Interview: 'design anomaly detection for a factory sensor with 200 signals.'
hardAnswer
- (1) EDA: distribution + autocorrelation per signal; note skew / seasonality.
- (2) Feature engineering: rolling stats + FFT / wavelet features + inter-sensor correlations.
- (3) Ensemble: (a) univariate rolling z-score / STL, (b) Isolation Forest on features, (c) multivariate autoencoder on signal windows.
- (4) Score → alerts with severity levels.
- (5) HITL: operator confirms anomalies → label + retrain.
- (6) Explainability: feature contribution per alert.
- (7) Monitor: false alert rate + MTTR.
Check yourself — multiple choice
- Random
- EDA + rolling/FFT features + ensemble (rolling z-score + iForest + AE on windows) + HITL retrain + explainability + FAR/MTTR monitoring
- Just threshold
- Not real
Factory AD: EDA + rolling/FFT + iForest + AE + HITL + explainability + FAR/MTTR.
#interview#anomaly-detection#applications
Practise Unsupervised Learning
214 interview questions in this topic.
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