## 2 Motivation for Proposed Study and Related Works
Moreover, the authors of [15] proposed a comprehensive high-frequency vibration monitoring system for incipient fault detection and the isolation of gears, bearings and shafts/couplings in turbine engines and accessories, which feature a time synchronous averaging (TSA)-based denoising technique for noise reduction from events unrelated to the component of interest. These and many other studies reflect not just the efficiencies of spectral isolation as a denoising technique, but they also provide a verifiable rationale for developing computationally cost-effective and intelligent modeling, on the one hand, and improved discriminative modeling, on the other hand. In this domain, it may become an uphill task to determine which denoising technique is the most effective (considering all evaluation perspectives). However, the FFT offers a reliable solution for identifying critical frequency components in a signal [15; 16]. In contrast to traditional Bayesian methods, DL models offer superior intelligent modeling efficiencies and are skewing reliability studies (and applications) on industrial cyber-physical systems (ICPSs) as a consequence of the increasing complexity of processes and systems and the inherent necessity to model them [5]. Process efficiencies, equipment condition monitoring, spatiotemporal forecasting, and many other solutions have been recently improved as a result of this shift to DL-based support [21; 22; 23; 24; 25]; however, some issues remain, including over-fitting and interpretability issues, optimal hyperparameter selection/optimization, standardized weight initialization paradigm, and discovering the optimal decision criteria between power consumption and performance [4; 26]. In spite of this, given the necessity of providing accurate real-time solutions for ICPS components, and especially considering the growing need for uncertainty modeling, sensor data discrepancies, and dynamic operating conditions, DL techniques remain preferable even at the expense of computational power. In this light, numerous DL-based algorithms have been developed over the years, including, but not limited to the RNNs and echo state networks for time-series forecasting [21; 22; 23], CNNs for discriminative modeling/diagnostics [5; 24], and the multi-purpose DNNs [5; 11]. Most of these algorithms are stand-alone models that obviously come with their shortcomings and may be component-specific and/or application-specific. Moreso, the task presented herein clearly points at the CNNs and MLPs as possible solutions considering that their architecture are fundamentally designed for discriminative modeling and/or classification purposes (even though MLPs can also model regression/forecasting problems accurately). In this quest, our study explores different architectures of these algorithms for the proposed case study. This is particularly necessary considering that different model architectures (number of layers and hyperparameters) invariably produce different results at different computational costs [5; 11]. Actually, the deeper the model architecture, the higher the probability of over-fitting and computational cost (and vice versa). By exploring different architectures of the models, we may provide an empirically strong rationale for validating the impact of spectral isolation for improved condition monitoring/diagnostics on the one hand and the choice of, and recommendation for, the appropriate DL model(s) on the other hand, while considering ease-of-use, real-time applicability, and other necessary factors.