Enhanced Fault Diagnosis in Time-series Data by Deep Learning

The increasing complexity and demands of modern machinery systems necessitate precise and timely fault diagnosis in time-series data. Effective fault detection, especially in components like bearings, is crucial for reducing unexpected downtime, and maintenance costs, and ensuring operational safety. Traditional fault diagnosis methods have rendered limited success due to their inability to discern intricate patterns in multifaceted time-series data. In response to this challenge, this paper introduces an enhanced diagnostic methodology harnessing the power of deep learning. Utilizing a ResNet50 model that has been pre-trained on the ImageNet dataset and subsequently fine-tuned for time-series data, we propose a novel approach to convert time-series data into a format amenable for this deep convolutional neural network. This methodology allows the model to identify subtle fault signatures that might be overlooked by traditional techniques. Comprehensive experiments on bearing fault datasets underscore the model’s promising performance relative to conventional methods, suggesting potential improvements in accuracy and computational efficacy. These results offer a glimpse into the possible integration of deep learning models, pre-trained on extensive datasets like ImageNet, into machinery fault diagnosis.