Leveragingwideband MEMS sensors (20Hz–80kHz)anddeep learning voiceprint models , the system provides 24/7 non-contact monitoring forDC bias, winding deformation, OLTC binding, partial discharge (PD), and cooler anomalies . By analyzingSound Pressure Level (SPL) deviation, dominant frequency shift, odd/even harmonic ratio, and LZC spectral complexity , it isolates fault signatures from corona interference and ambient noise—delivering anew dimension of quantifiable asset health data .

Addressing typical failure modes such asgearbox tooth wear, bearing pitting, generator misalignment, and PWM harmonic stress in converters , the system employsbone-conductive sensorsto capture structure-borne vibration voiceprints andMEMS air-coupled sensorsto detect ultrasonic emissions from electrical stress. By analyzingspectral energy distribution, sideband modulation patterns, and harmonic distortion trends , it identifies early-stage mechanical looseness and insulation degradation—oftenbefore tower vibration sensors trigger alarms —providing a wider window forpredictive maintenance .

In PV plants,high-frequency PWM switching harmonicsfrom inverters andpartial discharge or moisture ingressin box transformers are two primary failure risks. The system deploysnon-contact MEMS acoustic sensorsto capture wideband signals from 20Hz to 80kHz, combined withdeep learning noise reductionto filter wind, rain, and bird noise. By analyzingharmonic energy ratio, odd/even harmonic proportion, and ultrasonic energy bursts , it accurately detects inverter capacitor aging, winding looseness, and insulation tracking—significantly reducing manual inspection burdens and unplanned downtime at remote sites.

Industrial rotating equipment failures often originate frombearing pitting, gear tooth breakage, coupling misalignment, and dynamic imbalance . The system employsbone-conductive contact sensorsto directly couple surface vibration voiceprints, complemented byMEMS air-coupled sensorsto detect lubrication splash anomalies and airflow turbulence. Throughsideband modulation analysis, kurtosis trend tracking, and characteristic frequency energy ratios , it distinguishes normal wear from incipient faults—providing a clear decision window forcondition-based maintenanceand avoiding unplanned downtime.