APPARATUS AND METHOD FOR FAILURE MODE CLASSIFICATION OF ROTATING EQUIPMENT BASED ON DEEP LEARNING DENOISING MODEL

摘要

Disclosed is an apparatus and method for detecting a defect in rotating equipment using vibration signal noise removal based on deep learning, and the apparatus and method for detecting defects in rotating equipment using vibration signal noise removal based on deep learning according to an embodiment of the present application, A data receiving unit for collecting data, a first learning unit for learning a deep learning-based noise removal model that removes noise from vibration data based on the collected vibration data, a first learning unit for learning the target vibration data from the equipment to be analyzed, and the learning The preprocessing is performed using a noise removal unit that removes noise from the target vibration data based on the noise removal model, a data preprocessor that performs preprocessing on the target vibration data from which the noise has been removed, and a pre-learned defect detection model. It may include a defect detection unit that determines whether a defect is based on the performed target vibration data.