Fault Diagnosis of Spindle Device in Hoist Using Variational Mode Decomposition and Statistical Features

Abstract

By analyzing nonlinear and nonstationary vibration signals from the spindle device of the mine hoist, it is a challenge to overcome the difficulty of fault feature extraction and accurately identify the fault of rotor-bearing system. In response to this problem, this paper proposes a new approach based on variational mode decomposition (VMD), SVM, and statistical characteristics such as variance contribution rate (VCR), energy entropy (EE), and permutation entropy (PE). Comparisons have gone to evaluate the performance of rolling bearing defect by using EMD (Empirical Mode Decomposition), MEEMD (Modified Ensemble EMD), BP (Back Propagation) network, single or multiple statistical characteristics, and different motor loads. The experiment was carried out on the mechanical failure simulator of the main shaft device of the hoist, which verified the reliability and effectiveness of the method. The results show that the diagnosis method is suitable for feature extraction of bearing fault signals, with the highest diagnosis accuracy. It can provide a good practical reference for the fault diagnosis of mechanical equipment of the hoist spindle device and has certain practical value.

Bearing fault diagnosis based on optimized variational mode decomposition and 1D convolutional neural networks

“…As the Adam optimization algorithm (Adam) may reduce the oscillations along the path of the steepest descent towards the optimum that is sometimes caused by stochastic gradient descent algorithm [44], we use the Adam algorithm to update the parameters of the deep NN. The stochastic gradient descent with momentum update is (20) where and are the first-order moment estimation and second-order moment estimation of the gradient, respectively, � and � is the correction of and , which can be approximated to the unbiased estimation of expectation. The parameter t represents the number of times, 1 are 2 are constants, controlling exponential attenuation.…”

“…The energy features are extracted from the intrinsic modal components decomposed by VMD and used as the input of support vector machine (SVM) to judge the working state and fault type of the bearing. Gu et al [20] proposed a new fault diagnosis method based on statistical characteristics such as variational pattern decomposition (VMD), SVM and variance contribution rate (VCR), energy entropy (EE), and permutation entropy (PE). Liu et al [21] proposed a feature extraction method based on parameter optimization of VMD and sample entropy, and further used to support SVM for fault diagnosis.…”

Multipoint Optimal Minimum Entropy Deconvolution Adjusted for Automatic Fault Diagnosis of Hoist Bearing

“…Once a bearing fault occurs, it is effortless to cause serious safety accidents. erefore, the study of bearing automatic fault diagnosis is very important for a safe operation of the hoisting system [1,2].…”

Application of ICEEMDAN Energy Entropy and AFSA-SVM for Fault Diagnosis of Hoist Sheave Bearing

“…As one of the most intelligent and cutting-edge fields in the field of artificial intelligence, the application of SVM received increasing attention [ 12 , 13 , 14 ], reflecting in the aspects of regression estimation, pattern recognition, and fault diagnosis, such as the fault diagnosis of the vehicle suspensions, automatic detection of diabetic eye disease, and predictive control of the industrial process [ 15 , 16 , 17 ]. In the aspect of bearing fault diagnosis, the application of SVM has been reported in many literatures [ 18 , 19 , 20 , 21 ]. For example, Gu et al [ 18 ] proposes an approach based on the variational mode decomposition, support vector machine, and statistical characteristics to analyze the vibration signals of bearing on the spindle device of the mine hoist.…”

“…In the aspect of bearing fault diagnosis, the application of SVM has been reported in many literatures [ 18 , 19 , 20 , 21 ]. For example, Gu et al [ 18 ] proposes an approach based on the variational mode decomposition, support vector machine, and statistical characteristics to analyze the vibration signals of bearing on the spindle device of the mine hoist. Li et al [ 19 ] used ensemble SVM for the intelligent classification of the bearing’s faults, combined with the nonlinear dynamics entropy.…”