Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing

Effective intelligent fault diagnosis has long been a research focus on the condition monitoring of rotary machinery systems. Traditionally, time-domain vibration-based fault diagnosis has some deficiencies, such as complex computation of feature vectors, excessive dependence on prior knowledge and diagnostic expertise, and limited capacity for learning complex relationships in fault signals. Furthermore, following the increase in condition data, how to promptly process the massive fault data and automatically provide accurate diagnosis has become an urgent need to solve. Inspired by the idea of compressed sensing and deep learning, a novel intelligent diagnosis method is proposed for fault identification of rotating machines. In this paper, a nonlinear projection is applied to achieve the compressed acquisition, which not only reduces the amount of measured data that contained all the information of faults but also realizes the automatic feature extraction in transform domain. For exploring the discrimination hidden in the acquired data, a stacked sparse autoencoders-based deep neural network is established and performed with an unsupervised learning procedure followed by a supervised fine-tuning process. We studied the significance of compressed acquisition and provided the effects of key factors and comparison with traditional methods. The effectiveness of the proposed method is validated using data sets from rolling element bearings and the analysis shows that it is able to obtain high diagnotic accuracies and is superior to the existing methods. The proposed method reduces the need of human labor and expertise and provides new strategy to handle the massive data more easily.

Intelligent Bearing Fault Diagnosis Method Combining Compressed Data Acquisition and Deep Learning

We present a generic extension of variational mode decomposition (VMD) algorithm to multivariate or multichannel data. The proposed method utilizes a model for multivariate modulated oscillations that is based on the presence of a joint or common frequency component among all channels of input data. We then formulate a variational optimization problem that aims to extract an ensemble of band-limited modes containing inherent multivariate modulated oscillations present in the data. The cost function to be minimized is the sum of bandwidths of all signal modes across all input data channels, which is a generic extension of the cost function used in standard VMD to multivariate data. Minimization of the resulting variational model is achieved through the alternating direction method of multipliers (ADMM) that yields an optimal set of multivariate modes in terms of narrow bandwidth and corresponding center frequencies. The proposed extension is elegant as it does not require any extra user-defined parameters for its operation i.e., it uses the same parameters as standard VMD. We demonstrate the effectiveness of the proposed method through results obtained from extensive simulations involving test (synthetic) and real world multivariate data sets. Specifically, we highlight the utility of the proposed method in two real world applications which include the separation of alpha rhythms in multivariate electroencephalogram (EEG) data and the decomposition of bivariate cardiotocographic signals that consist of fetal heart rate and maternal uterine contraction (FHR-UC) as its two channels.

Multivariate Variational Mode Decomposition

Review: Wavelet-based numerical analysis: A review and classification

A novel adaptive stochastic resonance method based on coupled bistable systems and its application in rolling bearing fault diagnosis

Complex variational mode decomposition for signal processing applications

Compressed sparse time–frequency feature representation via compressive sensing and its applications in fault diagnosis

Identification of crack in a rotor system based on wavelet finite element method

Most icing studies use 2D or quasi-3D models, ignoring the effects of blade span and tip icing. In this paper, a new model for predicting 3D vertical axis wind turbine blades icing by supplementing the blade span is established, the icing characteristics of blades under multiple working conditions are simulated step by step from the time scale, and the unsteady icing laws are compared and analyzed. Ice properties and surface temperature distribution determine the corresponding deicing power. The analysis results show that the unfrozen water film flows in the spanwise direction of the blade, resulting in a smaller icing limit on the pressure surface than the two-dimensional model, which indicates that the chordwise anti-icing structure can be set in a small range. At the same time, the annular ridge ice formed at the end of the blade slows down the reduction rate of the lift-drag ratio of the blade, effectively reducing the influence of the leading edge ice shape on the aerodynamic performance of the blade. Finally, the critical ice melting power density required to reach equilibrium temperature at the leading edge of the blade is reduced by about 11% compared to the trailing edge due to the increased thermal resistance of the gas-to-blade matrix interface by the ice layer.

https://downloads.hindawi.com/journals/js/2022/1527169.pdf

Qiuyun Mo

Papers

Complex variational mode decomposition for signal processing applications

Compressed sparse time–frequency feature representation via compressive sensing and its applications in fault diagnosis

Identification of crack in a rotor system based on wavelet finite element method

Numerical Calculation Method of Spanwise Icing Characteristics and Performance Loss of Three-Dimensional Vertical Axis Wind Turbine Blades