2026-10-14T16:27:10Z https://www.scidb.cn/oai

10.57760/sciencedb.012os 2026-09-16T17:01:21Z

2026-09-16 Code and data for "Leakage-Aware Evaluation and Real-Time Feasibility of Machine Learning for Condition Monitoring of Servo-Driven Machinery" doi:10.57760/sciencedb.012os en This archive contains the complete analysis pipeline and the simulated dataset for the paper "Leakage-Aware Evaluation and Real-Time Feasibility of Machine Learning for Condition Monitoring of Servo-Driven Machinery". Running it reproduces every number, table and figure in the paper from the raw data. Contents - code/ — the full pipeline: 23-dimensional feature extraction; five partitioning protocols (random segment split, random segment cross-validation, record-grouped cross-validation, leave-one-load-out, leave-one-fault-size-out); leakage quantification in two window-overlap configurations; the leakage-free benchmark with confidence intervals; hyper-parameter tuning performed strictly inside the training folds; a capacity sweep over three deep architectures; per-segment latency measurement; cross-domain transfer; and paired significance tests (Nadeau-Bengio corrected resampled paired t-test with a repeat-level Wilcoxon check, Holm-corrected within two families). - code/figures/make_figures.py — regenerates all ten figures from the result files. It never retrains a model, so the figures cannot drift from the tables. - data/simulink/ — the simulated servo drive-current dataset: 120 independent randomised simulation runs across four conditions. servo_runs_meta.csv records the random seed and the full parameter vector of every run, so the dataset is exactly reproducible. - tests/ — four self-checks: the experimental design, numeric traceability of the manuscript, citation consistency, and figure/table ordering. - refs/, requirements.txt, run_all.py, README.md. The CWRU vibration data are not redistributed here; they are publicly available from the Case Western Reserve University Bearing Data Center, and code/data/download_cwru.py fetches the mirror used in the paper. Two details, documented in the README, change results materially: the grouping key must be the recording identifier rather than the segment, and grouped folds must be class-stratified at the group level. scikit-learn's GroupKFold does not stratify and, on CWRU, produces a fold containing no inner-race recordings at all. data leakage; evaluation protocol; condition monitoring; fault diagnosis; machine learning; servo drive 梅凯 彭国超 PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset Science Data Bank