Washing Machine Bearing Abnormal Noise Detection Dataset

The current industrial landscape faces significant challenges in predictive maintenance, particularly in identifying abnormal noises from machinery components such as motor bearings. Existing solutions often rely on manual inspections, which are time-consuming and prone to human error. This dataset aims to address the need for a reliable method to detect potential anomalies in bearing performance through image analysis. The dataset comprises images captured from various bearings under different operational conditions. Data collection involved using high-resolution cameras in controlled factory environments to ensure consistent lighting and angle. Quality control measures included multiple rounds of labeling by trained technicians, consistency checks, and expert reviews to ensure high accuracy. The images are stored in JPG format, organized into folders based on bearing conditions.