LED Driver Anomaly Detection Using Usage Profiles
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Solution Overview
Problem
Current LED driver devices in building infrastructure systems provide limited predictive capabilities for monitoring the current and future states of light systems, relying on basic operational data that lacks detail for reliable prediction of system states and potential failures.
Innovation Solution
A method for detecting anomalous events in driver devices by generating usage profiles based on parameter data, comparing current data to these profiles, and outputting signals for predictive maintenance, which includes monitoring physical parameters like dimming levels, power consumption, and environmental conditions to anticipate future issues without adding excessive cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If basic operational data is collected by LED driver devices, then data transmission is achieved, but the data lacks detail for reliable prediction of system states and potential failures
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing multiple operational parameters (forward voltage, reverse leakage current, power consumption, temperature) before actual failures occur. Usage profiles are generated in advance to establish baseline behavior, enabling early detection of deviations that indicate potential failures.
Solution Approach 2:
The monitoring system segments the analysis by evaluating multiple individual parameters (forward voltage, reverse leakage current, power consumption, temperature) separately and then综合分析 them together. This segmentation allows detailed examination of each parameter's contribution to overall system health while maintaining comprehensive prediction capability.
2Measurement precision
If additional test equipment is added to improve monitoring capabilities, then detection accuracy improves, but the cost of the technical building infrastructure increases disproportionally
Solution Approach 1:
The LED driver device performs self-monitoring by using its own existing operational data (forward voltage, reverse leakage current, power consumption, temperature) to detect anomalies. The system serves itself by comparing current parameter values against pre-generated usage profiles, eliminating the need for additional external test equipment.
Solution Approach 2:
The existing LED driver device is made multi-functional by enabling it to both drive the LED string and simultaneously monitor its own operational parameters for anomaly detection. The same hardware components that control LED operation are also used to collect diagnostic data, avoiding additional specialized equipment.
3Reliability
If comprehensive parameter monitoring is implemented, then predictive maintenance is enabled, but system complexity and resource requirements increase
Solution Approach 1:
Usage profiles are generated in advance during normal operation to establish baseline parameter ranges for each LED string. This preliminary action creates reference data that simplifies subsequent anomaly detection, as the system only needs to compare current readings against pre-established profiles rather than performing complex real-time analysis.
Solution Approach 2:
The system implements feedback by continuously comparing current operational parameters against usage profiles and generating anomaly detection outputs. When deviations are detected, the system provides feedback signals that trigger maintenance alerts, creating a closed-loop monitoring system that automatically responds to changing conditions without increasing complexity.
Data Source
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AI summary
The invention concerns a method for detecting anomalous events in a driver device of a building infrastructure system, a corresponding driver device, e.g. a light driver device, and a building infrastructure system. The method comprises, in a usage profile generating phase, obtaining parameter data for at least one physical parameter of the driver device during operation of the driver device in the building infrastructure system during a predefined time period; and generating a usage profile for the driver device based on the obtained parameter data. The generated usage profile includes characteristic parameter ranges for the at least one physical parameter determined based on the obtained parameter data. In a subsequent monitoring phase, the method obtains current parameter data for the at least one physical parameter of the driver device, then determines whether an anomalous event in the driver device has occurred by comparing the obtained current parameter data with the usage profile of the driver device. The method generates and outputs a signal generated based on the determined anomalous event.