An end-to-end framework combining time–frequency expert knowledge and modified transformer networks for vibration signal classification

Jin, Cancan; Chen, Xi

doi:10.1016/j.eswa.2021.114570

Cited by 38 publications

(7 citation statements)

References 49 publications

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“…The model uses both positional encoding and a dense interpolation embedding technique to incorporate temporal order into representation learning. In another study that classified vibration signals [133], time-frequency features such as Frequency Coefficients and Short Time Fourier Transformation (STFT) spectrums are used as input embeddings to the transformers. A multi-head attention-based model was applied to raw optical satellite time series classification using Gaussian Process Interpolation [134] embedding and outperformed convolution, and recurrent neural networks [135].…”

Section: Transformersmentioning

confidence: 99%

Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey

Foumani¹,

Miller²,

Tan³

et al. 2023

Preprint

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Time Series Classification and Extrinsic Regression are important and challenging machine learning tasks. Deep learning has revolutionized natural language processing and computer vision and holds great promise in other fields such as time series analysis where the relevant features must often be abstracted from the raw data but are not known a priori. This paper surveys the current state of the art in the fast-moving field of deep learning for time series classification and extrinsic regression. We review different network architectures and training methods used for these tasks and discuss the challenges and opportunities when applying deep learning to time series data. We also summarize two critical applications of time series classification and extrinsic regression, human activity recognition and satellite earth observation.

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Section: Transformersmentioning

confidence: 99%

Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey

Foumani¹,

Miller²,

Tan³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Finally, the output of the transformer block was sent into a linear layer to yield the predicted RUL. Jin and Chen [41] proposed a transformer-based framework for vibration signal classification. In this framework, the time-frequency spectrum features were extracted from raw signal data using a discrete Fourier transform and short-term Fourier transform.…”

Section: Studies Of the Attention Mechanism In Phmmentioning

confidence: 99%

Spatial attention-based convolutional transformer for bearing remaining useful life prediction

Chen¹,

Wang²,

Liu³

et al. 2022

Meas. Sci. Technol.

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The remaining useful life (RUL) prediction is of significance to the health management of bearings. Recently, deep learning has been widely investigated for bearing RUL prediction due to its great success in sequence learning. However, the improvement of the prediction accuracy of existing deep learning algorithms heavily relies on feature engineering such as handcrafted feature generation and time–frequency transformation, which increase the complexity and difficulty of the actual deployment. In this paper, a novel spatial attention-based convolutional transformer (SAConvFormer) is proposed to establish an accurate bearing RUL prediction model based on raw vibration data without prior knowledge or feature engineering. In this algorithm, firstly, a convolutional neural network enhanced by a spatial attention mechanism is proposed to squeeze the feature maps and extract the local and global features from raw bearing vibration data effectively. Then, the extracted senior features are fed into a transformer network to further explore the sequential patterns relevant to the bearing RUL. An experimental study using the XJTU-SY rolling bearings dataset revealed the merits of the proposed deep learning algorithm in terms of root-mean-square-error (RMSE) and mean-absolute-error (MAE) in comparison with other state-of-the-art algorithms.

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“…Considering their extensive use in other domains, Transformers bear great potential for SHM damage detection. Some researchers have made use of attention mechanisms similar to those found in Transformers for bearing remaining useful life prediction [56], but to the best of the authors' knowledge, only in [57] have proper Transformer models been used in this context. In this work, however, an end-to-end approach was not used; instead, the model was fed a pre-processed input composed of time series features such as Mel frequency Cepstral coefficients (MFCCs) and short-time Fourier transformation (STFT).…”

Section: Diagnosing Leading Edge Erosionmentioning

confidence: 99%

Modeling and Monitoring Erosion of the Leading Edge of Wind Turbine Blades

et al. 2021

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Leading edge surface erosion is an emerging issue in wind turbine blade reliability, causing a reduction in power performance, aerodynamic loads imbalance, increased noise emission, and, ultimately, additional maintenance costs, and, if left untreated, it leads to the compromise of the functionality of the blade. In this work, we first propose an empirical spatio-temporal stochastic model for simulating leading edge erosion, to be used in conjunction with aeroelastic simulations, and subsequently present a deep learning model to be trained on simulated data, which aims to monitor leading edge erosion by detecting and classifying the degradation severity. This could help wind farm operators to reduce maintenance costs by planning cleaning and repair activities more efficiently. The main ingredients of the model include a damage process that progresses at random times, across multiple discrete states characterized by a non-homogeneous compound Poisson process, which is used to describe the random and time-dependent degradation of the blade surface, thus implicitly affecting its aerodynamic properties. The model allows for one, or more, zones along the span of the blades to be independently affected by erosion. The proposed model accounts for uncertainties in the local airfoil aerodynamics via parameterization of the lift and drag coefficients’ curves. The proposed model was used to generate a stochastic ensemble of degrading airfoil aerodynamic polars, for use in forward aero-servo-elastic simulations, where we computed the effect of leading edge erosion degradation on the dynamic response of a wind turbine under varying turbulent input inflow conditions. The dynamic response was chosen as a defining output as this relates to the output variable that is most commonly monitored under a structural health monitoring (SHM) regime. In this context, we further proposed an approach for spatio-temporal dependent diagnostics of leading erosion, namely, a deep learning attention-based Transformer, which we modified for classification tasks on slow degradation processes with long sequence multivariate time-series as inputs. We performed multiple sets of numerical experiments, aiming to evaluate the Transformer for diagnostics and assess its limitations. The results revealed Transformers as a potent method for diagnosis of such degradation processes. The attention-based mechanism allows the network to focus on different features at different time intervals for better prediction accuracy, especially for long time-series sequences representing a slow degradation process.

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An end-to-end framework combining time–frequency expert knowledge and modified transformer networks for vibration signal classification

Cited by 38 publications

References 49 publications

Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey

Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey

Spatial attention-based convolutional transformer for bearing remaining useful life prediction

Modeling and Monitoring Erosion of the Leading Edge of Wind Turbine Blades

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