Principal Component Analysis (PCA) is a dimensionality reduction technique used in statistics and data analysis. Its primary goal is to transform high-dimensional data into a lower-dimensional space while retaining as much of the original variability as possible. PCA achieves this by identifying the principal components, which are linear combinations of the original variables. These components are ordered by the amount of variance they capture, allowing for the reduction of data complexity while preserving essential information.


Vibration-Based Status Identification of Power Transmission Poles

Among the power transmission infrastructures, the low-voltage overhead power lines are specifically critical, due to the complicated roadside environments and the significant number of connections to the end utility users. Maintaining of such a large size grid with mostly wood poles is a challenging task and knowing the operating status and its structural integrity drastically speeds up the routine inspection. Applying a data-driven approach using accelerometer data to analyze the power line-induced vibration to classify different poles within different operational conditions is proposed.Feature creation is the important aspect to improve an accuracy of data-driven algorithms. For this purpose, a time-frequency domain classifier is developed, based on the data collected from two tri-axial accelerometers installed on the wood poles before and after streetlights are on. Data are explored both in time and frequency domain using techniques such as data augmentation and segmentation, averaging, filtering, and principal component analysis. Results of the machine learning classifier clearly shows distinct characteristics among the data collected from different work conditions and different poles. Further exploration of the applied algorithm will be pursued to construct more sophisticated features based on supervised learning to enhance the identification accuracy.