Degradation prognostics of aerial bundled cables based on multi-sensor data fusion

Development of advanced health monitoring sensors and high-performance computing enabled multi-sensors information to analyse degradation in complex engineering systems/components. These sensors are often aimed to capture different physical aspects of a system. Thus, each sensor only contains partial information about the same degradation process. A significant need is to devise a contemporary data fusion method that effectively integrates independent multi-sensors measurements leading to a better prognosis of the degradation process. Unlike conventional data fusion methodologies that fuse multiple sensors’ information prior implementation of prognosis step, this paper presents a novel fusion framework based on multi-sensor prognosis data. The framework is developed using Particle Filtering (PF) technique coupled with Auto-Regressive Integrated Moving Average (ARIMA) model. The proposed framework is applied on historical Non-Destructive Testing (NDT) database generated through Ultrasonic Probe Listening and Thermal Imaging of in-service aerial bundled cables (ABCs) installed in coastal regions to evaluate cable degradation over time. Promising results of f-steps prediction scheme from fused degradation values, as compared to using individual measurement mode data, indicates the efficacy of the proposed method.