Machine Learning Model for Batch Process Failure Prediction
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Solution Overview
Problem
Current computer systems lack effective methods for predicting future failures in batch processes, which are crucial for ensuring process automation efficiency and reliability, especially in complex and dynamic environments like financial services.
Innovation Solution
The implementation of a computer-implemented method that utilizes machine learning models trained with historical data to predict future failures or successes in batch processes by extracting relevant features, generating a training dataset, and integrating descriptive analytics for real-time predictions, enabling proactive measures to prevent failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional monitoring methods are used for batch processes, then system complexity is low, but the ability to predict future failures is insufficient
Solution Approach 1:
The system performs preliminary actions by training machine learning models on historical data before actual batch process execution. The models are pre-trained with features extracted from historical batch object data, incident data, and change order data to predict potential failures before they occur in real-time operations
Solution Approach 2:
The patent introduces machine learning models as intermediary components between historical data and failure prediction. These models act as mediators that process and analyze multiple data sources (batch object data, incident data, change order data) to generate predictive insights about future batch process failures
2Measurement precision
If machine learning models are trained with comprehensive historical data, then prediction accuracy improves, but data processing time increases
Solution Approach 1:
The system segments the comprehensive historical data into distinct feature sets: batch object features, incident features, change order features, and workflow features. This segmentation allows the machine learning model to process and analyze specific aspects of batch process failures independently, improving both prediction accuracy and processing efficiency
Solution Approach 2:
Feature extraction and data preprocessing are performed in advance before model training and deployment. The system pre-processes historical data to create training datasets with extracted features, reducing the computational burden during real-time prediction operations
Data Source
AI summary
Systems and methods involving provision of machine-learning-based prediction of future failure, anomaly, etc. in execution of batch processes are disclosed. In one illustrative implementation, an exemplary method may comprise obtaining historical data from prior execution of one or more batch processes, training a machine learning model to predict one or more future failure(s) and/or future flag(s) in execution of a future batch process, generating and/or collecting descriptive analytics pertinent to execution of the batch processes, and predicting a future failure and/or future flag in execution of the batch processes using the trained machine learning model and/or the descriptive analytics.