The most universally acknowledged benchmark for augmentation of effectiveness for chemical process control is the Tennessee Eastman process. Such as its versatility that it has been widely utilized as the threshold for development of numerous fault detection methods that can boost efficiency while simultaneously establishing operational safety. Thus, through this research excursion, we propose a further developed and novel approach towards fault detection through advanced statistical analysis, the Statistical Rank-Based Model for Fault Detection (SRBM-FD). Rather than simply relying on the fundamental accuracy of various Machine Learning and Deep Learning algorithms, the SRBM-FD intends to introduce astuteness in the feature selection process. This is done through a comprehensive ranking framework based on the combined potency of Correlation Ratios (Eta correlation), Spearman and Point-Biserial correlations. The additional focus on the relevance of specific features to fault detection, one is able to streamline the process and in turn, enhance its effectiveness. Through rigorous experimentation and regular comparison with orthodox fault detection methods, we have been able to conceive a more thorough and cohesive process in the form of the SRBM-FD. The versatile and ubiquitous nature of the SRBM-FD model underscores its potential for use in fault detection across numerous industrial processes that can essentially enhance efficiency and improve operational safety.