Condition Assessment of Timber Utility Poles Based on a Hierarchical Data Fusion Model

Abstract

This paper proposes a novel hierarchical data fusion technique for the non-destructive testing (NDT) and condition assessment of timber utility poles. The new method analyses stress wave data from multi-sensor multi-excitation guided wave testing using a hierarchical data fusion model consisting of feature extraction, data compression, pattern recognition and decision fusion algorithms. The proposed technique is validated on guided waved at a of in-situ timber poles. The actual health states of these poles are known from autopsies conducted after the testing, forming a ground-truth for supervised classification. In the proposed method, a data fusion level extracts the main features from the sampled stress wave signals using power spectrum density (PSD) estimation, wavelet packet transform (WPT) and empirical mode decomposition (EMD). These features are then compiled to a feature vector via real-number encoding and sent to the next level for further processing. Principal component analysis (PCA) is also adopted for feature compression and to minimise information redundancy and noise interference. In the feature fusion level, two classifiers based on support vector machine (SVM) are applied to sensor separated data of the two excitation types and the pole condition is identified. In the decision making fusion level, the D-S evidence theory is employed to integrate the results from the individual sensors obtaining a final decision. The results of the in-situ timber pole testing show that the proposed hierarchical data fusion model was able to distinguish between healthy and faulty poles demonstrating the effectiveness of the new method.

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