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Experimental Design & Analysis: Lead the Design of Experiments (DoE), defining acquisition parameters, sensor requirements, sampling frequencies, and test methodologies to guarantee repeatability and statistical reliability.
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Signal Validation: Perform immediate signal analysis (time & frequency domain) to verify data quality before it enters the AI pipeline, ensuring physical phenomena are correctly captured.
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Instrumentation Setup: Select, install, and validate industrial and scientific sensors (vibration, temperature, pressure, torque, strain, flow), ensuring proper mounting, calibration, grounding, shielding, and overall signal integrity.
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DAQ Operation: Configure and operate high-fidelity DAQ systems (e.g., HBK/HBM, Dewesoft, Siemens, NI), managing channel setups, filtering, synchronization, and triggering for complex test campaigns.
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Automation Scripting: Develop and maintain Python-based tools specifically for hardware automation and signal processing (using libraries like Scipy/Numpy) to streamline data ingestion and validation.
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System Integration: Work closely with mechanical and automation teams to ensure test benches are properly instrumented, electrically integrated, and safe for operation.
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Documentation: Ensure traceability and comprehensive documentation of test configurations, wiring diagrams, sensor setups, DAQ settings, and calibration routines.
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Troubleshooting: Diagnose and resolve issues related to electrical noise, grounding loops, synchronization failures, connector faults, or hardware limitations.
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Continuous Improvement: Optimize acquisition workflows, tooling, and laboratory best practices to increase testing throughput and data reliability.