Event Prediction from Observed Sequences for Predictive Maintenance
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Solution Overview
Problem
Existing predictive maintenance methods struggle to accurately and reliably predict future system failures in manufacturing plants and other real-world systems, often due to inadequate leveraging of historical data.
Innovation Solution
A method is disclosed for training a machine learning model to predict future events in a system by receiving event data, determining positive and negative training samples, and training the model to predict the occurrence of specific events within a threshold time frame based on sequences of past events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing predictive maintenance methods are used, then system monitoring is performed, but prediction accuracy and reliability are insufficient
Solution Approach 1:
The system performs preliminary actions by collecting and storing historical event data before failures occur, creating training samples that enable the machine learning model to learn patterns and predict future failures with higher accuracy and reliability
Solution Approach 2:
The system uses feedback mechanisms where predicted failures are validated against actual system behavior, allowing the machine learning model to continuously improve its prediction accuracy and reliability through iterative training on new data
2Reliability
If historical data is not adequately leveraged, then simple monitoring is used, but prediction reliability is poor
Solution Approach 1:
The system segments historical data into distinct training samples (positive and negative) based on whether they precede failures or not, allowing the machine learning model to process and learn from structured data in a manageable way that improves reliability without overwhelming complexity
Solution Approach 2:
The machine learning model acts as an intermediary between raw historical data and prediction outputs, automatically processing and interpreting complex data patterns to generate reliable predictions without requiring manual analysis of the entire historical dataset
3Productivity
If machines operate continuously, then productivity is maintained, but failure risks increase
Solution Approach 1:
The system takes preliminary action by predicting potential failures before they occur, allowing operators to perform maintenance activities in advance that prevent failures while maintaining continuous operation and productivity
Solution Approach 2:
The system applies preliminary anti-action by identifying and countering the conditions that lead to failures through predictive analysis, preventing harmful factors from manifesting while the system remains operational
Data Source
AI summary
An event analysis and prediction system is disclosed that is configured to analyze at least one event data stream from a monitored system for the purpose of predicting future events in the system, e.g., for predictive maintenance of the system. The event analysis and prediction system advantageously predicts, in real-time, such failures in the monitored system that would otherwise lead to interruptions. The event analysis and prediction system advantageously leverages a novel data generation procedure that, in essence, converts the problem of failure prediction to a classification problem, which is solved using machine learning algorithms. The event analysis and prediction system is designed to work well even when there is limited labeled data available for model training.