematical Problems in Engineering

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Local mean decomposition (LMD) is widely used in the area of multicomponents signal processing and fault diagnosis. One of the major problems is end effects, which distort the decomposed waveform at each end of the analyzed signal and influence feature frequency. In order to solve this problem, this paper proposes a novel self-adaptive waveform point extended method based on long short-term memory (LSTM) network. First, based on existing signal points, the LSTM network parameters of right and left ends are trained; then, these parameters are used to extend the waveform point at each end-side of signal; furthermore, the corresponding parameters are adaptively updated. The proposed method is compared with the characteristic segment extension and the traditional neural network extension methods through a simulated signal to verify the effectiveness. By combing the proposed method with LMD, an improved LMD algorithm is obtained. Finally, application of rolling bearing fault signal is carried out by the improved LMD algorithm, and the results show that the feature frequencies of the rolling bearing’s ball and inner and outer rings are successfully extracted.

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Section: Waveform Point Extension Methods and An Improvedmentioning

confidence: 99%

Section: Lstm Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Elimination of End effects in LMD Based on LSTM Network and Applications for Rolling Bearing Fault Feature Extraction

Liang

Wang

et al. 2020

Mathematical Problems in Engineering

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“…This study's ultimate goal is to explore the utilization of LSTM RNNs to predict future engine vibration in order to be used in a warning system to give indications for the problem before it occurs in order to avoid or mitigate it. An initial work examined building viable Recurrent Neural Networks (RNN) using Long Short Term Memory (LSTM) neurons to predict aircraft engine vibrations [17]. To achieve this, three different LSTM RNNs architectures were examined to find which would provide better results.…”

Section: Previous Workmentioning

confidence: 99%

Optimizing long short-term memory recurrent neural networks using ant colony optimization to predict turbine engine vibration

ElSaid

Jamiy

Higgins

et al. 2018

Applied Soft Computing

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127

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This article expands on research that has been done to develop a recurrent neural network (RNN) capable of predicting aircraft engine vibrations using long short-term memory (LSTM) neurons. LSTM RNNs can provide a more generalizable and robust method for prediction over analytical calculations of engine vibration, as analytical calculations must be solved iteratively based on specific empirical engine parameters, making this approach ungeneralizable across multiple engines. In initial work, multiple LSTM RNN architectures were proposed, evaluated and compared. This research improves the performance of the most effective LSTM network design proposed in the previous work by using a promising neuroevolution method based on ant colony optimization (ACO) to develop and enhance the LSTM cell structure of the network. A parallelized version of the ACO neuroevolution algorithm has been developed and the evolved LSTM RNNs were compared to the previously used fixed topology. The evolved networks were trained on a large database of flight data records obtained from an airline containing flights that suffered from excessive vibration. Results were obtained using MPI (Message Passing Interface) on a high performance computing (HPC) cluster, evolving 1000 different LSTM cell structures using 168 cores over 4 days. The new evolved LSTM cells showed an improvement of 1.35%, reducing prediction error from 5.51% to 4.17% when predicting excessive engine vibrations 10 seconds in the future, while at the same time dramatically reducing the number of weights from 21,170 to 11,810.

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“…Lipton et al [17] apply the LSTM network on the clinical time series data to solve the problem of phenotyping critical care patients. ElSaid et al [18] predict the excess vibration events in aircraft engines with LSTM recurrent neural networks.…”

Section: Figure3 a Simple Rnn Modelmentioning

confidence: 99%

LSTM-Based Temperature Prediction for Hot-Axles of Locomotives

et al. 2017

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Abstract:The reliability of locomotives plays a central role for the smooth operation of railway systems. Hot-axle failures are one of the most commonly found problems leading to locomotive accidents. Since the operating status of the locomotive axle bearings can be distinctly reflected by the axle temperatures, online temperature monitoring has become an essential way to detect hot-axle failures. In this work, we explore the feasibility of predict the hot-axle failures by identifying the temperature from predicted nominal values. We propose a data-driven approach based on the Long Short-Term Memory (LSTM) network to predict the sensor temperature for axle bearings. The effectiveness of the prediction model was validated with operation data collected from commercial locomotives. With a prediction accuracy is within a few percent, the proposed techniques can be used as a dynamic reference for hot-axle monitoring.

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Using LSTM recurrent neural networks to predict excess vibration events in aircraft engines

Cited by 49 publications

References 4 publications

Elimination of End effects in LMD Based on LSTM Network and Applications for Rolling Bearing Fault Feature Extraction

Elimination of End effects in LMD Based on LSTM Network and Applications for Rolling Bearing Fault Feature Extraction

Optimizing long short-term memory recurrent neural networks using ant colony optimization to predict turbine engine vibration

LSTM-Based Temperature Prediction for Hot-Axles of Locomotives

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