## 4 Experimental Assessment
### DL-Based Diagnostic Assessments  
Apart from the spectral isolation module, the proposed intelligent monitoring framework features state-of-the-art stand-alone DL-based classifiers--1D-CNN and MLP. Although different custom and hybrid models have been reported in numerous works, generality remains a major concern for their global adoption [31]; therefore, it is often recommended to use established stand-alone DL-based models for practical cases. Although highly reliable, stand-alone DL algorithms have their limitations, which has motivated different methods for closing these limiting gaps. For example, CNNs are strongly affected by inputs with dynamic transient behavior, while MLPs are easily fooled by inputs due to their high dependence on a priori knowledge [32]. This is yet another valid rationale for adopting the spectral isolation technique as a preprocessing tool to ensure that only the critical spectral segments are retained. Arguably, certain factors affect computational costs, transfer learning potentials of DL models between domains, model stochasticity and over-fitting, and they often include: the model's structure--the number of layers, filters, and nodes in the mode--the right initialization method, choice of the activation function(s), hyperparameter values, learning rates and epoch sizes, the choice of regularization techniques employed, and the cost function (and the befitting loss minimization algorithm) employed. On the other hand, linear activation functions often flaw in comparison with nonlinear activation functions and even among the nonlinear counterparts, some are more suitable for classification problems (for example, ReLU and Softmax) while some are more suitable for regression problems (for example, ReLU, Tanh, and LeakyReLU). Consequently, it becomes a challenge to achieve a standard/global architecture for these models since, for instance, an excess of layers, nodes, and/or filters adversely effect computational efficiency and may lead to over-fitting, while the reverse may lead to under-fitting. However, users are motivated to try different architectures, and for each architecture, a cross-validation training/testing process over multiple trials is also encouraged to minimize the probability of accidental successes by the model(s). In this light, we designed different 1D-CNN and MLP models for the diagnostic process, and they are summarized in Table 1 below. As shown in Table 1, each of the DL models has different architectures with hyperparameter values that were chosen from experience (trial on different tasks), apart from the fully connected network (FCN) that was proposed in [33], and have been reported in many classification tasks. Overall, the Dense_output has three (3) nodes--one each for the different grinder wheel health stages, while the input shape is varied according to the shape (channel) of the input/source data. Generally, the \"categorical_crossentropy\" provides a reliable metric for evaluating the training loss for classification problems and was employed in all the models. On the other hand, the Adam weight optimizer--an improved stochastic gradient descent-based optimizer--offers better efficiencies, faster convergence, and improved validation scores on large datasets and was used throughout. The proposed diagnostic case study exploits vibration signals from seven (7) accelerometers with an optional module for employing the proposed spectral isolation technique; however, it also offers different input/source data channels, which may include the use of the raw signals (from all the accelerometers, the spindle only, or the frame only), the use of the critical segments from the signals (from all the accelerometers, the spindle only, or the frame only), and/or the use of alternating combinations of raw signals and critical segments from the signals (from all the accelerometers, the spindle only, or the frame only).