A variable mosquito flying optimization‐based hybrid artificial neural network model for the alarm tuning of process fault detection systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Chemical process systems are becoming extremely complex due to increased automation, heat and mass intensification, and expectation of higher efficiency. Many fault detection and diagnostic methods have been proposed for processing facilities. However, managing the missed alarm rate and the false alarm rate (FAR) in the detection and isolation of the fault is crucial in the complex process systems. This work presents a new data‐driven fault detection model using an artificial neural network (ANN) and variable mosquito flying optimization (V‐MFO) technique. The model is based on the optimization of the number of neurons in the hidden layer of the neural network. Subsequently, the model parameters have been tuned using the V‐MFO algorithm for maximizing the fault detection rate (FDR) while minimizing the FAR. The proposed fault detection method has been implemented on the Tennessee Eastman benchmark process. The performance of the proposed model has been evaluated in terms of accuracy, FDR and FAR against well‐known statistical‐based methods such as principal component analysis (PCA), kernel PCA, semiparametric PCA, modified independent component analysis, k nearest neighbors, linear discriminant analysis, support vector machine, and the ANN. The model is observed to be competitive for fault detection among the test algorithms. It recorded slightly improved accuracy and FDR. The proposed model also resulted in 0.6% improvement in the FAR and 8% improvement in missed detection rate compared to the simple ANN. This method provides an efficient fault detection tool for complex process systems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it