Laser Burst Logging for Predictive Failure Detection
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Solution Overview
Problem
Current laser system monitoring technologies fail to detect potential failures in advance, only providing notice after component failure has occurred, which can lead to costly and time-consuming repairs, and lack the capability for remote diagnosis and preventative action.
Innovation Solution
Embedding oscilloscope-type behavior within a computer-connected laser system to capture and store data at the time of component failure, synchronizing data capture during system events, and using burst logging to aggregate and analyze data for predictive diagnostics and remote monitoring, enabling automated analytics and preventative actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional laser system monitoring is used, then system simplicity is maintained, but failure detection capability is insufficient and only provides notice after component failure
Solution Approach 1:
The system performs preliminary actions by continuously monitoring sensor data and comparing it against stored failure-mode profiles before actual failure occurs. This allows the system to detect potential failures in advance and trigger alerts or automated responses, transforming reactive monitoring into proactive failure prevention while maintaining manageable complexity through pre-configured analysis rules
Solution Approach 2:
The system creates copies of failure data by storing multiple sets of sensor data collected at different times, including normal operation data and failure-mode data. These data copies are stored in databases and can be compared against current sensor readings to identify emerging failure patterns, enabling historical analysis without requiring complex real-time processing of every data point
2Loss of information
If data is captured continuously at high sampling rates for all sensors, then complete failure data is obtained, but data storage requirements and processing load increase significantly
Solution Approach 1:
The system applies local quality by capturing sensor data at different sampling rates tailored to each sensor's characteristics and failure detection needs. Critical sensors that provide early warning of failure modes are sampled at higher rates, while less critical sensors use lower sampling rates. This selective approach ensures complete failure data is captured for important parameters while minimizing overall data storage requirements
Solution Approach 2:
The system uses partial action by selectively capturing and storing only the data necessary for specific failure mode detection. Rather than continuously storing all sensor data at maximum resolution, the system captures data at appropriate rates for each failure scenario and stores multiple sets of targeted data, achieving sufficient failure detection capability without the excessive storage burden of universal high-rate sampling
3Ease of operation
If laser systems are monitored remotely over networks, then remote diagnosis capability is enabled, but network bandwidth consumption and data transmission time increase
Solution Approach 1:
The system extracts and transmits only the essential failure detection data and alerts to remote locations, rather than transmitting complete raw sensor datasets. By identifying and sending only the critical information needed for failure diagnosis—such as anomaly detections, failure mode matches, and key sensor readings—the system enables effective remote monitoring while minimizing network bandwidth consumption and data transmission time
Solution Approach 2:
The system performs preliminary data processing and analysis locally before transmission, pre-packaging failure detection results and insights for remote access. By conducting initial failure mode matching and data correlation locally, the system prepares condensed failure information in advance, reducing the time and bandwidth required for remote diagnosis while maintaining comprehensive monitoring capabilities
Data Source
AI summary
A burst logging system logs and transmits to a local or remote computing system event data related to errors in and or potential failures of laser system components. The system further provides for capturing data at different rates from different sensors, synchronization of data capture associated with system events and the possibility for aggregation of data from multiple systems, which can in turn be leveraged to predict and or remediate future system events.