DAQ Card for Predictive Maintenance
Case Details
Client: Confidential Predictive Maintenance Service Provider
Industry: Wind Energy & Industrial Motor Monitoring / Predictive Maintenance
Services Delivered: Hardware Design • Firmware & Embedded Software Development • Multi-Channel Data Acquisition • Cloud Connectivity Integration • Signal Conditioning & Analytics Enablement
Technologies Used: ARM Cortex-A53 Quad-Core Processor • Yocto Linux OS • Multi-Channel High-Speed DAQ (16 channels, 100 KSPS) • IEPE Sensor Interface • AC/DC Voltage & Current Measurement • Ethernet & Wi-Fi Connectivity • Event-Triggered & Scheduled Data Logging
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1. Client Overview
Our client is a predictive maintenance service provider specializing in the health monitoring of wind turbines and industrial motor-driven systems. Operating across multiple wind farm sites and industrial facilities, they offer condition monitoring as a service to asset owners — continuously tracking the health of rotating machinery to detect faults early and prevent costly unplanned failures. To scale their service offering and improve monitoring accuracy, they approached Aartronix to develop a custom, high-speed data acquisition card capable of simultaneously capturing signals from multiple sensor types, with seamless cloud connectivity for remote analytics.
2. Challenge / Problem
Wind farm operators and industrial motor users face serious operational and financial consequences when equipment health goes unmonitored:
- Unpredictable and costly downtime — without continuous monitoring, failures in critical components such as bearings, gearboxes, and motor windings occur without warning, bringing operations to an unplanned halt and resulting in significant revenue loss, particularly in wind energy where turbine availability directly impacts power generation output.
- No early maintenance alerts — traditional time-based maintenance schedules are blind to the actual condition of equipment. Faults that develop between scheduled inspection intervals go undetected until they escalate into catastrophic failures requiring expensive emergency repairs.
- Reactive rather than planned maintenance — without real-time health data, maintenance teams are forced to respond to breakdowns after they happen rather than addressing issues proactively, driving up repair costs, spare parts expenditure, and technician call-out expenses.
- Limited visibility across remote sites — wind farms are typically located in geographically remote or offshore locations. Manual inspection of every turbine is time-consuming, expensive, and impractical at the frequency needed to reliably catch developing faults.
- Shortened equipment lifespan — operating machinery without insight into its actual condition means minor issues such as misalignment, imbalance, or early-stage bearing wear are left unaddressed, accelerating component degradation and reducing the overall service life of expensive assets.
- High operational risk — undetected electrical faults such as voltage imbalances, current anomalies, and winding insulation degradation in motors and generators can escalate into safety incidents or irreversible equipment damage if not identified early.
The client needed a single, ruggedized, and versatile DAQ hardware platform that could meet the signal fidelity, connectivity, and maintainability demands of their growing predictive maintenance service.
3. Solution
Aartronix designed and developed a custom Multi-Channel DAQ Card built on an ARM Cortex-A53 quad-core processor running a Yocto-based Linux OS, providing the processing capability and long-term software maintainability required for a deployed service platform.
High-Speed Simultaneous Sampling: The card features 16 analog input channels sampled simultaneously at up to 100 KSPS per channel — preserving precise signal timing relationships essential for accurate fault detection in multi-channel vibration analysis and electrical signature analysis of motors and turbine generators.
Versatile Signal Conditioning: A single card supports the full range of sensor and signal types encountered across wind farm and motor monitoring applications:
- IEPE sensor interface — for accelerometers used in bearing, gearbox, and drivetrain vibration monitoring.
- AC/DC voltage input — configurable up to 1000 V for direct monitoring of generator and grid-side power quality.
- AC/DC current measurement — direct up to 1 A, and via current transformer (CT) up to 5000 A for high-power generator and motor current signature analysis.
- 0–10 V AC/DC input — for process-level analog signals from auxiliary sensors and transducers.
Cloud Connectivity: Onboard Ethernet and Wi-Fi enable continuous, automated data transfer to the client’s cloud analytics platform — critical for wind farm sites where physical access is infrequent and remote monitoring is the primary mode of operation.
Flexible Data Capture: Scheduled logging ensures continuous baseline data collection, while event-triggered capture activates high-resolution recording when signal thresholds are breached — allowing the client to capture the precise moment a fault signature appears without storing unnecessary data.
4. Results & Impact
The deployment of the Aartronix DAQ card enabled the client to enhance the quality and reach of their predictive maintenance service across wind farm and industrial motor installations:
Outcome | Impact |
Accurate Fault Detection | Simultaneous 16-channel sampling at 100 KSPS enabled precise vibration and electrical signature analysis, allowing early identification of bearing wear, gearbox faults, and winding degradation in turbines and motors. |
Unified Sensor Platform | A single DAQ card replaced the need for multiple disparate sensing devices, supporting IEPE, high-voltage, high-current, and low-voltage inputs across all monitored asset types. |
Remote Site Monitoring | Built-in Wi-Fi and Ethernet eliminated the need for manual data collection at wind farm sites, enabling fully remote, continuous monitoring from the client’s central analytics platform. |
Scalable Service Deployment | The standardised, maintainable Yocto Linux platform allowed consistent deployment across multiple client sites with minimal site-specific engineering effort. |
Power Quality Insights | Continuous monitoring of voltage, current, and harmonic distortion on generator outputs provided additional value-added insights on grid-side power quality alongside mechanical health data. |
