Predictive Maintenance and Hardware Diagnostics

• Analyze sensor data streams — motor current, vibration, temperature, and cycle time — to identify early indicators of component degradation before failure occurs Analyze
• Evaluate AI-assisted anomaly detection alerts (e.g., "left drive motor current 15% above baseline, predicted bearing failure in 7-14 days") to prioritize maintenance scheduling during low-volume windows Evaluate
• Apply remote diagnostic workflows — pulling navigation logs, LIDAR data, network logs, and software version data — to resolve 60% of reported issues without dispatching a field visit Apply
• Create hardware diagnostic decision trees organized by symptom ("robot stops unexpectedly" → check battery → check network → check obstacle detection → check software version → escalate) Create
• Analyze MTBF (Mean Time Between Failure) data per component type to optimize preventive maintenance schedules — replacing components at data-driven intervals rather than fixed calendar schedules Analyze
• Apply a spare parts inventory strategy based on component failure rates, lead times, and regional depot placement to reduce MTTR (Mean Time To Repair) Apply
• Create comprehensive maintenance logs in a CMMS capturing robot ID, failure mode, root cause, parts replaced, labor hours, and downtime duration to build the dataset that enables predictive maintenance Create
• Evaluate the cost tradeoff between reactive (fix when broken), preventive (fixed schedule), and predictive (data-driven) maintenance approaches for different component types Evaluate

Levels: Remember · Understand · Apply · Analyze · Evaluate · Create — highest demands most original thinking.