It is a great challenge to accurately and automatically identify different faults of the key components in rotating machinery. In this paper, a new method called feature fusion deep belief network is proposed for the intelligent fault diagnosis of rolling bearing. Firstly, a deep belief network (DBN) is constructed with several pre-trained restricted Boltzmann machines for feature learning of the raw vibration data. Secondly, locality preserving projection (LPP) is adopted to fuse the deep features to further enhance the quality of the learned deep features. Finally, the fusion deep features are fed into Softmax for automatic and accurate fault diagnosis. The proposed method is applied to analyze the experimental rolling bearing signals, and the results show that the proposed method is more effective than the traditional intelligent diagnosis methods.
JiangH.K., LiC.L. and LiH.X., An improved EEMD with multiwavelet packet for rotating machinery multi-fault diagnosis, Mechanical Systems and Signal Processing36 (2013), 225–239.
2.
ShaoH.D., JiangH.K., WangF.A. and WangY.N., Rolling bearing fault diagnosis using adaptive deep belief network with dual-tree complex wavelet packet, ISA Transactions69 (2017), 187–201.
3.
ChenJ.L., LiZ.P., PanJ., ChenG.G. and ZiY.Y., Wavelet transform based on inner product in fault diagnosis of rotating machinery: Areview, Mechanical Systems and Signal Processing70 (2016), 1–35.
4.
CabreraD., SanchoF., LiC., CerradaM., SanchezR.V., PachecoandF. and OliveiraJ.V.D., Automatic Feature extraction of time-seriesapplied to fault severity evaluation of helical gearbox instationary and non-stationary speed operation, Applied SoftComputing58 (2017), 53–64.
5.
LeiY.G., ZuoM.J., HeZ.J. and ZiY.Y., A multidimensional hybrid intelligent method for gear fault diagnosis, Expert Systems with Applications37 (2010), 1419–1430.
6.
ShaoH.D., JiangH.K., ZhangH.Z., DuanW.J., LiangT.C. and WuS.P., Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing, Mechanical Systems and Signal Processing100 (2018), 743–765.
7.
JinX., ZhaoM. and ChowT.W.S., Motor bearing fault diagnosis usingtrace ratio linear discriminant analysis, IEEE Transactions on Industrial Electronics61 (2014), 2441–2451.
8.
ShaoH.D., JiangH.K., ZhaoH.W. and WangF.A., A novel deepautoencoder feature learning method for rotating machinery fault diagnosis, Mechanical Systems and Signal Processing98 (2017), 187–204.
9.
SchmidhuberJ., Deep learning in neural networks: An overview, Neural Networks61 (2015), 85–117.
10.
JiaF., LeiY.G., LinJ., ZhouX. and LuN., Deep neural networks: Apromising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data, Mechanical Systems and Signal Processing72–73 (2016), 303–315.
11.
ShaoH.D., JiangH.K., ZhangX. and NiuM.G., Rolling bearing fault diagnosis using an optimization deep belief network, Measurement Science and Technology26 (2015), 115002.
12.
TamilselvanP. and WangP.F., Failure diagnosis using deep belieflearning based health state classification, Reliability Engineering & System Safety115 (2013), 124–135.
13.
TranV.T., AlThobianiF. and BallA., An approach to fault diagnosisof reciprocating compressor valves using Teager-Kaiser energy operator and deep belief networks, Expert Systems with Applications41 (2014), 4113–4122.
14.
ZabalzaJ., RenJ.C., ZhengJ.B., ZhaoH.M. and QingC.M., Novelsegmented stacked autoencoder for effective dimensionality reductionand feature extraction in hyperspectral imaging, Neurocomputing185 (2016), 1–10.
15.
DingX.X., HeQ.B. and LuoN.W., A fusion feature and itsimprovement based on locality preserving projections for rollingelement bearing fault classification, Journal of Sound and Vibration335 (2015), 367–383.
16.
HintonG.E. and SalakhutdinovR.R., Reducing the dimensionality ofdata with neural networks, Science313 (2006), 504–507.
17.
LeiY.G., HeZ.J. and ZiY.Y., EEMD method and WNN for faultdiagnosis of locomotive roller bearings, Expert Systems with Applications38 (2011), 7334–7341.
18.
ShaoH.D., JiangH.K., WangF.A. and ZhaoH.W., An enhancement deepfeature fusion method for rotating machinery fault diagnosis, Knowledge-Based Systems119 (2017), 200–220.
19.
KimS., ChoiY. and LeeM., Deep learning with support vector datadescription, Neurocomputing165 (2015), 111–117.
20.
VincentP., LarochelleH., LajoieI., BengioY. and ManzagolstackedP.A., stacked denoising autoencoders: Learning useful representations in adeep network with a local denoising criterion, Journal of Machine Learning Research11 (2010), 3371–3408.
