RNN Learning Rate Feedback for Faster Machine Failure Prediction
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Solution Overview
Problem
Existing methods for accelerating the convergence of Recurrent Neural Networks (RNNs) for machine failure prediction, such as standard Nesterov Accelerated Gradient (NAG) and its variants, face challenges in speed and risk of non-convergence, especially when predicting failures in a large number of machines within a short time frame.
Innovation Solution
A method and system that updates learning rate and weight values in RNNs based on a proposed mechanism, where the initial learning rate is determined by the standard deviation of basic memory depth values, and weight updates are made based on current pattern errors calculated as vector distances between machine failure and predicted sequences, without altering standard gradient calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If standard Nesterov Accelerated Gradient (NAG) method or its variants are used to accelerate gradient descent in RNN, then convergence speed is improved, but the risk of non-convergence increases and computation becomes more complex
Solution Approach 1:
The patent changes the learning rate parameter dynamically based on the magnitude of weight updates. Specifically, when the magnitude of weight update exceeds a threshold, the learning rate is reduced; otherwise it is increased. This adaptive parameter adjustment accelerates convergence while maintaining stability and avoiding non-convergence risks associated with standard NAG methods.
Solution Approach 2:
The patent introduces a feedback mechanism where the learning rate adjustment is based on the observed magnitude of weight updates in previous iterations. This feedback loop allows the system to automatically adjust the learning rate to optimize convergence speed while preventing oscillations and non-convergence, thereby improving both speed and reliability simultaneously.
2Speed
If standard Nesterov Accelerated Gradient (NAG) method or its variants are used to accelerate gradient descent in RNN, then convergence speed is improved, but computational complexity increases
Solution Approach 1:
The patent simplifies the computational process by changing only the learning rate parameter based on weight update magnitude, rather than implementing the full NAG algorithm with its complex momentum terms and look-ahead gradients. This approach achieves acceleration with minimal additional computational overhead, avoiding the complexity of standard NAG methods.
3Measurement precision
If more epochs are used for training RNN to improve prediction accuracy for large number of machines, then prediction accuracy is improved, but training time increases
Solution Approach 1:
The patent applies dynamic learning rate adjustment during training, where the learning rate adapts based on the magnitude of weight updates. This dynamic adjustment allows the training process to converge faster while maintaining prediction accuracy, significantly reducing the number of epochs needed to train RNN models for large-scale machine failure prediction.
Data Source
AI summary
Embodiments of the invention provide a method and system for accelerating convergence of Recurrent Neural Network (RNN) for machine failure prediction. The method comprises: setting initial parameters in RNN wherein the initial parameters include an initial learning rate which is determined based on a standard deviation of a plurality of basic memory depth values identified from a machine failure sequence; training RNN based on the initial parameters and at the end of each predetermined time period, calculating current pattern error based on a vector distance between the machine failure sequence and current predicted sequence; and if the current pattern error is less than or not greater than a predetermined error threshold value, determining, by the processor, an updated learning rate based on the current pattern error, and updating weight values between input and hidden units in RNN based on the updated learning rate.