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PT900 Dual-rotor System Fault Simulator
PT900 Dual-rotor System Fault Simulator PT900 Dual-rotor System Fault Simulator
PT900 Dual-rotor System Fault Simulator PT900 Dual-rotor System Fault Simulator
PT900 Dual-rotor System Fault Simulator PT900 Dual-rotor System Fault Simulator

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PT900 Dual-rotor System Fault Simulator

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Advanced Mechanical Bearing Gear Fault Simulator Test Bed is prone to mechanical damage to its internal components after prolonged use, leading to wear and failure. Therefore, it is necessary to perform feature mining, classification, and prediction of fault categories for rotating machinery such as bearings, gearboxes, and shafts. Vibration
signal analysis is the most widely used research method in the diagnosis of rotating machinery faults. By collecting
vibration acceleration signals from components such as gears and bearings with different damage conditions during
operation, machine learning methods are applied to classify and predict signals, and potential features of fault data
are excavated. The rotating machinery fault simulation test bench can simulate and reproduce common fault vibration signals of rotating machinery, making it difficult to extract early fault features for reproduction. With the development and improvement of machine learning technology, researchers have applied machine learning models to fault
diagnosis, improving diagnostic accuracy.
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