Large discrepancy of sample distribution resulting from speed fluctuation is a great challenge to mechanical equipment health monitoring. Existing fault diagnosis methods are often limited by the acquisition mechanism of single-modal measurement. Considering the above problems, a multidimensional features dynamically adjusted adaptive network (MFDAAN) fused vibro-acoustic modal signals is proposed in this paper. The MFDAAN considers the context information of activation features by Funnel activation (FReLU) function to activate the vibro-acoustic signal features. In order to obtain fusion features, the multidimensional features of vibro-acoustic signals are dynamically adjusted at different stages by channel attention mechanisms, which is capable of considering the global information. Wasserstein distance is employed in the domain-adversarial training strategy to improve the property extracting domain-invariant features. The effectiveness of the MFDAAN is verified by cross-domain fault diagnosis experiments in two different scenarios. The results show that the MFDAAN can achieve good diagnostic effect for the tasks set of cross-domain fault diagnosis.
AnZLiSWangJ, et al. (2020) A novel bearing intelligent fault diagnosis framework under time-varying working conditions using recurrent neural network. ISA Transactions100: 155–170.
2.
ArjovskyMChintalaSBottouL (2017) Wasserstein generative adversarial networks. In: DoinaPYeeWhye T (eds), Proceedings of the 34th International Conference on Machine Learning. Proceedings of Machine Learning Research: PMLR, 70: 214–223.
3.
ChenMShaoHDouH, et al. (2022) Data augmentation and intelligent fault diagnosis of planetary gearbox using ILoFGAN under extremely limited samples. IEEE Transactions on Reliability72(3): 1–9.
4.
CuiJXiePWangX, et al. (2022) M2FN: an end-to-end multi-task and multi-sensor fusion network for intelligent fault diagnosis. Measurement204: 112085.
5.
GaninYLempitskyV (2015) Unsupervised domain adaptation by backpropagation. International Conference on Machine Learning. PMLR, 37: 1180–1189.
6.
GlowaczA (2018) Acoustic based fault diagnosis of three-phase induction motor. Applied Acoustics137: 82–89.
7.
GuanYMengZSunD, et al. (2021) 2MNet: multi-sensor and multi-scale model toward accurate fault diagnosis of rolling bearing. Reliability Engineering & System Safety216: 108017.
8.
HanBXingSWangJ, et al. (2023) A new multichannel deep adaptive adversarial network for cross-domain fault diagnosis. Measurement Science and Technology34: 065002.
9.
HeZShaoHWangP, et al. (2020) Deep transfer multi-wavelet auto-encoder for intelligent fault diagnosis of gearbox with few target training samples. Knowledge-Based Systems191: 105313.
10.
HeYTangHRenY, et al. (2022) A deep multi-signal fusion adversarial model based transfer learning and residual network for axial piston pump fault diagnosis. Measurement192: 110889.
11.
HuJShenLSunG (2018) Squeeze-and-excitation networks. Proceedings of the IEEE conference on computer vision and pattern recognition: 7132–7141.
12.
JiangXWangXHanB, et al. (2023) A novel hybrid distance guided domain adversarial method for cross domain fault diagnosis of gearbox. Measurement Science and Technology34: 065115.
13.
JinZHanQZhangK, et al. (2020) An intelligent fault diagnosis method of rolling bearings based on Welch power spectrum transformation with radial basis function neural network. Journal of Vibration and Control26: 629–642.
14.
KarabacakYEGürsel ÖzmenNGümüşelL (2022) Intelligent worm gearbox fault diagnosis under various working conditions using vibration, sound and thermal features. Applied Acoustics186: 108463.
15.
LiBChowMYTipsuwanY, et al. (2000) Neural-network-based motor rolling bearing fault diagnosis. IEEE Transactions on Industrial Electronics47: 1060–1069.
16.
LiWZhongXShaoH, et al. (2022) Multi-mode data augmentation and fault diagnosis of rotating machinery using modified ACGAN designed with new framework. Advanced Engineering Informatics52: 101552.
17.
LiuRYangBZioE, et al. (2018) Artificial intelligence for fault diagnosis of rotating machinery: a review. Mechanical Systems and Signal Processing108: 33–47.
18.
LuoJShaoHCaoH, et al. (2022) Modified DSAN for unsupervised cross-domain fault diagnosis of bearing under speed fluctuation. Journal of Manufacturing Systems65: 180–191.
19.
MaNZhangXSunJ. (2020) Funnel activation for visual recognition. In: Computer vision–ECCV 2020: 16th European conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XI 16. Berlin: Springer, 351–368.
20.
Van der MaatenLHintonG (2008) Visualizing data using t-SNE. Journal of Machine Learning Research9: 2579–2605.
21.
WangQWuBZhuP, et al. (2020) ECA-Net: efficient channel attention for deep convolutional neural networks. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition: 11534–11542.
22.
WangXMaoDLiX (2021) Bearing fault diagnosis based on vibro-acoustic data fusion and 1D-CNN network. Measurement173: 108518.
23.
XiaoYShaoHHanS, et al. (2022) Novel joint transfer network for unsupervised bearing fault diagnosis from simulation domain to experimental domain. IEEE/ASME Transactions on Mechatronics27: 5254–5263.
24.
YanSShaoHXiaoY, et al. (2022) Semi-supervised fault diagnosis of machinery using LPS-DGAT under speed fluctuation and extremely low labeled rates. Advanced Engineering Informatics53: 101648.
25.
ZhangDStewartEEntezamiM, et al. (2020) Intelligent acoustic-based fault diagnosis of roller bearings using a deep graph convolutional network. Measurement156: 107585.
26.
ZhangZHuangWLiaoY, et al. (2022) Bearing fault diagnosis via generalized logarithm sparse regularization. Mechanical Systems and Signal Processing167: 108576.
27.
ZhangXHuangWWangR, et al. (2023) Multi-stage distribution correction: a promising data augmentation method for few-shot fault diagnosis. Engineering Applications of Artificial Intelligence123: 106477.
28.
ZhangZWangJLiS, et al. (2023) Fast nonlinear blind deconvolution for rotating machinery fault diagnosis. Mechanical Systems and Signal Processing187: 109918.
29.
ZhaoXYaoJDengW, et al. (2023) Multiscale deep graph convolutional networks for intelligent fault diagnosis of rotor-bearing system under fluctuating working conditions. IEEE Transactions on Industrial Informatics19: 166–176.
