Smoke Detector TinyML for On-Device Predictive Maintenance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing smoke detectors face challenges in performing predictive maintenance and detecting fire phenomena independently without relying on cloud computing due to limited memory, computation, and power constraints, necessitating efficient algorithms and data structures for edge devices.
Innovation Solution
Implementing Tiny Machine Learning (TinyML) models, specifically using MEMS microphones and LSTM networks, to compress and deploy machine learning models on smoke detectors for predictive maintenance and sound pattern recognition, enabling efficient detection of fire-related sounds and building anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cloud computing infrastructure is used for processing smoke detector data, then computational accuracy and model training capability are improved, but latency, energy consumption, and privacy concerns increase
Solution Approach 1:
The patent introduces an intermediary mechanism by pre-training machine learning models offline and deploying compressed versions to edge devices. This intermediary process allows complex computational work to be done in advance on powerful systems, while the deployed models operate efficiently on resource-constrained smoke detectors without requiring real-time cloud communication.
Solution Approach 2:
The patent applies preliminary action by performing model training and compression operations before deployment to the edge device. The complex model training is completed in advance on cloud or workstation systems, and the resulting compressed models are then transferred to smoke detectors, eliminating the need for real-time cloud processing during operation.
2Measurement precision
If cloud computing infrastructure is used for processing smoke detector data, then computational accuracy is improved, but energy consumption increases
Solution Approach 1:
The patent introduces an intermediary mechanism by pre-training machine learning models offline and deploying compressed versions to edge devices. This intermediary process allows complex computational work to be done in advance on powerful systems, while the deployed models operate efficiently on resource-constrained smoke detectors without requiring real-time cloud communication.
Solution Approach 2:
The patent enables self-service by making smoke detectors self-sufficient through on-device inference capabilities. Once the compressed model is deployed, the smoke detector can independently perform detection and pattern recognition without requiring continuous cloud connectivity or energy-intensive data transmission, thereby reducing operational energy consumption.
3Adaptability or versatility
If traditional machine learning models are deployed on smoke detectors, then predictive maintenance capability is improved, but device complexity and resource requirements increase
Solution Approach 1:
The patent applies the extraction principle by removing unnecessary complexity from the machine learning model through compression techniques. The system extracts only the essential model parameters and structures needed for accurate inference, discarding redundant computational layers, thereby reducing model size and device complexity while preserving predictive maintenance functionality.
Solution Approach 2:
The patent utilizes parameter changes by transforming the model into a compressed format with modified parameters suitable for edge deployment. Through techniques like quantization and pruning, the model parameters are changed to occupy less memory and require fewer computational resources, enabling predictive maintenance capability on resource-constrained smoke detectors.
Data Source
Figure 1~2
Figure 3
AI summary
The present disclosure relates to a method and smoke detector comprising a microphone and an electronic data processor arranged to: capture a microphone signal for sampling an ambient sound sample over a period of time; process the captured ambient sound sample with a pretrained machine-learning model to determine whether the captured ambient sound sample matches predetermined sound patterns of repetitive machinery noise.