Fiber Optic Vibration Data Curation for Rare Event Detection
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Solution Overview
Problem
Monitoring linear assets using fiber optic cables generates vast amounts of vibration data, making it challenging to identify rare and potentially hazardous events, and uploading all data for analysis can exceed bandwidth limits.
Innovation Solution
A computer-implemented method and system that curate and pre-select vibration data by identifying data clusters with sudden energy changes, determining event locations, and filtering out irrelevant data, using algorithms like Z-score normalization and frequency band selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all vibration data is uploaded to cloud or computing network for event detection, then event classification accuracy is improved, but bandwidth consumption increases and bandwidth limits are exceeded
Solution Approach 1:
The system performs preliminary data curation and pre-selection at the edge device before uploading to the cloud. It calculates vibrational energy changes, identifies data clusters representing potential events, and filters out normal operational vibrations in advance. This preliminary processing reduces the data volume significantly while preserving the essential event information needed for accurate classification.
Solution Approach 2:
The system extracts only the relevant portions of vibration data that contain potential events. By identifying data clusters with sudden vibrational energy changes and filtering out normal operational patterns, the system separates useful event data from the vast amount of irrelevant background vibration data, transmitting only the extracted relevant information to the cloud.
2Measurement precision
If vibration data is collected at high measurement resolution (e.g., 500 microseconds) for real-time monitoring, then event detection capability is improved, but data volume increases making it challenging to identify rare events
Solution Approach 1:
The system changes the parameter of vibrational energy by calculating energy changes over time windows instead of processing raw vibration amplitudes directly. This transformation converts the high-resolution vibration data into a derived parameter (energy change) that highlights sudden events while reducing the complexity of identifying rare events in the raw data.
Solution Approach 2:
The system performs preliminary calculation of vibrational energy changes and identification of data clusters before detailed event analysis. This preliminary action organizes the high-resolution data into meaningful patterns, making subsequent event detection and classification more manageable despite the high measurement resolution.
3Quantity of substance
If data curation and pre-selection are performed to reduce data transmission, then bandwidth consumption is reduced, but processing time and computational resources are required
Solution Approach 1:
The system performs data curation periodically by analyzing vibration data in time windows and identifying data clusters at regular intervals. This periodic processing approach balances the need for real-time event detection with computational efficiency, reducing processing time while maintaining effective data filtering and selection.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Efficiently detects and filters relevant events in real-time, reducing data transmission and processing requirements, thereby avoiding bandwidth issues and enabling accurate event classification.
Implementation Method 1
Laser light propagating through the fiber optic cable may be backscattered by scatter sites in the fiber optic cable
Data Source
Figure 1~2
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AI summary
A computer-implemented method is provided. The method comprises receiving (S1) vibration data (VD), wherein the vibration data (VD) is inferred by back-scattered laser light from a fiber optic cable (21) and contains information of vibrational energy at a plurality of measurement segments (22, 23) of the fiber optic cable (21); determining (S2) whether a vibrational energy change during a recent time window (Δtr) in at least one measurement segment exceeds a predefined amplitude; if the predefined amplitude is exceeded, determining (S3) whether the vibrational energy change in the at least one measurement segment (22, 23) exceeds a predefined duration (D), and/or whether the vibrational energy change for measurement segments in proximity to the at least one measurement segment (22, 23) exceeds a predefined spatial extent (SE), thereby determining whether a data cluster (DC) representing the vibrational energy change is present in the vibration data (VD); and providing (S4) an event signal (ES) for the case that such data cluster (DC) is identified in the vibration data (VD). Further, a data processing system and a computer program product are provided.