Battery Assembly Predictive Maintenance via Temporal Signal Trends
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Solution Overview
Problem
Conventional battery monitoring approaches fail to predict incipient faults in battery assemblies, relying on instantaneous threshold checking and reactive alarm generation, which leads to detection of issues only after the battery has failed, and are prone to inconsistent results due to manual interpretation and lack of proactive maintenance.
Innovation Solution
A predictive monitoring system that generates a temporal sequence of monotonically increasing values from monitored parameters, analyzing trends towards operational boundaries to predict fault conditions, allowing for proactive maintenance and detection of gradual deviations in battery health.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional instantaneous threshold checking is used for battery monitoring, then the system is simple to implement, but it fails to predict incipient faults and only detects issues after battery failure
Solution Approach 1:
The system performs preliminary analysis by generating a temporal sequence from monitored parameters and analyzing trends toward operational boundaries before actual faults occur. This allows prediction of incipient faults by detecting gradual deviations in battery health parameters over time, rather than waiting for threshold violations after failure.
2Measurement precision
If manual interpretation of battery parameters is used, then the monitoring approach is simple, but it produces inconsistent results and lacks proactive maintenance capability
Solution Approach 1:
The system replaces manual interpretation with automated temporal sequence analysis. The analyzer automatically generates monotonically increasing temporal sequences from monitored parameters and analyzes trends toward operational boundaries, providing consistent and objective fault detection without human intervention.
3Reliability
If comprehensive temporal analysis is implemented for predictive maintenance, then fault prediction accuracy improves, but computational complexity increases for embedded implementation
Solution Approach 1:
The system extracts only the essential temporal characteristics from monitored parameters by generating a temporal sequence of monotonically increasing values. This extraction approach captures the critical trend information needed for predictive maintenance while minimizing computational requirements for embedded implementation.
Data Source
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AI summary
A technique of monitoring a battery assembly may include monitoring a parameter associated with the battery assembly to obtain a number of monitored parameter samples. A temporal sequence of monotonically increasing values may be generated from the monitored parameter samples. The temporal sequence may be analyzed for an indication of a trend in the monitored parameter toward one of an upper operational boundary or a lower operational boundary to predict a fault condition of the battery assembly.