AuresSound provides sound-based condition monitoring to deliver real-time acoustical and technical data about machinery processes. This continuous information helps operations reduce unplanned stoppages, optimize process cycles, and improve overall equipment effectiveness (OEE). The system processes data instantly, issues specific alarms, and stores historical data in the cloud to support energy efficiency and quality control.
Funding
Funding not disclosed
Founders
Product
Problem
Manufacturing and process facilities often rely on periodic manual inspections or legacy vibration sensors, which can miss early signs of equipment degradation. This leads to unplanned stoppages, reduced Overall Equipment Effectiveness (OEE), and higher energy consumption. The lack of continuous, high‑resolution condition data hampers proactive maintenance planning.
Solution
AuresSound delivers continuous acoustic‑based condition monitoring by attaching rugged microphones to critical machinery. The system captures high‑frequency sound signatures and processes them on the edge using digital signal processing (DSP) algorithms to extract health indicators in real time. Extracted metrics are streamed to a cloud platform where they are correlated with equipment models and visualized through a web dashboard. Automated alarms pinpoint the source and severity of anomalies, enabling operators to intervene before a failure occurs. Historical acoustic datasets are stored securely for trend analysis, supporting root‑cause investigations and energy‑efficiency reporting. By integrating with existing SCADA or MES interfaces via RESTful APIs, AuresSound fits into established operational workflows without extensive retrofitting.
Target Audience
The primary customers are mid‑to‑large scale manufacturers, process plants, and utility operators that require continuous equipment health insight to maintain production uptime and meet sustainability targets.
Features
- Industrial‑grade acoustic sensor arrays with built‑in pre‑amplification for high‑SNR capture on rotating and stationary equipment
- Edge‑deployed DSP pipeline that performs real‑time spectral analysis, feature extraction, and anomaly scoring locally
- Cloud‑native data lake that archives raw waveforms and derived metrics with role‑based access controls
- Configurable alarm engine with multi‑level thresholds, location tagging, and push notifications to mobile or control‑room consoles
- REST and OPC‑UA APIs for seamless integration with SCADA, MES, and enterprise asset management (EAM) systems
- Predictive maintenance models that leverage machine‑learning classifiers trained on historical acoustic patterns to forecast component wear
- Energy‑efficiency analytics that correlate acoustic health scores with power consumption trends to identify optimization opportunities