Show HN: I used an IoT sensor and Claude to diagnose a hairdryer (lgdimaggio.github.io)

A developer demonstrated a novel approach to diagnosing hairdryer faults using an IoT sensor and Claude, an AI model. Unlike traditional methods that rely on vague fault predictions, this setup utilizes real-time data from a wideband accelerometer to provide precise diagnostics. By interpreting sensor data through established signal-processing techniques, Claude delivers evidence-based insights, including specific fault indicators, severity assessments, and actionable maintenance recommendations. For instance, it detected potential outer-ring bearing wear with quantified reliability and vibration levels, all explained in user-friendly terms. This development is significant for the AI/ML community as it showcases the integration of machine learning with IoT technology for enhanced predictive maintenance. By employing a modular approach with dedicated Claude Skills and standardized ISO 10816 guidelines, the system ensures reliable and repeatable diagnoses. It not only streamlines the diagnostic process with a natural language interface but also allows for comprehensive reporting, making it accessible to maintenance teams without requiring extensive technical expertise. This innovative combination could reshape how industries approach equipment maintenance, leading to improved operational efficiency and reduced downtime.