Rolling Bearing Sub-Health Recognition via Extreme Learning Machine Based on Deep Belief Network Optimized by Improved Fireworks

Abstract

Rolling bearings, as the main components of the large industrial rotating equipment, usually work under complex conditions and are prone to break down. It can provide a certain theoretical basis for identifying the sub-health state of the industrial equipment by the analysis from the incipient weak signals. Thus, a subhealth recognition offline algorithm based on Refined Composite Multiscale Dispersion Entropy (RCMDE) and Deep Belief Network-Extreme Learning Machine (DBN-ELM) optimized by Improved Firework Algorithm (IFWA) is proposed. First of all, in light of the drawbacks that it is easy to fall into local optima and cross the boundary for exploding fireworks in Firework Algorithm (FWA), Cauchy mutation and adaptive dynamic explosion radius factor coefficient is introduced into IFWA. Secondly, Maximum Correlation Kurtosis Deconvolution (MCKD) optimized by the improved parameters is used to process the incipient vibration signals with nonlinearity, nonstationary, and IFWA is used to adaptively adjust to the period and the filter length in MCKD(IFWA-MCKD). Then, each sequence of signals is further extracted the feature-RCMDE to rich sample diversity. Finally, combining the powerful unsupervised learning capability from DBN and the generalization capability from ELM, DBN-ELM can be established. What's more, in order to avoid the interference of human on the parameters, IFWA is used to optimize the number of hidden nodes in DBN-ELM, and the IFWA-DBN-ELM is established. It shows that the algorithm has the higher sub-health recognition accuracy, better robustness and generalization, which has a better industrial application prospect.

Remote Control and Fault Diagnosis of Port Mechanical Equipment Based on Wireless Communication Technology

“…At present, the fault diagnosis methods for port machinery mainly include the methods based on statistical analysis and signal processing. Luo et al proposed a diagnosis method for rolling bearing fault based on the principle of optimization index consistency, which can effectively detect the fault location [10]. Bril and others proposed a signal processing method based on sound feature and improved sparse representation, which effectively realized mechanical fault diagnosis [11].…”

Cholesky Factorization Based Online Sequential Multiple Kernel Extreme Learning Machine Algorithm for a Cement Clinker Free Lime Content Prediction Model

“…In this subsection, the RMSE, mean absolute percentage error (MAPE), mean absolute error (MAE), maximum absolute error (MAXE), and coefficient of determination (R 2 ) are used as performance assessment indexes for the fCaO models' prediction. The expressions of RMSE are the same as (30), and the other criteria expressions are given as follows:…”

“…In recent years, the extreme learning machine (ELM) [22,23] and kernel extreme learning machine (KELM) [24] are proposed and have been successfully applied to model identification, such as saliency detection [25], gesture recognition [26], image classification [27], nonlinear fault detection [28], seepage time soft sensor model of nonwoven fabric [29], and rolling bearing sub-health recognition [30]. However, different kernel functions have different characteristics, and the performances are varying in different applications.…”

Improved DBO-VMD and optimized DBN-ELM based fault diagnosis for control valve

“…Employing multiple layers of linear transformations can effectively extract deep features from noisy signals. A standard DBN is a special form of a Markov Random Field constructed by stacking multiple restricted Boltzmann machines (RBMs) [43]. In each RBM, v and h represent the visible layer and hidden layer, respectively.…”