  This study introduces a novel methodology for fault detection and cause
identification within the Tennessee Eastman Process (TEP) by integrating a
Bidirectional Long Short-Term Memory (BiLSTM) neural network with an Integrated
Attention Mechanism (IAM). The IAM combines the strengths of scaled dot product
attention, residual attention, and dynamic attention to capture intricate
patterns and dependencies crucial for TEP fault detection. Initially, the
attention mechanism extracts important features from the input data, enhancing
the model's interpretability and relevance. The BiLSTM network processes these
features bidirectionally to capture long-range dependencies, and the IAM
further refines the output, leading to improved fault detection results.
Simulation results demonstrate the efficacy of this approach, showcasing
superior performance in accuracy, false alarm rate, and misclassification rate
compared to existing methods. This methodology provides a robust and
interpretable solution for fault detection and diagnosis in the TEP,
highlighting its potential for industrial applications.
