Event-Based Failure Prediction for Equipment RUL Estimation
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Solution Overview
Problem
Existing techniques for predicting equipment failures and estimating remaining useful life (RUL) are inadequate for event-based sequential data, as they are designed for regularly sampled continuous sensor measurements and fail to utilize the domain knowledge encoded in discrete events, leading to challenges such as overfitting due to data scarcity and poor generalization.
Innovation Solution
The proposed solution involves data augmentation techniques, feature extraction modules, learnable neural network-based attention mechanisms, a data-adaptive optimization framework, and a cost-sensitive optimization framework to effectively handle event-based sequential data, enabling accurate failure prediction and RUL estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional regression-based approaches (neural networks, Hidden Markov Models) are applied to event-based sequential data, then the model structure can be established, but the model suffers from overfitting and poor generalization due to data scarcity
Solution Approach 1:
The patent applies data augmentation techniques to create synthetic copies of failure sequences by introducing semantic variations (e.g., adding/removing events, modifying event attributes) while preserving the underlying failure patterns. This generates additional training samples from limited real failure data, improving model generalization without requiring more actual failure occurrences.
Solution Approach 2:
The patent transforms event-based sequential data into multiple parameter representations including event sequences, event attributes, temporal patterns, and aggregated statistics. By changing the parameter representation of the same underlying data, the model can learn from diverse feature perspectives, effectively increasing the information content available for training.
2Measurement precision
If domain knowledge encoded in discrete events is not utilized, then the model can process raw sensor measurements, but the model fails to capture important failure patterns and degradation trends
Solution Approach 1:
The patent introduces an event-based intermediate representation layer between raw sensor measurements and the prediction model. This intermediary encodes domain knowledge about failure patterns, degradation trends, and critical thresholds into structured event sequences, allowing the model to process information in a domain-relevant format that captures essential failure characteristics.
Solution Approach 2:
The patent segments the continuous sensor measurement stream into discrete event-based units that represent specific failure conditions or degradation milestones. By segmenting the data into meaningful event categories (e.g., threshold violations, anomaly detections, operational transitions), the model can focus on learning from significant failure-related patterns rather than processing all raw measurements equally.
3Quantity of substance
If data augmentation is applied to generate semantically similar samples, then the training data quantity increases, but the computational complexity and training time increase
Solution Approach 1:
The patent applies partial data augmentation by generating synthetic samples only for underrepresented failure modes or critical failure patterns, rather than augmenting all training data uniformly. This selective approach increases training data quantity for the most important cases while limiting the overall computational overhead and training time increase.
Data Source
AI summary
Example implementations involve systems and methods for predicting failures and remaining useful life (RUL) for equipment, which can involve, for data received from the equipment comprising fault events, conducting feature extraction on the data to generate sequences of event features based on the fault events; applying deep learning modeling to the sequences of event features to generate a model configured to predict the failures and the RUL for the equipment based on event features extracted from data of the equipment; and executing optimization on the model.