Evaluation of texture feature based on basic local binary pattern for wood defect classification

Wood defects detection has been studied a lot recently to detect the defects on the wood surface and assist the manufacturers in having a clear wood to be used to produce a high-quality product. Therefore, the defects on the wood affect and reduce the quality of wood. This research proposes an effective feature extraction technique called the local binary pattern (LBP) with a common classifier called Support Vector Machine (SVM). Our goal is to classify the natural defects on the wood surface. First, preprocessing was applied to convert the RGB images into grayscale images. Then, the research applied the LBP feature extraction technique with eight neighbors (P=8) and several radius (R) values. After that, we apply the SVM classifier for the classification and measure the proposed technique's performance. The experimental result shows that the average accuracy achieved is 65% on the balanced dataset with P=8 and R=1. It indicates that the proposed technique works moderately well to classify wood defects. This study will consequently contribute to the overall wood defect detection framework, which generally benefits the automated inspection of the wood defects

Experimental and computational vibration analysis for diagnosing the defects in high performance composite structures using machine learning approach

Delamination in laminated structures is a concern in high-performance structural applications, which challenges the latest non-destructive testing techniques. This study assesses the delamination damage in the glass fiber-reinforced laminated composite structures using structural health monitoring techniques. Glass fiber-reinforced rectangular laminate composite plates with and without delamination were considered to obtain the forced vibration response using an in-house developed finite element model. The damage was diagnosed in the laminated composite using machine learning algorithms through statistical information extracted from the forced vibration response. Using an attribute evaluator, the features that made the greatest contribution were identified from the extracted features. The selected features were further classified using machine learning algorithms, such as decision tree, random forest, naive Bayes, and Bayes net algorithms, to diagnose the damage in the laminated structure. The decision tree method was found to be a computationally effective model in diagnosing the delamination of the composite structure. The effectiveness of the finite element model was further validated with the experimental results, obtained from modal analysis using fabricated laminated and delaminated composite plates. Our proposed model showed 98.5% accuracy in diagnosing the damage in the fabricated composite structure. Hence, this research work motivates the development of online prognostic and health monitoring modules for detecting early damage to prevent catastrophic failures of structures

Approaches for diagnosis and prognosis of asset condition: application to railway switch systems

This thesis presents a novel fault diagnosis and prognosis methodology which is applied to railway switches. To improve on existing fault diagnosis, energy-based thresholding wavelets (EBTW) are introduced. EBTW are used to decompose sensor measurement signals, and then to reconstruct them within a lower dimensional feature vector. The extracted features replace the original signals and are fed into a neural network classifier for fault diagnosis. Compared to existing wavelet-based feature extraction methods, the new EBTW method has the advantage of an intrinsic energy conservation property during the wavelet transform process. The EBTW method localises and redistributes the signal energy to realise an efficient feature extraction and dimension reduction. The presented diagnosis approach is validated using real-world switch data collected from the Guangzhou Metro in China. The results show that the proposed diagnosis approach can achieve 100% accuracy in identifying a railway switch overdriving fault with various severities, improving upon existing methods of conventional discrete wavelet transform (C-DWT) and soft-thresholding discrete wavelet transform (ST-DWT) by 8.33% and 16.67%, respectively. The presented prognosis approach is constructed based on traditional data-driven prognosis modelling. The concept of a remaining maintenance-free operating period (RMFOP) is introduced, which transforms the usefulness of sensor measurement data that is readily available from operations prior to failure. Useful features are then extracted from the original measurement data, and modelled using linear and exponential regression curve fitting models. By extracting key features, the original measurement data can be transformed into degradation signals that directly reflect the variations in each movement of a switch machine. The features are then fed into regression models to derive the probability distribution of switch residual life. To update the probability distribution from one operation to the next, Bayesian theory is incorporated into the models. The proposed RMFOP-based approach is validated using real-world electrical current sensor measurement data that were collected between January 2018 and February 2019 from multiple operational railway switches across Great Britain. The results show that the linear model and the exponential model can both provide residual life distributions with a satisfactory prediction accuracy. The exponential model demonstrates better predictions, the accuracy of which exceeds 95% when 90% life percentage has elapsed. By applying the RMFOP-based prognosis approach to operational data, the railway switch health condition that is affected by incipient overdriving failure is predicted