Industrial Equipment Condition Detection Using ML Weight Matrices
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Solution Overview
Problem
Current methods for detecting faults in industrial process equipment require significant expert effort to generate accurate weight matrices, leading to potential human errors and inefficiencies in identifying equipment conditions, which can result in inadequate preventive maintenance.
Innovation Solution
An automated system utilizing machine learning techniques, including supervised and unsupervised learning, to generate weight matrices based on historic parameter values and real-time data, enabling the detection of equipment conditions without relying on domain expertise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expert analysis is used to generate weight matrices for fault detection, then detection accuracy can be achieved, but significant expert effort and time are required
Solution Approach 1:
The system enables self-service by automatically generating weight matrices using unsupervised learning algorithms that analyze historical parameter data without requiring expert intervention. The algorithm autonomously identifies patterns and relationships in the data, replacing the need for expert analysis while maintaining detection accuracy.
Solution Approach 2:
The patent substitutes the mechanical process of expert analysis with an automated computational system. Machine learning algorithms process historical data and generate weight matrices through computational operations, replacing the manual cognitive work of experts with automated information processing.
2Measurement precision
If manual weight matrix configuration is performed for each process equipment, then accurate fault detection is possible, but the process becomes complex and error-prone
Solution Approach 1:
The system achieves universality by creating a single automated framework that can generate weight matrices for multiple types of process equipment (turbines, pumps, motors, fans, compressors, boilers, heat exchangers) using the same unsupervised learning algorithm, eliminating the need for separate manual configuration processes for each equipment type.
Solution Approach 2:
The automated system performs self-configuration by automatically analyzing historical data and generating appropriate weight matrices for each equipment type based on its specific parameters and fault patterns, removing the burden of manual configuration from operators.
3Reliability
If expert-generated weight matrices are used, then fault detection can be performed, but human errors cannot be completely ruled out
Solution Approach 1:
The patent replaces the human expert system with an automated machine learning system that processes data through consistent algorithmic operations. This substitution eliminates human cognitive errors, fatigue, and variability, providing more reliable and consistent fault detection across different operators and time periods.
Solution Approach 2:
The system incorporates feedback mechanisms where detection results and historical data continuously refine the weight matrices through iterative learning processes. This feedback loop allows the system to self-correct and improve over time, reducing errors and enhancing reliability through continuous optimization based on actual performance data.
Data Source
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AI summary
The present disclosure discloses method and control system for detecting condition of plurality of process equipment in industrial plant. The proposed methodology implements machine learning techniques for detecting the fault. The machine learning techniques include supervised learning and unsupervised learning. Real-time values of plurality of parameters associated with each of the plurality of process equipment along with weight matrix and threshold attribute is used in the unsupervised learning to detect the condition. The weight matrix associated with each of the plurality of process equipment is generated using the supervised learning. Plurality of historic values of the plurality of parameters relating to the corresponding process equipment are analysed in the supervised learning to generate the weight matrix for the corresponding process equipment. The threshold attribute associated with each of the plurality of parameters for the corresponding process equipment is determined using the real-time values of said plurality of parameters.