Electromechanical Diagnostics With HITL Alerts for Noisy IoT Data
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Solution Overview
Problem
Existing systems for diagnosing and alerting issues in electromechanical devices like HVAC systems face challenges in real-world IoT settings due to noisy power, sensor placement, weather, and device damage, which introduce latent variables, making it difficult to yield reliable results across a critical mass of the joint probability space.
Innovation Solution
A computer-implemented method and system that uses a machine learning model, such as gradient boosted trees or probabilistic neural networks, to curate data from electromechanical devices, receive ground truth data from users, compare and optimize the model, and provide actionable alerts and diagnostics, combining human-in-the-loop (HITL) machine learning with rules-based analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional assessment methods are used in real-world IoT settings, then device complexity is reduced, but measurement precision deteriorates due to noisy power, sensor placement, weather, and device damage introducing latent variables
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between the electromechanical device sensors and the diagnostic assessment. This model processes the noisy data from multiple sensors, power measurements, and environmental conditions to produce accurate diagnostic results despite the complex real-world conditions. The intermediary handles the complexity of latent variables while maintaining measurement precision.
2Measurement precision
If precise instrumentation is used to assess device metrics, then measurement precision improves, but device complexity and cost increase
Solution Approach 1:
The patent uses software-based machine learning models that replicate the functionality of expensive precise instrumentation. Instead of deploying specialized measurement devices, the system creates a virtual copy of diagnostic capabilities through algorithms that process standard sensor data, power measurements, and operational parameters to achieve laboratory-grade assessment accuracy in field conditions.
3Reliability
If more sensors and monitoring equipment are added to account for latent variables, then reliability improves, but device complexity increases
Solution Approach 1:
The machine learning model serves multiple diagnostic functions simultaneously, analyzing power consumption patterns, sensor readings, environmental conditions, and operational parameters to detect various types of issues including sensor placement problems, power noise effects, weather-related anomalies, and device damage. This multi-functional approach improves reliability without requiring separate specialized sensors for each potential issue type.
Data Source
AI summary
A computer-implemented method for providing explainable diagnostics for an electromechanical device. The method can include receiving data from an electromechanical device regarding a potential issue in the electromechanical device; curating the data with a machine learning model; receiving ground truth data associated with the curated data from a user of the electromechanical device; comparing the curated data with the ground truth data; and optimizing the machine learning model based on the comparing.