A remaining useful life prediction method combining multi-bearing common features evaluation and ConvLSTM networks

Key Points

• The central aim is to develop a method for accurately predicting the remaining useful life of systems using advanced networks.
• Developed a prediction method combining multi-bearing common features evaluation with ConvLSTM networks.
• Evaluated the method's effectiveness under various conditions.
• Analyzed performance metrics to determine accuracy of predictions.
• Demonstrated improved accuracy in life predictions compared to conventional methods.
• Achieved a significant reduction in prediction error rates with the ConvLSTM approach.

Abstract