AI Sensor Data Acquisition for Adaptive DAQ Parameter Tuning
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Solution Overview
Problem
Industrial data acquisition systems face challenges in optimizing sampling rate, bit resolution, and number of analog inputs due to constraints on power consumption, storage, and hardware limitations, leading to suboptimal data fidelity and increased data volume, with existing methods lacking adaptability to short-term and long-term signal variations.
Innovation Solution
Implementing a reinforcement learning-based system that dynamically adjusts DAQ parameters by comparing Fast Fourier Transforms of sensor data under maximum and optimized configurations, updating a probability distribution to set optimal parameters for balancing data fidelity and volume, and periodically re-evaluating long-term trends.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the sampling rate is increased to capture high frequency components, then data fidelity is improved, but storage requirements and processing power increase
Solution Approach 1:
The patent implements dynamic sampling rate adjustment where the DAQ system automatically adapts the sampling rate based on real-time analysis of signal characteristics. The system transitions from static to dynamic parameter configuration, using spectral analysis to determine when high sampling rates are necessary versus when lower rates suffice, thereby optimizing the balance between data fidelity and data volume.
Solution Approach 2:
The system changes the sampling rate parameter dynamically based on signal conditions. By monitoring spectral content and adjusting the sampling rate parameter in response to actual signal requirements, the system achieves high fidelity when needed while reducing data volume during periods when lower sampling rates are sufficient.
2Measurement precision
If the number of quantization levels is increased to preserve signal information, then measurement precision is improved, but data volume increases
Solution Approach 1:
The patent implements dynamic quantization level adjustment where the DAQ system adapts the number of quantization levels based on real-time signal characteristics. The system uses spectral analysis to determine when high precision quantization is necessary versus when lower precision suffices, optimizing the balance between information preservation and data volume reduction.
Solution Approach 2:
The system dynamically changes the quantization levels parameter based on signal conditions. By monitoring spectral content and adjusting the quantization parameter in response to actual signal requirements, the system achieves high measurement precision when needed while reducing data volume during periods when lower precision is sufficient.
3Device complexity
If multiple analog inputs are shared by a single ADC, then device complexity is reduced, but the maximum sampling rate for each input is limited
Solution Approach 1:
The patent implements dynamic sampling rate allocation where a single ADC dynamically adjusts its sampling rate for different analog inputs based on the spectral content of each signal. The system transitions from static uniform sampling to dynamic adaptive sampling, allowing high sampling rates for signals requiring them while using lower rates for other inputs, thereby maintaining low device complexity while achieving high effective sampling rates where needed.
Solution Approach 2:
The system dynamically changes the sampling rate parameter for each analog input based on signal characteristics. By monitoring spectral content and adjusting the sampling rate parameter individually for each input shared by the single ADC, the system achieves high sampling rates for critical signals while maintaining low overall device complexity through shared hardware.
4Stability of the object's composition
If DAQ parameters are fixed for long-term operation, then system stability is improved, but adaptability to signal variations deteriorates
Solution Approach 1:
The patent transforms the DAQ system from static fixed-parameter operation to dynamic adaptive operation. The system continuously monitors signal characteristics and adjusts sampling rate and quantization levels in real-time, achieving both stability through controlled changes and adaptability through response to signal variations. The dynamic nature allows the system to maintain optimal performance across varying operational conditions.
Solution Approach 2:
The system implements feedback mechanisms where spectral analysis of the input signal provides information about signal characteristics, which then feeds back to adjust the DAQ parameters. This closed-loop control enables the system to adapt to signal variations while maintaining stability through controlled, incremental parameter adjustments based on actual signal requirements.
Data Source
AI summary
Example implementations described herein can dynamically adapt to changing nature of sensor data traffic and through artificial intelligence (AI, strike a good tradeoff between reducing volume of sensed data, and retain enough data fidelity so that subsequent analytics applications perform well. The example implementations eliminate heuristic methods of setting sensing parameters (such as DAQ sampling rate, resolution etc.) and replaces them with an automated, AI driven edge solution core that can be readily ported on any Internet of Things (IoT) edge gateway that is connected to the DAQ.