Process fault isolation based on transfer entropy algorithm.

Area of Science:

• Industrial Process Control
• Data-driven Fault Diagnosis
• Information Theory Applications

Background:

• Industrial plants face increasing complexity and strict regulations, demanding efficient fault detection and isolation.
• Current fault isolation methods are broadly classified into model-based and data-based approaches.
• Transfer entropy is a data-based technique used to determine disturbance propagation direction and identify root causes.

Purpose of the Study:

• To propose a novel transfer entropy-based method for isolating process faults in industrial plants.
• To leverage transfer entropy for generating distinct information flow patterns indicative of specific faults.
• To enhance the accuracy and reliability of fault isolation in complex industrial systems.

Main Methods:

• Utilizing transfer entropy to analyze information flow dynamics among process variables.
• Developing a method to recognize correlations within transferred information during abnormal conditions.
• Generating unique information flow signatures for different fault types.

Main Results:

• The proposed method successfully generates distinct patterns of information flow for various process faults.
• Experimental validation demonstrates the method's capability to accurately correlate information transfer with fault conditions.
• The novel approach shows superior performance compared to conventional fault isolation techniques.

Conclusions:

• The developed transfer entropy-based method offers a powerful new tool for industrial process fault isolation.
• This approach provides a data-driven means to understand and diagnose faults by analyzing inter-variable information exchange.
• The findings highlight the potential of transfer entropy in improving the safety and efficiency of industrial operations.