Energy Harvesting Device Failure Detection via Server Mapping
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Solution Overview
Problem
Energy harvesting devices face reliability issues when they cannot connect to a server due to insufficient energy from their environmental sources, making it difficult to determine whether the device is offline due to lack of energy or a failure, leading to potential unnecessary maintenance alarms.
Innovation Solution
An energy harvesting system that uses a server with an advanced algorithm incorporating machine learning or neural networks to create a mapping database based on secondary information sources, determining the operational state of devices by comparing current conditions to historical data, distinguishing between reasonable offline states and device failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If energy harvesting devices operate without continuous server connection, then device autonomy and reliability are improved, but the ability to detect device failures is worsened
Solution Approach 1:
The system implements feedback by continuously monitoring energy production levels and comparing them against historical data and environmental conditions. The server receives periodic status reports from devices and sends back diagnostic information, enabling remote failure detection without requiring constant connectivity. This feedback loop allows the system to maintain device autonomy while improving failure detection capability through data analysis.
Solution Approach 2:
The system performs preliminary actions by establishing baseline performance metrics and creating predictive models before failures occur. Historical energy production data is analyzed in advance to identify patterns and anomalies that indicate potential failures. This preliminary analysis enables the system to detect failures earlier and distinguish between temporary energy shortages and actual device failures.
2Measurement precision
If monitoring frequency is increased to detect failures, then failure detection accuracy is improved, but energy consumption is worsened
Solution Approach 1:
The system implements periodic monitoring at optimized intervals rather than continuous monitoring. Devices report their status at predetermined time intervals, and the server performs batch analysis of accumulated data. This periodic action maintains adequate failure detection accuracy while significantly reducing the energy consumption associated with constant communication and processing.
Solution Approach 2:
The system applies partial monitoring by focusing measurement resources on critical parameters and high-risk devices. Instead of monitoring all devices at maximum frequency, the system adjusts monitoring intensity based on device importance, historical performance, and current environmental conditions. This partial action approach maintains detection accuracy for critical failures while reducing overall energy consumption.
Data Source
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Figure 3
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AI summary
The invention relates to a method that comprises: obtaining information relating to an energy harvesting device (1 1) from a secondary information source by a server (15) comprising a mapping database, comparing the obtained information to the information in the mapping database by the server (15), and determining a default state of the energy harvesting device (1 1). The invention further relates to an apparatus, a system and a computer program product comprising instructions to perform the method.