What is an Acoustic Fingerprint?

An acoustic fingerprint is a unique digital signature derived from the sound frequency characteristics emitted by machinery, equipment, or environmental events. Much like a human fingerprint, every mechanical state and operational anomaly possesses a distinct acoustic profile.

Industrial Acoustic Fingerprint Solutions

Hertzinno is the world’s first provider of all-scenario acoustic fingerprint solutions. By integrating state-of-the-art deep learning algorithms with advanced sound recognition technology, we enable intelligent monitoring across diverse sectors including power grid operations, oil & gas leak detection, manufacturing quality control (QC), and pipeline integrity. We transform complex industrial noise into precise, actionable insights to ensure operational safety and efficiency.

HZ-FA-110 2-Channel Acoustic Fingerprint Analysis
HZ-FA-110

Dual-Channel Acoustic Analysis System

An elite IIoT device designed for real-time sound feature extraction and fingerprint analysis, surpassing traditional decibel-based monitoring for critical industrial assets.

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HZ-FA-8 8-Channel Acoustic Analysis
HZ-FA-8

8-Channel Synchronous Edge Device

High-speed, multi-point synchronous sampling for complex machinery, providing comprehensive acoustic data acquisition and edge computing capabilities.

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HZ-FA-QC Product Quality Inspection System
HZ-FA-QC

Acoustic QC Inspection System

Automated quality control utilizing acoustic fingerprinting to capture audible and ultrasonic signals, creating a unique digital identity for every product.

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HZ-PS-20A Pipeline Security Sentinel
HZ-PS-20A

Pipeline Security Sentinel

AI-powered acoustic monitoring for large-scale pipeline networks, specialized in real-time leak detection and structural anomaly analysis.

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Industrial Applications

From UHV substations to distributed PV arrays, HERTZINNO turns operational sound into quantifiable, actionable digital assets.

Smart Grid | Transformers · GIS · Switchgear

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 .

Wind Turbine Monitoring | Drivetrain · Converter · Generator

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 .

Solar PV Monitoring | Inverter · Box Transformer · DC Side

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.

Rotating Equipment Monitoring | Motors · Vacuum Pumps · Centrifuges · Blowers

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.

Trusted by Industry Giants

From automotive leaders to energy pioneers, HERTZINNO's technology is deployed across diverse sectors, ensuring the operational safety of critical assets for our global partners.

Partner
Partner
Toyota
Daikin
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