Systems that make experimental hardware observable, diagnosable, controllable and usable by operators.
The CCM monitoring design I presented used digitizer boards and DAQ for PMT signals, fast/nearline processing, a MySQL database, and a Python Dash/Plotly website for online visualization.
Detector waveforms acquired through digitizer boards and DAQ.
Pulse finding and derived detector quantities generated from the acquired data.
Selected values stored and presented as online plots for users and shifters.
Click to inspect the architecture slide full-size.
Schneider M340 systems, analog/digital I/O, structured control logic, HMI interfaces, manual/automatic operation, timers and interlocks.
Pressure, temperature, flow, valve-position and other process measurements; 4–20 mA and 0–10 V signals; scaling and calibration workflows.
RS-232 and Modbus TCP integration, device polling, logging and offline control-network configuration.
Arduino-class systems and SSR/relay interfaces used for experimental prototypes and first-pass controls.
Waveform acquisition, nearline analysis, Python logging, database integration and diagnostic trending.
Using measured system state to verify prerequisites, isolate faults, confirm interlocks and document actual equipment behavior.
For CCM/SBND laser work, the system supported remote GUI control and external triggering. I also documented and worked with a fully interlocked protective enclosure in which opening the lid interrupted laser operation.
Laser monitoring / control GUI.
I value enough instrumentation and state feedback to distinguish an actual process event from a bad sensor, reconstruct the sequence that produced a failure, and identify the subsystem that should be investigated next.