Rolling Bearing Fault Diagnosis Based on MFDFA-SPS and ELM

Abstract

Rolling bearings, as important parts on supporting rotating shafts, frequently suffer from fatigue failures. If these rolling bearing failures are not found in time, it will have a huge impact on the whole mechanical system’s operating safety and operating life. To improve the diagnosis of different faults as well as different degrees of faults, a fault diagnosis method based on the multifractal detrended fluctuation analysis (MFDFA) method-singularity power spectrum (SPS) with extreme learning machine (ELM) is proposed. First, MFDFA and SPS analyses are performed on vibration acceleration signals with different faults and different degrees of damage under the same operating conditions, the spectral parameters of stability and quantitative description of differentiation are selected for feature extraction, and then the selected six feature parameters are put into the extreme learning machine for fault classification. The effectiveness of the MFDFA-SPS feature extraction method is demonstrated by analyzing and testing the measured bearing signals. The fault diagnosis accuracy of the bearing fault signals can reach 99.2% based on the MFDFA-SPS with ELM method by using the Case Western Reserve database. The improvements are 6.79% and 18.42% compared to the fault diagnosis methods based on MFDFA with ELM and SPS with ELM. Compared with the methods based on MFDFA-SPS with LSSVM classifier and SVM classifier, the accuracy improvements are 3.54% and 4.25%, respectively. The results show that the method proposed in this paper can achieve the diagnosis of bearing faults and the method based on MFDFA-SPS with ELM is more efficient than the methods based on MFDFA-SPS with LSSVM and SVM classifiers, which is suitable for practical engineering problem-solving.

Fault Diagnosis of Rolling Bearings Based on WPE by Wavelet Decomposition and ELM

“…Determine the parameters of the extreme learning machine (ELM) model using the five-fold cross-validation method, i.e., by the range of the number of nodes the hidden layer determined via the best training accuracy of each fold. ELM, which is a new type of single implicit layer feedforward neural network, consisted of the input layer, the hidden layer, and the output layer [ 15 ].…”

“…The method of cross-validation for ELM in Yang Y.’s paper is random validation: firstly, randomly select 40 out of 50 sets of signals as training sets, and the other 10 sets are the test sets; secondly, take the number of hidden nodes to be 50; finally, the average of the accuracies obtained from five runs is taken as the average accuracy. In Table 5 , we use Normal, I1, I2, O1, O2, B1, and B2 to do the calculations based on PE or WPE by wavelet decomposition and classified by the ELM with the random cross-validation used in the paper [ 15 ]. We also perform calculations using the same data (Normal, I1, I3, O1, O3, B1, and B3) as in Yang Y.’s paper and compare their average runtime and average accuracy using the methods in this paper.…”

The Diagnosis of Congestive Heart Failure Based on Generalized Multiscale Entropy-Wavelet Leaders

“…The Extreme Learning Machine (ELM) is a single implicit layer feedforward neural network [ 32 , 33 ]. By setting the number of neurons in the implicit layer, the connection weights between the implicit layer and the output layer are not adjusted iteratively but are determined once by solving a system of equations.…”

Applying Stochastic Models to Analyze Vibration Features of Roller Bearings

“…This model considers that the bearing's health status cannot be directly observed but can only be inferred from observable data, such as vibration signals or acoustic emissions. The HMM assumes that the bearing's health status follows a hidden state process that can be modelled by a Markov chain, and the observable data is generated based on the current hidden state [17][18][19][20][21][22][23][24][25][26][27][28].…”