Abstract
Fault diagnosis of rope tension is of great significance for safety in hoisting systems. A novel diagnosis method based on the vibration signals of the head sheaves is proposed. First, the signal is decomposed by the ensemble empirical mode decomposition (EEMD); then the main intrinsic module functions (IMFs) are extracted by correlation analysis. Second, the energy and the permutation entropy (PE) of the main IMFs were calculated to create the feature vector. Third, a particle swarm optimization - support vector machine (PSO-SVM) is applied to classify tension states. The effectiveness and advantage of the proposed method are validated by experiments. Compared with the conventional force-sensor-based method, it has clear advantages in sensor installation, data transmission, safety, and reliability.
Highlights
Hoisting equipment is widely used in industry, especially the mining industry
Fault diagnosis of rope tension is of great significance to hoisting safety because lifting containers are connected with winding drums by long wire ropes [1]
Conventional fault detection methods are based on force sensors that are installed at the connection parts of the ropes and the containers
Summary
Hoisting equipment is widely used in industry, especially the mining industry. A mine hoist is a key device to transport ore, materials, and people between the surface and underground. Conventional fault detection methods are based on force sensors that are installed at the connection parts of the ropes and the containers It faces some problems as follows: first, the sensor installation may affect the safety of the original structure [2]. A novel fault diagnosis method for the rope tension based on vibration signals is proposed. Because of the easy-using and excellent performance for complex signals, the EMD-based methods have been widely applied in fault diagnosis [7]. FAULT DIAGNOSIS OF ROPE TENSION IN HOISTING SYSTEMS BASED ON VIBRATION SIGNALS. A fault diagnosis method for rope tension based on the vibration signal of the head sheave is proposed, which combines the EEMD decomposition, the feature extraction by PE and energy, and the PSO-SVM classification.
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