Edge ML Failure Prediction for Live Vehicle Sensor Data
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Solution Overview
Problem
Existing data analytics techniques for predicting component failures in real-time are limited by the static nature of machine learning models, which require extensive retraining and are not adaptable to ever-changing live data, leading to inefficiencies and delays in identifying potential failures.
Innovation Solution
A system utilizing edge computing and real-time processing of current and past data values with machine learning algorithms to predict component failures, employing low-latency wireless networks and modified linear regression algorithms to continuously adapt to new data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional machine learning models are used for failure prediction, then model robustness is improved, but adaptability to changing live data deteriorates
Solution Approach 1:
The patent implements a dual-phase machine learning architecture where an offline phase trains a robust model on historical data, and an online phase continuously adapts the model to live data streams. This dynamic two-phase approach allows the system to maintain model robustness while adapting to changing conditions in real-time vehicle sensor data.
2Reliability
If offline training phase is used, then model learning capacity is improved, but processing time and computational resources deteriorate
Solution Approach 1:
The patent performs comprehensive model training in advance during an offline phase using historical sensor data, creating a pre-trained robust model. This preliminary action allows the system to avoid time-consuming retraining when deploying to production, as the model is already trained and ready for real-time adaptation during the online phase.
Solution Approach 2:
The system dynamically transitions from an offline training phase to an online adaptation phase, where the model continuously learns from live data without requiring full retraining. This dynamic approach balances comprehensive learning capacity with efficient real-time processing by updating the model incrementally rather than retraining from scratch.
3Measurement precision
If extensive retraining is performed, then model accuracy is improved, but computational resources and processing time deteriorate
Solution Approach 1:
The patent implements a dynamic two-phase learning system where extensive training occurs offline using historical data to achieve high accuracy, followed by efficient online adaptation to live data. This dynamic approach maintains prediction accuracy while minimizing computational resource consumption during real-time operation by avoiding repeated extensive retraining.
Data Source
AI summary
A method, apparatus, and system are described. The method includes generating a set of current values associated with at least one component included on a moving vehicle and providing the set of current values over a wireless network. The values are generated by one or more sensors. An edge computing device receives the current values. The method further includes processing the set of current values in real-time using at least one machine learning algorithm to identify a value of a point in time for a failure of one of the at least one component based on the set of current values and at least one set of past values received. The past values are stored in a memory. The set of current values are transmitted with a low time latency between the generating the set of current values and the processing of the set of current values.