Intelligent Deep Learning Method for Forecasting the Health Evolution Trend of Aero-Engine With Dispersion Entropy-Based Multi-Scale Series Aggregation and LSTM Neural Network

Jiang, Wei; Zhang, Nan; Xue, Xiaoming; Xu, Yanhe; Wang, Xinzi

doi:10.1109/access.2020.2974190

Cited by 12 publications

(4 citation statements)

References 34 publications

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“…To achieve this, the LSTM algorithm proposed by Hochreiter and Schmidhuber 29 is adopted in the present work. The LSTM has been widely used in various time-series data-related tasks, such as the analysis of working conditions and prediction of working status and running data of power equipment, 30 prediction of the health evolution trend of aero-engine, 31 fault diagnosis of rolling bearings. 32 In the principle, to overcome the inherent gradient disappear problem in recurrent neural network (RNN) algorithms, LSTM improved RNN by designing a special structure of duplicate “cell,” 33 as shown in Figure 3.…”

Section: Methodsmentioning

confidence: 99%

Prediction of frictional braking noise based on brake dynamometer test and artificial intelligent algorithms

Wang

Zhong

Niu

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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Based on brake noise dynamometer test data, combined with the artificial intelligent algorithms, frictional braking noise is quantitatively analyzed and predicted in this study. To achieve this goal, a frictional braking noise prediction method is indicatively proposed, which consists of two main parts: first, based on the experimental data obtained from the brake noise dynamometer tests, and combining with the improved Long-Short-Term Memory (LSTM) algorithm, the coefficients of friction (COFs) are predicted under various braking test conditions. Then, based on the predicted braking COFs and other selected critical braking parameters, the quantitative prediction of frictional braking noise is obtained by means of the optimized eXtreme Gradient Boosting (XGBoost) algorithm. Finally, the inherent features of the XGBoost algorithm are employed to qualitatively analyze the importance of the main factors affecting the frictional braking noise. The prediction algorithms of COFs and frictional braking noise are validated by the brake dynamomter test data, and the R2 (R square) scores of both the LSTM and XGBoost prediction algorithms are 0.9, which verifies the feasibility of both algorithms. The main contribution of this work is to predict the braking noise based on a large set of test data and combined with the LSTM and XGBoost artificial intelligent algorithms, which can significantly save time for the brake system development and braking performance testing, and has significance to the rapid prediction of braking frictional noise and fast NVH (noise, vibration, and harshness) optimal design of frictional braking systems.

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Section: Methodsmentioning

confidence: 99%

Prediction of frictional braking noise based on brake dynamometer test and artificial intelligent algorithms

Wang

Zhong

Niu

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…In recent years, due to the rapid development of machine learning (ML) theory and artificial intelligence (AI) technology, more and more ML algorithms have been applied in regression and classification prediction applications. Zhang et al [11] employed the LSTM (Long Short-term Memory) algorithm to predict operation conditions of industrial IoT equipment and Jiang et al [12] applied it to predict the health evolution trends of an aero-engine. Zhang et al [13] combined CNN (Convolutional Neural Networks) and SVM (Support Vector Machine) for fault diagnosis of braking friction of mechanical equipment.…”

Section: Introductionmentioning

confidence: 99%

Braking Friction Coefficient Prediction Using PSO–GRU Algorithm Based on Braking Dynamometer Testing

Wang,

Yu,

Liu

et al. 2024

Lubricants

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The coefficients of friction (COFs) is one of the most important parameters used to evaluate the braking performance of a friction brake. Many indicators that affect the safety and comfort of automobiles are associated with brake COFs. The manufacturers of friction brakes and their components are required to spend huge amounts of time and money to carry out experimental tests to ensure the COFs of a newly developed braking system meet the required standards. In order to save time and costs for the development of new friction brake applications, the GRU (Gate Recurrent Unit) algorithm optimized by the improved PSO (particle swarm optimization) global optimization method is employed in this work to predict brake COFs based on existing experimental data obtained from friction braking dynamometer tests. Compared with the LSTM (Long Short-Term Memory) method, the GRU algorithm optimized by PSO avoids the accuracy reduction problem caused by gradient descent in the training process and hence reduces the prediction error and computational cost. The combined PSO–GRU algorithm increases the coefficient of determination (R2) of the prediction by 4.7%, reduces the MAE (mean absolute error) by 14.3%, and increases the prediction speed by 40.1% compared with the standalone GRU method. The prediction method based on machine learning proposed in this study can not only be applied to the prediction of automobile braking COFs but also for other frictional system problems, such as the prediction of braking noise and the friction of various bearing transmission components.

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“…Before fault recognition, it is necessary to detect the status of the rolling bearing. The original signal contains lots of information reflecting the operation states, which are nonlinearity and non-stationarity [12,13]. To analyze the signal with respect to time and frequency, researchers have proposed many vibration-based signal analysis methods such as empirical mode decomposition (EMD), wavelet transform [14], the Hilbert-Huang transform, etc.…”

Section: Introductionmentioning

confidence: 99%

A progressive fault diagnosis method for rolling bearings based on VMD energy entropy and a deep adversarial transfer network

Xu¹,

Li²,

Jiang³

et al. 2022

Meas. Sci. Technol.

Self Cite

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Bearing fault diagnosis with extensive labeled fault data has been achieved. In engineering, most machines are in a healthy state. When a fault does occur, the machine is shut down as soon as possible, thus it is difficult and uneconomical to collect enough fault data with labels to carry out a fault diagnosis. To solve the problem, a hybrid fault diagnosis method for rotating machinery, based on variational mode decomposition energy entropy (VMD-EE) and transfer learning (TL), is proposed. First, we decompose the original signal using VMD, calculate the EE value of the modal components, then build a dataset using fault data from these components. Second, a deep residual neural network is proposed to extract high-dimensional features from the dataset and divide the data into source and target domains according to the working conditions. Finally, W-distances are introduced to dynamically evaluate the importance of the conditional and marginal distribution probabilities to minimize the loss, with a feature-based TL method being used to dynamically balance the distribution adaption and reduce the difference in the probability distributions of the two domains. The proposed method is validated using the datasets of Case Western Reserve University and a machinery fault simulator platform. The VMD-based intelligent health detection and statistical analysis solve the problem of mode mixing very well and accurately detect signal faults, or not, by E E θ , the threshold of VMD-EE. Meanwhile, the accuracy of TL-based neural network fault diagnosis is up to 99.4%, and the losses are kept at around 0.02. These results show the accuracy and robustness of the proposed method in the absence of datasets and under varying operating conditions.

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Intelligent Deep Learning Method for Forecasting the Health Evolution Trend of Aero-Engine With Dispersion Entropy-Based Multi-Scale Series Aggregation and LSTM Neural Network

Cited by 12 publications

References 34 publications

Prediction of frictional braking noise based on brake dynamometer test and artificial intelligent algorithms

Prediction of frictional braking noise based on brake dynamometer test and artificial intelligent algorithms

Braking Friction Coefficient Prediction Using PSO–GRU Algorithm Based on Braking Dynamometer Testing

A progressive fault diagnosis method for rolling bearings based on VMD energy entropy and a deep adversarial transfer network

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