DS-LSTM and Dense Network for Hardware Failure Prediction
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Solution Overview
Problem
Traditional methods for predicting hardware component failure in IoT devices lack consideration of time component variance, leading to limited accuracy and precision due to the complexity of parameter interactions and class imbalance issues in failure data analysis.
Innovation Solution
A deep learning-based system utilizing a double-stacked long short-term memory (DS-LSTM) network combined with a dense neural network (DNN) that incorporates time-series data and addresses class imbalance by oversampling failed device observations, effectively ranking continuous and categorical parameters to predict hardware component failure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional statistical binary classification approaches are used for component failure prediction, then the method is simple to implement, but the accuracy and precision are limited due to not considering time component variance
Solution Approach 1:
The patent transforms the prediction approach by changing from static parameter analysis to dynamic time-series parameter analysis. It incorporates temporal dimensions by using sequential data processing and time-dependent feature extraction, allowing the model to capture evolving patterns in device parameters that lead to failure, thereby improving prediction accuracy while maintaining reasonable complexity
Solution Approach 2:
The patent adds the time dimension to the prediction model by processing parameters as time-series data rather than static values. This dimensional expansion allows the system to analyze how parameters evolve over time and identify temporal patterns indicative of impending failure, significantly enhancing prediction precision without excessive complexity increase
2Measurement precision
If deep learning models with time-series input are used to improve prediction precision, then the accuracy increases, but the device complexity and computational requirements increase significantly
Solution Approach 1:
The patent segments the complex prediction task into distinct components: feature extraction from time-series data, temporal pattern recognition, and failure probability classification. By dividing the problem into manageable modules with specialized functions, the system achieves high prediction accuracy while keeping individual component complexity可控
Solution Approach 2:
The patent introduces intermediate representations and feature engineering layers that bridge raw time-series data and final predictions. These intermediary processing stages transform complex temporal patterns into meaningful features, reducing the burden on the final classification model and optimizing the overall system complexity-accuracy trade-off
3Measurement precision
If class imbalance is addressed by oversampling failed device observations, then the prediction precision for failed devices improves, but the training time and computational resources increase
Solution Approach 1:
The patent applies partial oversampling by selectively duplicating or weighting only the minority class (failed device) samples to a程度 that balances the class distribution. This partial action approach improves failure detection precision without fully duplicating the training process, thereby reducing the time penalty compared to complete oversampling while still achieving better precision
Data Source
AI summary
A system, method, and computer-readable medium are provided for a hardware component failure prediction system that can incorporate a time-series dimension as an input while also addressing issues related to a class imbalance problem associated with failure data. Embodiments utilize a double-stacked long short-term memory (DS-LSTM) deep neural network with a first layer of the DS-LSTM passing hidden cell states learned from a sequence of multi-dimensional parameter time steps to a second layer of the DS-LSTM that is configured to capture a next sequential prediction output. Output from the second layer is combined with a set of categorical variables to an input layer of a fully-connected dense neural network layer. Information generated by the dense neural network provides prediction of whether a hardware component will fail in a given future time interval.