Optimization of the Hybrid Feature Learning Algorithm

In recent years, machine learning (ML) algorithms have been used to minimize maintenance costs and identify problems early in the automotive sector. The determination of an asset’s residual useful life of a component at a specific time is known as “remaining useful life” (RUL). The extensive evolution of data makes it challenging to analyze and interpret high-level and valuable features from the data. The issue arises in all disciplines, and the automotive industry is no exception, given the large number of sensors to consider. Existing RUL research has not given much thought to the influence of high dimensionality data on component maintenance and deterioration. The fundamental purpose of feature selection (FS) is to select a subset of features from the data without compromising model performance. This work proposes a hybrid approach to the FS problem that combines Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). When tested on public datasets, our results demonstrate a rise in regression accuracy and a reduction in the number of selected features.