Process Fault Analysis Using Probabilistic Anomaly Models
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Solution Overview
Problem
Existing methods for fault analysis and process deviation prediction in process engineering plants, such as paint shops, are inefficient and unreliable, lacking the ability to accurately identify and predict errors and deviations in a timely manner.
Innovation Solution
A method for fault analysis and prediction of process deviations that involves linking process values to error situations using criteria like pre-links, prioritization based on relevance and position, and creating predictive models using machine learning techniques to identify and suggest causes of faults and deviations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual fault analysis methods are used in process engineering plants, then operators can identify errors, but the analysis is time-consuming and unreliable
Solution Approach 1:
The patent replaces manual mechanical analysis methods with automated computer-based systems that use machine learning models and algorithms to detect and analyze faults, eliminating the need for human operators to manually examine process data and significantly improving both speed and reliability
Solution Approach 2:
The patent introduces intermediate processing layers including data preprocessing modules, feature extraction components, and multiple levels of analysis (local, regional, global) that act as intermediaries between raw process data and final fault conclusions, enabling systematic and reliable automated fault detection
2Productivity
If all process values are analyzed equally for fault detection, then comprehensive coverage is achieved, but critical faults are not prioritized and response time increases
Solution Approach 1:
The patent applies different analysis strategies and priority levels to different process values based on their local characteristics and importance to the specific process step, allowing critical parameters to be analyzed with higher priority and more detailed methods while maintaining comprehensive coverage
Solution Approach 2:
The patent dynamically changes analysis parameters such as threshold values, window sizes, and priority weights based on the current process state and historical data, enabling the system to adapt to varying conditions and prioritize the most relevant faults at any given time
3Measurement precision
If simple fault detection methods are used, then implementation is easy, but prediction accuracy and ability to identify root causes is insufficient
Solution Approach 1:
The patent divides the fault detection system into multiple independent modules including data collection, preprocessing, feature extraction, local analysis, regional analysis, global analysis, and prediction components, allowing complex functionality to be achieved through coordinated simple modules that can be developed and maintained independently
Solution Approach 2:
The patent transitions from analyzing individual process values in isolation to examining patterns across multiple dimensions including temporal patterns, spatial relationships between process steps, and correlations among different parameters, significantly improving prediction accuracy through multi-dimensional pattern recognition
4Reliability
If historical data is extensively used for training models, then prediction reliability improves, but data processing time and computational resources increase
Solution Approach 1:
The patent extracts only the most relevant features and patterns from historical data using feature selection techniques and dimensionality reduction methods, retaining the essential information needed for reliable predictions while discarding redundant data that would consume unnecessary computational resources
Solution Approach 2:
The patent uses a hierarchical approach where simpler, faster models provide initial predictions and only more computationally intensive analyses are performed when needed, achieving sufficient reliability through partial use of available data and methods rather than exhaustive analysis of all historical information
Data Source
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AI summary
The invention relates to a method for detecting anomalies and/or faults in a technical system, for example in a painting system, wherein anomalies and/or fault situations can be easily and reliably detected using the method. According to the invention, the method has the following steps: - automatically generating an anomaly and/or fault model of the technical system, said model comprising information on the probability of the occurrence of process values; - automatically reading process values of the technical system while operating same; and - automatically detecting an anomaly and/or fault situation by ascertaining a probability of occurrence using the anomaly and/or fault model on the basis of the read process values of the technical system and by checking the probability of occurrence for a limit value.