Rolling Bearing Fault Diagnosis Based on Deep Learning and Autoencoder Information Fusion

Abstract

The multisource information fusion technique is currently one of the common methods for rolling bearing fault diagnosis. However, the current research rarely fuses information from the data of different sensors. At the same time, the dispersion itself in the VAE method has asymmetric characteristics, which can enhance the robustness of the system. Therefore, in this paper, the information fusion method of the variational autoencoder (VAE) and random forest (RF) methods are targeted for subsequent lifetime evolution analysis. This fusion method achieves, for the first time, the simultaneous monitoring of acceleration signals, weak magnetic signals and temperature signals of rolling bearings, thus improving the fault diagnosis capability and laying the foundation for subsequent life evolution analysis and the study of the fault–slip correlation. Drawing on the experimental procedure of the CWRU’s rolling bearing dataset, the proposed VAERF technique was evaluated by conducting inner ring fault diagnosis experiments on the experimental platform of the self-research project. The proposed method exhibits the best performance compared to other point-to-point algorithms, achieving a classification rate of 98.19%. The comparison results further demonstrate that the deep learning fusion of weak magnetic and vibration signals can improve the fault diagnosis of rolling bearings.

Progress of Process Monitoring for the Multi-Mode Process: A Review

“…The model has a strong ability to deal with the new mode, but it cannot handle slightly variable drift faults or distinguish the difference between normal deviations and faults. Ma et al [89] used a variational automatic encoder and random forest information fusion method for bearing fault diagnosis and residual life prediction.…”

Cloud Based Fault Diagnosis by Convolutional Neural Network as Time–Frequency RGB Image Recognition of Industrial Machine Vibration with Internet of Things Connectivity

“…It can be an additional sensor only for fault diagnosis or a sensor already present in the system used by the control algorithms. The electromechanical machine or power system can be investigated by many different sensors and signals: current [ 71 , 72 ] and voltage [ 73 , 74 ], torque [ 75 , 76 ], angular velocity/position [ 77 , 78 ], linear 3-axis acceleration/speed/position [ 16 , 17 ], Doppler laser vibrometer [ 79 ], transmission coefficient and reflexion coefficient of omnidirectional antenna [ 80 ], strain/tension [ 81 , 82 , 83 , 84 ], power consumption [ 85 , 86 , 87 , 88 ], internal/external temperature at selected points [ 89 , 90 ] or surface temperature by thermal camera [ 91 , 92 ], depending on frequency range: displacement [ 93 ], vibrations [ 15 , 18 , 94 , 95 , 96 ], sound [ 97 , 98 , 99 ], sound from several microphones [ 100 ] or ultrasound [ 101 , 102 ], vibro-acoustic [ 103 ], chemical analysis of lubrication [ 104 , 105 ], chemical analysis by spectral imaging [ 106 , 107 , 108 , 109 ], camera imaging in human colour spectrum [ 110 , 111 , 112 , <...…”

A graph neural network-based bearing fault detection method

“…AE (Autoencoder) is an unsupervised approach to deep learning. In 21 , the maximum correlation entropy was used as the loss function of the deep autoencoder and the critical parameters of the deep autoencoder were optimized to fit the signal characteristics using an artificial fish swarm algorithm; Wang et al 22 used a Gaussian radial basis kernel function and acoustic emission method for fault diagnosis of bearings with high diagnostic accuracy and applicability; Shao et al 23 proposed an ensemble deep autoencoder for intelligent fault diagnosis of rolling bearings (EDAEs) method for unsupervised feature learning from measured vibration signals; similar to 24 – 26 also improved on SAEs (Stacked Autoencoders) for fault diagnosis of bearings, both with improved detection results compared to traditional SAEs; Zhang et al 27 proposed a semi-supervised learning method based on a depth generating model of variational autoencoder (VAE), The VAE generation function is used to improve the classification performance when only a tiny portion of the data has labels; Cui et al 28 proposed a rolling bearing fault detection and classification method combining feature distance stacked autoencoder (FD-SAE) and support vector machines by organically combining machine learning and deep learning methods; Shao et al 29 used Morlet wavelet activation function to establish an accurate non-smooth vibration data based on stacked autoencoder with an accurate nonlinear mapping between the original non-stationary vibration data and various fault states using Morlet wavelet activation function; Ma et al 30 applied the weak magnetic detection method to rolling bearing whole life cycle monitoring with an improved variational autoencoder; Li et al 31 proposed a unified framework combining predictive generative denoising autoencoder (PGDAE) and deep coral network (DCN).…”