Adaptive Regularization in ANN for Condition Monitoring and Fault Detection in Heavy-duty Hydraulic Machines
Bibliographic record
Abstract
This paper presents an extended architecture of an artificial neural network (ANN) to enhance the detection of internal leakage in a single-rod energy-efficient electro-hydrostatic actuator (ERA). The goal is to monitor and identify the level of internal leakage in this type of actuator. A novel scheme based on a 3-layer ANN algorithm is proposed that can detect actuator leakage faults at various levels - low, medium, and high. The algorithm aims to maximize the efficacy of fault detection using an adaptive regularization technique. Experimental results show that the new algorithm detects internal leakage as low as 0.5 L/min with over 90% accuracy. It can also recognize the severity of fault level (low, medium or severe) with over 80% confidence. Cognitive informatics drives the algorithms used in this scheme through data acquisition, pertinent data selection, training, classification, decision-making, and performance enhancements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".