Predictive Maintenance Modeling with Dynamic Time Warping
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Solution Overview
Problem
Current systems lack an effective method to predict maintenance events in complex machinery, relying on manual analysis and lacking real-time data integration from various sources, which can lead to delayed detection and increased maintenance costs.
Innovation Solution
A method and system utilizing processors to analyze historic maintenance data, identify precursor signals, and monitor future data from multiple resources to predict maintenance events by comparing sensor logs and maintenance logs through dynamic time warping, enabling proactive maintenance planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis methods are used for maintenance prediction, then system complexity is reduced, but detection precision and prediction accuracy deteriorate
Solution Approach 1:
The patent replaces manual analysis methods with automated computational algorithms including dynamic time warping and clustering techniques. The system uses processors to automatically analyze sensor data, identify precursor signals, and predict maintenance events, substituting human manual inspection with machine-based analytical systems that provide higher precision detection.
Solution Approach 2:
The patent introduces intermediate processing layers including data normalization modules, feature extraction algorithms, and pattern recognition systems that mediate between raw sensor data and final predictions. These intermediary computational components enable complex analysis while managing system complexity through modular architecture.
2Reliability
If real-time data integration from multiple sources is implemented, then prediction accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary data processing steps including normalization, filtering, and feature extraction that are performed on incoming data streams before main analysis. Historical maintenance data is pre-processed and stored in structured formats, enabling faster real-time prediction without sacrificing accuracy when new sensor data arrives.
Solution Approach 2:
The patent divides the data processing workflow into distinct modular segments: data collection from multiple sensors, data normalization, precursor signal identification, pattern matching, and prediction generation. This segmentation allows parallel processing of different data streams and reduces overall processing time while maintaining comprehensive analysis.
3Reliability
If comprehensive historic maintenance data is analyzed, then prediction reliability is improved, but data storage requirements and analysis complexity increase
Solution Approach 1:
The patent extracts and stores only the most relevant features and precursor signals from comprehensive historical maintenance data rather than retaining all raw data. The system identifies and preserves key patterns, anomaly types, and predictive indicators while discarding redundant information, reducing storage requirements while maintaining prediction reliability.
Solution Approach 2:
The patent transforms raw historical maintenance data into normalized parameters and standardized formats that reduce storage requirements. By converting diverse data types from multiple sensors into unified dimensionalless parameters and applying dimensionality reduction techniques, the system maintains comprehensive analysis capability with reduced data volume.
Data Source
AI summary
Systems, methods, and non-transitory computer-readable media can be configured to perform receiving a notification of a maintenance event associated with a resource. The method includes retrieving historic maintenance data in relation to the resource with which the fault is associated, the maintenance information originating from a time period preceding the time of the maintenance event. The method includes identifying at least a portion of the retrieved historic maintenance data as being indicative of the maintenance event. The method also includes causing the portion of the retrieved historic maintenance data identified as being indicative of the maintenance event to be stored as a precursor signal of the maintenance event. The method also includes causing future maintenance data received from a plurality of resources related to the resource with which the maintenance event is associated to be monitored to predict a future occurrence of the maintenance event in the plurality of resources.