Light Source Monitoring via Data Chunking for Fast Fault Detection
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Solution Overview
Problem
Monitoring the operation of illumination modules in imaging systems, particularly those operating at high frequencies, is challenging due to the large number of samples required and varying sampling time windows, which complicates real-time fault detection and mitigation.
Innovation Solution
A light source monitoring system that aggregates sampled data into data chunks and groups, allowing for flexible and simultaneous monitoring across different operating modes by condensing the number of samples handled, using data chunking, grouping, and rolling sums to analyze light source performance in parallel.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the light source operates at high frequencies to improve productivity, then the number of samples required for monitoring increases significantly, but this increases the complexity of real-time fault detection
Solution Approach 1:
The monitoring system divides the high-frequency light source signal into multiple lower-frequency channels by sampling at different phases. Each channel operates at a reduced frequency, making individual monitoring simpler while collectively covering the full high-frequency operation through parallel processing of segmented signals.
2Reliability
If the system monitors all samples in real-time to improve reliability, then fault detection accuracy improves, but the processing time and system complexity increase
Solution Approach 1:
The sample set is segmented into multiple channels based on sampling phase, allowing the system to monitor all samples for comprehensive fault detection while distributing the processing load across parallel channels. This maintains high reliability through complete sample coverage while reducing per-channel processing complexity.
Solution Approach 2:
The system performs monitoring on multiple phases beyond what a single phase would provide, effectively oversampling the signal. This partial redundancy across phases ensures that faults are detected even if some samples are missed, improving reliability while the distributed nature keeps individual processing tasks manageable.
3Measurement precision
If the system uses multiple sampling phases to improve measurement precision, then fault detection capability improves, but the device complexity and data processing requirements increase
Solution Approach 1:
The sampling process is segmented into multiple phases with each phase capturing different portions of the high-frequency signal. This segmentation enables precise fault detection by comparing across phases while keeping each individual sampling channel relatively simple, as each handles only a portion of the total signal spectrum.
Data Source
AI summary
A system such as an imaging system may include an illumination module. The illumination module may include a light source monitoring system. The light source monitoring system may sample a light source signal and aggregate the samples into data chunks. The light source monitoring system may selectively aggregate the data chunks to form groups of a monitoring time window. The light source monitoring system may detect a fault based on a value characterizing light source performance during the monitoring time window.