Vibration Data

Vibration data analysis is crucial for predictive maintenance and structural health monitoring, aiming to identify machine or structural faults from sensor readings before catastrophic failure. Current research heavily utilizes machine learning, particularly deep learning architectures like convolutional neural networks (CNNs) and recurrent neural networks (RNNs, such as LSTMs), often coupled with feature extraction techniques based on frequency domain transformations or histogram theory, to classify fault types and predict remaining useful life. This field is significantly impacting industrial applications by enabling proactive maintenance, reducing downtime, and improving operational efficiency, while also driving advancements in data acquisition and labeling methodologies for improved model training and generalization.