Medical IoT Sequence Mining for Fuzzy Timestamp Failure Prediction
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Solution Overview
Problem
Current predictive maintenance methods for medical devices, such as MRI and PET scanners, face challenges in accurately identifying system failures from vast amounts of log data due to imprecise timestamps and noise in event sequences, leading to inefficient maintenance and potential downtime.
Innovation Solution
The implementation of a modified SPADE algorithm that treats timestamps as 'fuzzy' and applies iterative sequence mining to identify predictive sequences, considering sequences with slight temporal variations and noise reduction through clustering, to improve the accuracy of system failure prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional sequence mining algorithms are used on medical device log data, then system failure prediction can be performed, but the precision of failure prediction deteriorates due to imprecise timestamps and noise in event sequences
Solution Approach 1:
The patent transforms the timestamp parameter from a precise numerical value to a fuzzy temporal interval representation. By changing how time is parameterized (from exact timestamps to time windows), the system can handle imprecise timing data while maintaining prediction accuracy. This is achieved by converting precise timestamp data into fuzzy temporal representations that capture the essence of event ordering without requiring exact timing precision.
Solution Approach 2:
The patent replaces traditional exact-matching sequence mining mechanics with fuzzy logic-based sequence mining. Instead of requiring precise temporal and sequential matching of events, the system uses fuzzy set theory to determine sequence membership, allowing for temporal variations and noise in the event data while still identifying meaningful failure patterns.
2Measurement precision
If strict temporal sequence matching is applied, then sequence mining accuracy is maintained, but the system becomes sensitive to timestamp imprecision and noise
Solution Approach 1:
The patent changes the matching criterion from exact temporal equality to fuzzy temporal inclusion. Instead of requiring events to match at exact timestamps, the system checks whether events fall within fuzzy time windows, making the matching process robust to timestamp imprecision while maintaining the ability to identify meaningful sequences.
Solution Approach 2:
The patent introduces fuzzy temporal intervals as an intermediary layer between the raw timestamp data and the sequence matching process. This intermediary representation acts as a buffer that absorbs timestamp imprecision and noise, allowing sequences to be matched based on their logical temporal relationships rather than exact timing.
3Reliability
If all possible sequences are mined from log data, then comprehensive failure patterns are identified, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent extracts and focuses only on the essential temporal and sequential relationships needed for failure prediction, rather than mining all possible sequences. By using fuzzy sequence mining with predefined temporal constraints and significance thresholds, the system extracts only the most relevant failure patterns, reducing computational complexity while maintaining predictive power.
Solution Approach 2:
The patent segments the sequence mining process into manageable stages: first identifying individual fuzzy sequences, then combining them into longer patterns, and finally filtering for significance. This segmented approach allows the system to handle large volumes of log data efficiently by processing sequences in hierarchical stages rather than attempting to mine all patterns simultaneously.
Data Source
AI summary
A computer-implemented method for configuring a computing device for predictive maintenance, a computer-implemented method for predictive maintenance as well as a predictive maintenance apparatus are disclosed. Training log files including event sequences are examined iteratively for sequences of increasing length in order to determine a set of configuration data containing event sequences that have high predictive power for a system failure. Forward and backward gap values are defined such that not only sequences in the exact same temporal order as in the training log files are examined but also sequences with slightly different temporal ordering. In this way, possibly imprecise and/or incorrect time stamps in log files are compensated.