A fault isolation strategy for industrial processes using outlier-degree-based variable contributions
Bibliographic record
Abstract
In industrial process monitoring, it is always a challenging and practical problem to analyze the causes of the system fault by isolating true fault variables from vast amounts of process data. However, the phenomenon of smearing effect occurs by using the traditional contribution analysis-based isolation methods since the defined isolation indices of different variables affect each other. In this paper, a new fault isolation method is proposed based on local outlier factor and improved k-nearest neighbor rule aiming to improve the isolation accuracy. Firstly, the nearest neighbors of each sample are obtained along the direction of a specific variable. Based on the nearest neighbors, the outlier-degree value of the variable is calculated and regarded as the contribution of the variable. Then, the contribution of the variable in all samples are obtained in the same way, among which the maximum one is selected as the isolation threshold value of this variable. During the online monitoring, the contribution of the variable in the newly collected sample is calculated in real time. Once the contribution is greater than the threshold, the variable is judged to be the dominant factor causing the system fault. Two cases on numerical example and Tennessee Eastman process are conducted to evaluate the effectiveness of the proposed method. • A fault isolation strategy is proposed where the contribution is defined by the distance between variables. • The variable contribution is obtained by calculating the outlier-degree relative to the k-nearest neighbors of variable. • Theoretical analysis of the isolability for the fault variables is given.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".