Industrial Vibration and Sound Monitoring using AWS Lookout

OneData’s industrial vibration & sound monitoring solution combines connected sensors, edge processing and cloud-based AI/ML to enable predictive maintenance of critical rotating and mechanical assets. Leveraging OneData’s expertise in IoT development, cloud architecture and machine-learning, the platform helps clients move from reactive breakdowns to proactive health-monitoring of equipment.

Architecture & AWS Integration

• At the edge, vibration sensors and acoustic sensors collect continuous telemetry from motors, bearings, compressors, turbines or HVAC units. Edge gateways running AWS IoT Greengrass preprocess the data locally — filtering, aggregation or local alerting — helping reduce latency and manage connectivity challenges.

• The filtered data streams securely into the AWS cloud where AWS Lookout for Equipment analyse time-series signals to detect early-stage anomalies, abnormal vibration patterns or unusual acoustic signatures indicative of bearing wear, imbalance, misalignment or impending failure.

• A time-series database or stream-processing architecture (e.g., Amazon Timestream, Kinesis) stores and ingests telemetry for model training, analytics and dashboards.

• OneData builds predictive maintenance models that produce risk-scores for individual assets, anticipate remaining-useful-life, trigger maintenance workflows and enable scheduling optimisation.

• Operational dashboards and alerting solutions deliver visibility: asset health, trending vibration levels, acoustic event logs, maintenance history, and recommendations.

• The full solution is integrated into asset management systems or manufacturing execution systems (MES), enabling seamless workflows: monitoring → detection → maintenance scheduling → resolution.

Capabilities & Features

• Continuous vibration & acoustic monitoring of mechanical assets with edge-IoT connectivity

• Local edge analytics via IoT Greengrass to enable rapid on-site detection and cloud integration for deeper learning

• Anomaly-detection and predictive-failure modelling using Lookout for Equipment or SageMaker

• Asset health dashboards, alerts and root-cause insights for maintenance teams

• Use-case adaptability: motors, pumps, compressors, wind-turbines, HVAC units, production lines

• Integration with maintenance management systems (CMMS/MES) for workflow automation

• Scalable architecture: from individual factory lines to fleet-wide deployment across multiple sites

• Helps enable safety assurance, reduced downtime, longer asset life, and optimised maintenance cost

Use Cases

• A manufacturing plant monitoring critical bearing health in high-speed motors to avoid catastrophic failure and production stoppage

• A wind-farm operator tracking vibration & sound profiles on turbines to schedule maintenance before faults escalate

• A compressor station in a petrochemical plant detecting early acoustic signature shifts to avoid unscheduled shutdowns

• HVAC system monitoring in large buildings or campuses, detecting anomalies in fans or blowers to reduce energy usage and downtime

Business Impact

By partnering with OneData, organisations gain a full‐stack solution that utilises AWS’s robust IoT and ML services, enabling them to shift from reactive maintenance to predictive operations. They reduce unplanned downtime, minimise maintenance-costs, enhance safety and extend asset life. With OneData’s cloud-native approach, deployments are secure, scalable and engineered for industrial environments, delivering measurable ROI through improved uptime, operational efficiency and reliability.