Cross-Domain SignalsGrid substations / transformer plants / condition-based maintenance
Transformer Core Hum Online Monitoring
01
Pain Points
- !The hum is a fundamental plus a string of harmonics; loosening/bias alters the harmonic structure and modulation, and manual listening can't catch the subtle changes.
- !Substations are unattended and need 7×24 online monitoring with automatic early warning on deviation.
- !Ambient and seasonal temperature shifts interfere, so judgment must be relative to each unit's own baseline rather than an absolute threshold.
02
The Tinia Approach
The modulation spectrum characterizes the hum's harmonic structure + TNR prominence + per-unit baseline + Z-Score deviation early warning, judged against each unit's own baseline to resist environmental interference.
Frequency weightingModulation-spectrum analysisTNR prominencePer-unit baselineZ-Score early warning
03
Nodes Used
Frequency Weightingweighting_filter
Suppress irrelevant bandsModulation-Spectrum Analysismodulation_spectrum
Characterize hum harmonics / modulation structureTNRtnr
Narrowband hum prominenceBaseline Statisticsbaseline_stats
Model each unit's normal stateZ-Score Anomaly Detectionzscore_anomaly
Baseline-deviation early warningCore nodes; add or remove steps as needed for your data and standards — the node graph is always editable.
04
Expected Output
- Trend over time of the hum's harmonic structure and modulation depth.
- Deviation early warning relative to each unit's own baseline, resistant to environmental / seasonal interference.
- A 7×24 online monitoring workflow streamable via SDK.
Reference Standards / Engineering PracticeIndustry practice (transformer condition-based maintenance / acoustic monitoring)
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