Deep LSTM RUL Estimation for Long-Term Degradation Sequences
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Solution Overview
Problem
Existing methods for predicting the Remaining Useful Life (RUL) of components in systems face challenges due to long-term dependency issues and computational impracticality, especially when dealing with complex degradation processes influenced by various factors, including undefined fault modes and environmental conditions, which are not adequately addressed by traditional models like HMMs and RNNs.
Innovation Solution
The use of deep Long Short-Term Memory (LSTM) networks for RUL estimation, which processes time sequence sensor data, event data, and operational data to model sequential relationships, and applies data preparation techniques such as outlier removal, sequence construction, and feature extraction using convolutional neural networks (CNNs) to improve prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional RNN models are used for RUL estimation, then sequential information can be captured, but long-term dependency problems and vanishing/exploding gradients occur
Solution Approach 1:
The patent introduces LSTM units as an intermediary mechanism between input data and output predictions. The LSTM unit includes a cell state that acts as a mediator to transport information across time steps, with forget gates, input gates, and output gates controlling information flow. This intermediary structure solves the long-term dependency problem by providing a dedicated pathway for gradient flow through the cell state, preventing vanishing and exploding gradients while maintaining sequential information.
2Reliability
If HMM approach is used for prognostics, then sequence learning can be performed, but computational complexity and storage requirements become infeasible when hidden states grow large
Solution Approach 1:
The patent replaces the traditional HMM mechanical framework with a neural network-based LSTM system. Instead of using discrete hidden states and transition probability matrices that require extensive storage and computation, the invention uses continuous neuron activations and weight parameters that can be efficiently stored and computed. The LSTM network learns sequential patterns through gradient-based optimization rather than through explicit state transitions, significantly reducing computational complexity and storage requirements while maintaining or improving prognostics accuracy.
3Measurement precision
If sliding windows are applied over data sequence for RUL estimation, then time sequence information is considered, but each window is treated as independent losing sequential relationships
Solution Approach 1:
The patent implements continuity of useful action by designing the LSTM network to process sequences in a continuous manner where the hidden state at each time step carries forward information from previous steps. Unlike sliding windows that create independent segments, the LSTM maintains an ongoing memory of the entire sequence through its recurrent structure, ensuring that sequential relationships are preserved throughout the data processing without loss of contextual information.
Data Source
AI summary
Example implementations described herein are directed to systems and methods for estimating the remaining useful life of a component or equipment through the application of models for deriving functions that can express the remaining useful life over time. In an aspect, the failure acceleration time point is determined for a given type of component, and a function is derived based on the application of models on the failure acceleration time point.