Fault diagnosis of rope tension in hoisting systems based on vibration signals
| Autor: | Shaohua Xue, Jianping Tan, Shi Lixiang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Vibration
Support vector machine Computer science Control theory Materials Science (miscellaneous) Feature vector Particle swarm optimization Business and International Management Fault (power engineering) Industrial and Manufacturing Engineering Hilbert–Huang transform Energy (signal processing) Rope |
| Zdroj: | Vibroengineering PROCEDIA. 30:49-54 |
| ISSN: | 2538-8479 2345-0533 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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