Predictive maintenance

Status

Competitive, not dominant. robustcov Auto(StudentTScatter) gives the best F1 at the fixed detection budget, while IsolationForest has the strongest PR-AUC and is faster. This is a good example of honest reporting: robustcov is useful, but it is not the only strong method for this dataset.

Problem

Predictive-maintenance data usually contain sensor or process measurements and a binary failure indicator. The goal is to rank observations or operating states by abnormality so that likely failures are prioritized for inspection.

Result table

Predictive-maintenance external result

Method	Seconds	F1	ROC-AUC	PR-AUC
sklearn IsolationForest	0.2076	0.9440	0.9872	0.9628
robustcov Auto(StudentTScatter)	0.7577	0.9469	0.9846	0.8199
sklearn EllipticEnvelope	0.1666	0.5103	0.9531	0.5805
sklearn LocalOutlierFactor	0.1379	0.1209	0.4843	0.0570

Output from the run

predictive maintenance benchmark
method,seconds,precision,recall,f1,roc_auc,pr_auc,detected
sklearn IsolationForest,0.2076,0.9440,0.9440,0.9440,0.9872,0.9628,339
robustcov Auto(StudentTScatter),0.7577,0.9469,0.9469,0.9469,0.9846,0.8199,339
sklearn EllipticEnvelope,0.1666,0.5103,0.5103,0.5103,0.9531,0.5805,339
sklearn LocalOutlierFactor,0.1379,0.1209,0.1209,0.1209,0.4843,0.0570,339
saved outputs to results/external/predictive_maintenance

Plots

Interpretation

At the selected detection budget, robustcov slightly improves F1 over IsolationForest. However, IsolationForest is faster and has substantially higher PR-AUC. The most honest recommendation is therefore:

• use robustcov when robust-distance interpretability or covariance-shaped sensor deviations are important;

• use IsolationForest as a strong default baseline;

• consider robustcov scores as additional features in a supervised maintenance model rather than as the only detector.