Hybrid Telemetry Image Matching for Infrastructure Outage Prediction
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Solution Overview
Problem
Current systems fail to accurately predict system failures by not considering a series of previous events leading up to a current time, reducing the accuracy of failure detection and prevention.
Innovation Solution
A computing platform trains a hybrid deep learning model combining a convolutional neural network (CNN) and a recurrent neural network (RNN) to classify telemetry state images and identify patterns, using long short-term memory (LSTM) for enhanced pattern recognition, to predict system failures and trigger preemptive resolution commands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current system performance analysis is used to identify failure likelihood, then the analysis process is simple, but the accuracy of failure detection is reduced
Solution Approach 1:
The patent transforms temporal data (time series events) into a spatial representation by creating telemetry state images that visualize system state transitions over time. This dimensional transformation allows the application of CNN image processing techniques to temporal patterns, enabling the system to detect failure patterns by analyzing visual representations of system evolution rather than raw numerical data, thereby improving detection accuracy while maintaining manageable system complexity.
Solution Approach 2:
The patent employs a hybrid deep learning model that combines CNN (for spatial pattern recognition in telemetry state images) and RNN/LSTM (for temporal sequence analysis). This composite architecture leverages the strengths of different neural network types: CNN excels at extracting features from the visualized state images, while RNN/LSTM captures the temporal dependencies in the sequence of state transitions, together achieving superior failure detection accuracy compared to single-model approaches.
2Measurement precision
If a series of previous events in time series are considered, then the accuracy of failure detection is improved, but the complexity of the analysis system increases
Solution Approach 1:
The patent performs preliminary processing of raw telemetry data by generating normalized telemetry state images that represent system states at different time points. This preprocessing step transforms complex time series data into standardized visual representations before analysis, making the subsequent pattern recognition task more manageable. By preparing the data in advance in a standardized format, the system reduces the complexity of analyzing raw time series sequences while preserving all necessary temporal information for accurate failure detection.
3Measurement precision
If telemetry data is normalized to values between 0.0 and 1.0, then the pattern recognition accuracy is improved, but the data processing time increases
Solution Approach 1:
The patent applies parameter transformation by normalizing telemetry data values to a standard range of 0.0 to 1.0. This parameter change serves multiple purposes: it scales diverse telemetry metrics (CPU usage, memory utilization, network traffic) to a common scale, enabling meaningful comparison and pattern recognition across different data types. The normalization process also improves the convergence and performance of the deep learning models by providing uniformly scaled input features, thereby enhancing pattern recognition accuracy despite the additional processing step.
Data Source
AI summary
A computing platform may train a hybrid deep learning model, including a CNN and RNN, to predict system failure for a system based on telemetry state images and transitions between the telemetry state images. The computing platform may receive initial telemetry data, and may generate an initial telemetry state image. The computing platform may receive additional telemetry data, and may generate an additional telemetry state image. The computing platform may classify, using the CNN and based on historical telemetry state images, the initial telemetry state image and the additional telemetry state image. The computing platform may identify, using the RNN and based on the classified telemetry state images and transitions between the classified telemetry state images, a matching pattern. The computing platform may identify, using the identified matching pattern, a likelihood of failure for the system, and may cause modification of operations at the system to prevent a predicted failure.