Root cause diagnosis and fault propagation path identification for complex industrial processes based on data space

Fault diagnosis plays a crucial role in ensuring industrial safety and enhancing social benefits. However, identifying fault paths accurately becomes challenging due to process coupling and complex flow. This paper proposes a data space -based approach for fault diagnosis and path recognition. It involves constructing a multi -tiered, multi -causal data space by extracting causal relationships from data. An attention -based convolutional neural network efficiently captures the causal relationships. Pruning and expert knowledge contribute to an elaborate structure. The fault propagation direction is determined using LSTM-least-square linear (OLS) method, aiding rapid problem location. Experimental verification on float glass production and the Tennessee Eastman demonstrates remarkable results, supporting improved reliability and efficiency in industrial processes.