Predictive Maintenance Modeling for Telecom Module Fault Detection
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Solution Overview
Problem
In telecommunications networks, there is a need to accurately predict faults in modules before they occur to prevent network downtime and unnecessary costs, and to associate alarms with the correct modules within the network.
Innovation Solution
An apparatus and method for predictive maintenance that collects and processes data from telecommunications networks to train a predictive model, using primary and secondary data bundles to identify potential faults and anomalies, and employs techniques like data summarization and auto-labeling to improve model performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If predictive maintenance is implemented to detect faults before they occur, then network reliability is improved, but the complexity of data collection and processing increases
Solution Approach 1:
The patent segments the data collection process into multiple time-windows (primary data bundle for recent period, secondary data bundle for historical period) and organizes measurement time-series by network elements and physical modules. This segmentation allows the system to handle large volumes of network data systematically, improving reliability while managing complexity through structured data organization.
Solution Approach 2:
The system performs preliminary data collection and processing by gathering measurement time-series before fault detection is needed. Maintenance data is processed in advance to determine associations with physical modules, and predictive models are trained using historical data bundles. This preliminary action enables the system to be ready for real-time fault prediction without increasing operational complexity during actual maintenance operations.
2Measurement precision
If multiple data bundles are collected and processed to improve prediction accuracy, then fault detection accuracy is improved, but the time required for data processing increases
Solution Approach 1:
The patent divides data into segmented time-windows: a primary data bundle containing recent measurement time-series and a secondary data bundle containing historical measurement time-series. This segmentation enables the system to process data in manageable chunks, improving accuracy through comprehensive data analysis while reducing processing time through parallelizable chunked processing.
Solution Approach 2:
The system performs preliminary processing of maintenance data to determine associations with physical modules before actual fault detection is needed. Historical data bundles are processed in advance to train predictive models, so that when faults occur, the system can quickly make predictions without real-time processing of all historical data, thus improving accuracy while reducing operational processing time.
3Reliability
If predictive models are trained using comprehensive data bundles, then model performance is improved, but the computational resources required increase
Solution Approach 1:
The patent segments data into primary and secondary data bundles, where the primary bundle contains recent measurement time-series and the secondary bundle contains historical data. This segmentation allows the system to train models using manageable data portions, improving model performance through comprehensive training while reducing computational resource consumption by processing data in structured segments rather than loading all data at once.
Solution Approach 2:
The system performs preliminary training of predictive models using historical data bundles before actual deployment. By pre-processing and training models in advance with historical measurement time-series, the system can make accurate predictions during operation without requiring intensive real-time computational resources, thus improving model performance while reducing active computational energy consumption.
Data Source
AI summary
An apparatus for predictive maintenance, the apparatus comprising means for:Collecting a primary data bundle, wherein the primary data bundle comprises a first set of measurement time-series, wherein the first set of measurement time-series represents first alarms relating to a physical module of a telecommunications network,Processing maintenance data to determine whether the primary data bundle is associated with a maintenance intervention on the physical module,In response to determining that the primary data bundle is associated with a maintenance intervention, collecting a secondary data bundle, wherein the secondary data bundle comprises a second set of measurement time-series, wherein the second set of measurement time-series represents second alarms relating to the physical module,Training a predictive model using the primary data bundle and the secondary data bundle, wherein the predictive model is configured to compute an anomaly-related prediction.