System availability enhancement using computational intelligence–based decision tree predictive model
Bibliographic record
Abstract
System availability is a key performance measure in the process industry. It ensures continuous operation of facilities to meet production targets, personnel safety and environmental sustainability. Process machinery condition assessment, early fault detection and its management are vital elements to ensure overall system availability. These elements can be explored and managed effectively by extracting hidden knowledge from machinery vibration information to improve plant availability and safe operations. This article describes a decision tree–based computational intelligence model using machinery vibration data to detect machinery faults, their severity, and suggests appropriate action to avoid unscheduled failures. Vibration data for this work were collected using a machinery simulator and real-world machine to show the applicability of the proposed model. Later, the data were analyzed to detect faults using decision tree–based model that was developed in MATLAB. Fault detection classification accuracies of 98% during training and 93% during testing showed excellent performance of the proposed model. The model also revealed that the proposed formulation has capability of detecting faults correctly in the range of 98%−99%. The results showed that the proposed decision tree–based model is effective in evaluating the condition of process machinery and predicting unscheduled equipment breakdowns with better accuracy and with reduced human effort.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".