Regression Model Builder for Equipment Failure Prediction
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Solution Overview
Problem
Current manufacturing and plant systems lack effective methods for gathering and analyzing data to predict equipment failures, leading to potential damage and safety risks due to unforeseen machinery malfunctions.
Innovation Solution
A data historian system that uses sensor data and a matrix model builder application to create regression models, allowing users to select time spans and compare new data against expected output tags to determine if it falls within a normal operating range, thereby predicting potential failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional monitoring devices are used to track equipment status, then basic operational data can be collected, but the system cannot effectively predict equipment failures or detect deviations from normal operation
Solution Approach 1:
The system performs preliminary actions by collecting and storing historical operational data in advance using the data historian. This pre-collected data is then used to train machine learning models that can predict future equipment failures, transforming raw historical data into predictive insights before actual failures occur.
Solution Approach 2:
The patent introduces machine learning models as intermediary components between raw sensor data and failure prediction outcomes. These models act as mediators that process historical data, identify patterns, and generate predictions about equipment status, bridging the gap between data collection and actionable insights.
2Measurement precision
If more data is collected from equipment to improve prediction accuracy, then failure detection capability improves, but system complexity and data processing requirements increase
Solution Approach 1:
The patent segments the data processing system into distinct functional modules: a data historian for storing historical data, machine learning models for pattern recognition, and a user interface for displaying predictions. This segmentation allows each component to handle specific tasks efficiently, reducing overall system complexity while maintaining high detection accuracy.
Solution Approach 2:
The machine learning models perform self-service by automatically learning from historical data and improving their prediction capabilities without requiring manual intervention. The system trains models using past equipment data, enabling them to autonomously identify failure patterns and apply this knowledge to future predictions.
3Object-affected harmful factors
If real-time monitoring is implemented to detect equipment deviations, then operator safety improves, but response time for data analysis may be insufficient
Solution Approach 1:
The system performs preliminary action by pre-training machine learning models using historical data before real-time monitoring is needed. This advance preparation enables the models to quickly analyze real-time sensor data and generate immediate predictions about equipment status, reducing analysis time while maintaining safety.
Solution Approach 2:
The patent replaces manual or traditional mechanical data analysis methods with machine learning-based computational analysis. This substitution enables rapid processing of real-time sensor data, providing immediate predictions about equipment failures and allowing operators to respond quickly to potential safety issues.
Data Source
AI summary
A system and method of monitoring equipment performance and predicting failures. The system can include a data historian that stores data for a piece of equipment and designates the data to tags. The tags can correspond to sensors that gather the data from the piece of equipment. A matrix model builder application can allow a user to generate regression models for various time spans to determine whether new data is within a normal operating range.