Fault Prediction Models for Industrial Process Deviations
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Solution Overview
Problem
Industrial-method plants, such as painting plants, face challenges in efficiently analyzing fault situations and predicting process deviations, leading to delays in maintenance and potential quality issues due to the complexity and slowness of these systems.
Innovation Solution
A method for fault analysis and prediction in industrial-method plants that includes automatic recognition of faults, storage of fault data, determination of fault causes, and generation of prediction models using machine learning techniques to prioritize and propose relevant process values, allowing for early detection and prevention of deviations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional fault analysis methods are used in industrial-method plants, then comprehensive fault detection is achieved, but analysis time and system complexity increase significantly
Solution Approach 1:
The system pre-establishes a fault database containing historical fault data, process values, and cause relationships before actual fault occurrences. When a fault is detected, the system quickly queries this pre-prepared database to identify potential causes and relevant process values, eliminating the need for time-consuming real-time analysis of all possible fault scenarios.
Solution Approach 2:
The system creates simplified representations of fault situations by storing fault situation data sets that capture essential characteristics of historical faults. These copied fault patterns enable rapid comparison and matching against current fault conditions, providing fast fault analysis without requiring complete reconstruction of complex causal relationships.
2Measurement precision
If comprehensive process values are analyzed for fault determination, then fault accuracy improves, but system complexity and data processing requirements increase
Solution Approach 1:
The system prioritizes and focuses analysis on specific process values that are most relevant to the detected fault situation, rather than uniformly analyzing all process values. By assigning different levels of importance to different process values based on their relationship to the fault, the system achieves accurate fault determination while reducing the complexity of data processing.
Solution Approach 2:
The system uses historical fault data sets that contain pre-identified relevant process values and their relationships. By copying and applying these established relationships from historical cases to current fault situations, the system determines which process values to analyze without requiring complex real-time evaluation of all possible parameters.
3Adaptability or versatility
If manual fault analysis procedures are used, then flexibility in handling diverse fault situations is maintained, but productivity and response time decrease
Solution Approach 1:
The system automatically performs fault analysis by querying the fault database, identifying relevant process values, and determining potential causes without requiring manual intervention. The automated system serves itself by using its own stored knowledge base to analyze faults, maintaining flexibility through the comprehensive database while dramatically improving productivity through automation.
Solution Approach 2:
The system incorporates feedback mechanisms where fault analysis results and outcomes are fed back into the fault database, continuously improving the system's knowledge base. This feedback loop enables the system to learn from past faults and improve future analysis accuracy, maintaining adaptability while operating at high speed through automated processes.
Data Source
AI summary
In order to provide a method for predicting process deviations in an industrial-method plant, for example a painting plant, by means of which process deviations are predictable simply and reliably, it is proposed according to the invention that the method should comprise the following:automatic generation of a prediction model;prediction of process deviations during operation of the industrial-method plant, using the prediction model.