High-Precision Inclinometer Temperature Compensation With BP Networks
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Solution Overview
Problem
Existing temperature compensation methods for inclinometers result in low precision, making them unsuitable for high-precision inclination data applications due to inadequate compensation of temperature effects on the scale factor and zero voltage.
Innovation Solution
A high-precision inclinometer temperature compensation method using a back propagation (BP) neural network-based model, optimized with the Limited-Broyden Fletcher Goldfarb Shanno (L-BFGS) iterative optimization algorithm, to improve precision by adapting model parameters and avoiding local optimal solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If polynomial fitting method is used to compensate temperature effects on scale factor and zero voltage, then the compensation process is simple, but the measurement precision is low
Solution Approach 1:
The patent transforms the compensation approach from direct polynomial fitting of scale factor and zero voltage to a neural network-based parameter transformation method. The BP neural network learns the complex nonlinear relationship between temperature and inclinometer output, automatically adjusting internal parameters (weights and thresholds) during training to achieve high-precision compensation without requiring explicit mathematical models of the temperature effects.
2Measurement precision
If BP neural network model is used for temperature compensation, then the measurement precision is improved, but the device complexity increases
Solution Approach 1:
The BP neural network implements self-service through automatic parameter optimization. During the training phase, the network autonomously adjusts its internal weights and thresholds using the L-BFGS optimization algorithm to minimize prediction error. This self-learning capability eliminates the need for manual model tuning and complex parameter setting, reducing the operational complexity despite the increased structural complexity of the neural network itself.
Solution Approach 2:
The patent incorporates feedback mechanisms through the neural network training process. The L-BFGS optimization algorithm continuously evaluates the network's prediction accuracy against known data and adjusts parameters accordingly. This feedback loop enables the system to automatically improve its compensation performance, balancing the increased model complexity with automated error correction that reduces manual intervention requirements.
Data Source
AI summary
A high-precision inclinometer temperature compensation method is provided, including: acquiring original data of an inclinometer; and constructing a BP neural network model, optimizing the BP neural network model by adopting L-BFGS iterative optimization algorithm, and inputting the original data into an optimized BP neural network model to obtain a temperature compensation result of the inclinometer.