Rope Tension Fault Diagnosis in Hoisting Systems Based on Vibration Signals Using EEMD, Improved Permutation Entropy, and PSO-SVM

Abstract

Fault diagnosis of rope tension is significantly important for hoisting safety, especially in mine hoists. Conventional diagnosis methods based on force sensors face some challenges regarding sensor installation, data transmission, safety, and reliability in harsh mine environments. In this paper, a novel fault diagnosis method for rope tension based on the vibration signals of head sheaves is proposed. First, the vibration signal is decomposed into some intrinsic mode functions (IMFs) by the ensemble empirical mode decomposition (EEMD) method. Second, a sensitivity index is proposed to extract the main IMFs, then the de-noised signal is obtained by the sum of the main IMFs. Third, the energy and the proposed improved permutation entropy (IPE) values of the main IMFs and the de-noised signal are calculated to create the feature vectors. The IPE is proposed to improve the PE by adding the amplitude information, and it proved to be more sensitive in simulations of impulse detecting and signal segmentation. Fourth, vibration samples in different tension states are used to train a particle swarm optimization–support vector machine (PSO-SVM) model. Lastly, the trained model is implemented to detect tension faults in practice. Two experimental results validated the effectiveness of the proposed method to detect tension faults, such as overload, underload, and imbalance, in both single-rope and multi-rope hoists. This study provides a new perspective for detecting tension faults in hoisting systems.

Rolling Bearing Fault Diagnosis Based on VMD-MPE and PSO-SVM

“…Common optimization algorithms include the genetic algorithm (GA) [ 28 ] and particle swarm optimization algorithm (PSO) [ 29 , 30 ]. Xue et al [ 31 ] used the PSO-SVM diagnostic model to diagnose the tension of wire rope in the hoisting system and compared it with the GA-SVM diagnostic model. The results show that the identification accuracy of PSO-SVM is higher than GA-SVM.…”

Research on a small sample fault diagnosis method for a high-pressure common rail system

“…As a complex system consisting of a high-pressure oil pump, common rail pipe, electronically controlled injector, and high-pressure fuel pipe, a high-pressure common rail system is susceptible to various failures during operation due to shock and vibration, wear, corrosion, and aging. In recent years, wavelet analysis, empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), [1][2][3][4][5][6][7][8] and other time domain analysis methods have been widely used in the analysis and diagnosis of diesel engine faults. [9][10][11][12] In the field of eigenvalue identification, energy entropy, permutation entropy, singular values, and time-domain features are applied to the extraction of feature parameters.…”

Expert Control of Mine Hoist Control System

“…It is the key equipment in production and is the only channel to transport person and material in the mines. Its performance directly affects the reliability and safety of production; once it fails, not only will it seriously damage the device but also cause server casualties [1][2][3]. In this paper, we are concerned on the designing expert control algorithm for the mine hoist control system.…”