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A multi-sensor feature fusion network model for bearings grease life assessment in accelerated experiments

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Abstract

This paper presents a multi-sensor feature fusion (MSFF) neural network comprised of two inception layer-type multiple channel feature fusion (MCFF) networks for both inner-sensor and cross-sensor feature fusion in conjunction with a deep residual neural network (ResNet) for accurate grease life assessment and bearings health monitoring. The single MCFF network is designed for low-level feature extraction and fusion of either vibration or acoustic emission signals at multi-scales. The concatenation of MCFF networks serves as a cross-sensor feature fusion layer to combine extracted features from both vibration and acoustic emission sources. A ResNet is developed for high-level feature extraction from the fused feature maps and prediction. Besides, to handle the large volume of collected data, original time-series data are transformed to the frequency domain with different sampling intervals and targeted ranges. The proposed MSFF network outperforms other models based on different fusion methods, fully connected network predictors and/or a single sensor source.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

AE:

Acoustic emission

FFT:

Fast Fourier transform

FC:

Fully connected

MCFF:

Multiple channel feature fusion

MSFF:

Multiple sensor feature fusion

RDF:

Raw data fusion

ResNet:

Residual neural network

SCFF:

Single-channel feature fusion

VB:

Vibration signal

L :

Grease life

\(T_{c}\) :

Temperature

\(S_{g}\) :

Half-life subtraction factor

N :

Shaft speed

\(\frac{DN}{(DN)_{L}}\) :

Speed penalty

D :

Average bearing diameter

W :

Radial load

C :

Specific dynamic capacity

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Authors and Affiliations

  1. NEC Laboratories America Inc., 4 Independence Way, Princeton, NJ, 08540, USA

    Zhuocheng Jiang

  2. Florida Institute of Technology, Melbourne, FL, 32901, USA

    Seong Hyeon Hong

  3. Department of Mechanical Engineering, University of South Carolina, Columbia, SC, 29208, USA

    Benjamin Albia & Yi Wang

  4. DEVCOM Army Research Laboratory, Aberdeen Proving Ground, MD, 21005, USA

    Adrian A. Hood & Asha J. Hall

  5. CFD Research Corporation, Huntsville, AL, 35806, USA

    Jackson Cornelius

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  1. Zhuocheng Jiang
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Correspondence to Yi Wang.

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This work was performed at University of South Carolina when Zhuocheng Jiang and Seong Hyeon Hong worked there as postdocs.

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Jiang, Z., Hong, S.H., Albia, B. et al. A multi-sensor feature fusion network model for bearings grease life assessment in accelerated experiments. Neural Comput & Applic 35, 5923–5937 (2023). https://doi.org/10.1007/s00521-022-07982-z

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