Antenna Array Fault Localization Using Near-Field Machine Learning
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Solution Overview
Problem
Existing methods for detecting and localizing faults in antenna arrays are computationally expensive, inefficient, and difficult to implement due to the high complexity and cost of monitoring large antenna arrays, especially when dealing with soft and hard faults, overheating, and scattering effects.
Innovation Solution
A computer-implemented method using machine learning techniques to detect and localize faulty antenna elements by analyzing near-field and far-field measurements, reducing the number of necessary measurements and improving robustness to imperfect test environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If brute force methods are used to measure each antenna element separately, then hard failures can be identified, but the method is limited for soft faults and becomes computationally expensive
Solution Approach 1:
The patent segments the fault detection problem into two distinct tasks: (1) classification of fault types (hard vs. soft faults) and (2) localization of faulty elements. This segmentation allows the system to apply different machine learning models optimized for each task, improving both accuracy and efficiency compared to brute force methods that attempt to solve both problems simultaneously
Solution Approach 2:
The patent replaces the mechanical/physical measurement approach (brute force measurement of each antenna element) with a machine learning-based system that processes electromagnetic field measurements. This substitution enables the system to detect both hard and soft faults efficiently by learning patterns from measurement data rather than requiring direct measurement of each element
2Measurement precision
If reconstruction approaches are used with transformation from near field to far field, then fault detection can be performed, but a high number of measurements and computations are required
Solution Approach 1:
The patent changes the parameter space by training machine learning models to work directly with near-field measurement data without requiring transformation to far-field conditions. This parameter change reduces the number of measurements needed and simplifies the measurement system while maintaining fault detection capability
Solution Approach 2:
The patent performs preliminary action by pre-training machine learning models with simulated data that includes various fault scenarios. This pre-training enables the system to detect faults with fewer actual measurements, reducing the complexity of the measurement system while maintaining high detection accuracy
3Productivity
If machine learning techniques are used for fault detection and localization, then computational effort is reduced and measurement requirements are minimized, but training data and model development are needed
Solution Approach 1:
The patent uses copying by creating simulated measurement data that replicates various fault scenarios. These synthetic copies of real measurement data are used to train the machine learning models, eliminating the need for extensive physical measurements during system setup while still providing comprehensive training coverage
Solution Approach 2:
The patent implements universality by developing a machine learning system that can detect multiple types of faults (hard faults, soft faults, isolated faults, block faults) using a unified approach. This multi-functional system reduces implementation complexity compared to having separate detection systems for each fault type
Data Source
AI summary
A computer-implemented method for detecting and localizing faults in an antenna array comprises performing fault detection on the antenna array to identify the presence of at least one faulty antenna element of the antenna array. The fault detection is performed using a machine learning technique. It is determined whether the antenna array is faulty or non-faulty, based on a result of the performed fault detection. The at least one faulty antenna element is localized if the antenna array is determined to be faulty, using a machine learning technique.