# Cloud-Based Analytics Module for Predictive Maintenance of the Textile Manufacturing Process
## 4 Experiments
### Evaluation Criteria  
We used mean-squared error (MSE) and mean absolute error (MAE) to evaluate prediction errors. 1. Mean-squared error  
MSE is used to estimate errors by calculating the difference between the predicted and actual values. Equation (5) shows the formula for MSE. The value is the average squared error between the expected and actual values. MSE is considered a risk function, concerned with the predicted squared error loss. It is extensively used as an estimator to predict performance. It is the sum of squared distances between the target and predicted values. It is a common metric for loss functions in regression analytics. Herein, MSE is used as the loss function for estimating the prediction performance. \[\text{MSE}=\frac{1}{m}\sum_{i=1}^{m}(y_{i}-\hat{y}_{i})^{2}, \tag{5}\] \begin{table}
\begin{tabular}{c c c c c} \hline
**Filename** & **Size** & **Number of** & **Number of Raw** & **Description** \\
**(csv)** & **(MB)** & **Columns** & **Data** & \\ \hline warpop & 7.1 & 37 & 26,103 & operation parameters in the warping process \\ sizeop & 5 & 46 & 15,950 & operation parameters in the sizing process \\ beamop & 8.6 & 31 & 37,089 & operation parameters in the beaming process \\ weaveop & 50.9 & 25 & 161,821 & operation parameters in the weaving process \\ \hline \end{tabular}
\end{table} 
Table 4: Manufacturing dataset. \begin{table}
\begin{tabular}{c c c} \hline
**Process** & **Name** & **Description** \\ \hline warping & & WARPSPEED & The speed of warping \\  & & WARPPRES & The tension of the Warper’s Beam \\  & & SSTENSION & The tension of monofilament \\  & & WARPTSION & The tension of warping \\  & & HYDRATENSION & The tension of hydraulic warping. \\ \hline  & & SIZINGSPEED & The speed of sizing. \\  & & SIZINGBPRES & The pressure of sizing \\ sizing & & SIZINGATENSION & The tension of sizing (roll-out) \\  & & SIZINGBTENSION & The tension of sizing (winding) \\  & & CONSISTENCY & The density of forming polymeric material \\  & & DENSITY & The density of the sizing \\ \hline  & & BEAMSPEED & The speed of beaming \\ beaming & & BEAMATENSION & The tension of roll-out \\  & & BEAMBTENSION & The tension of winding \\ \hline \end{tabular}
\end{table} 
Table 5: Target equipment setting parameters. where a vector of \(m\) predictions is generated from a sample of k data points on all variables, and \(y\) is the vector of observed values of the predicted variable, and \(y^{\prime}\) is the predicted value. Mean absolute error  
MAE is a measure of the absolute errors between predicted and actual values, where each error has equal weight. MAE estimates the average magnitude of the differences in a set of predictive values. Equation (6) shows the formula for MAE, where the value is the average of the absolute difference between the predicted and actual values. Commonly, MAE is used to assess the accuracy of continuous variable predictions. Herein, MAE is employed to estimate prediction performance. \[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-\hat{y}_{i}|, \tag{6}\]  
where a set of k predictions is generated from a sample of k data points on all variables, \(y\) and \(y^{\prime}\) are the observed and predicted values, respectively. Cross validation is a statistical approach commonly used to estimate the performance of ML models. It is extensively used to compare and select a model for a given predictive task by recursively resampling the dataset. In this study, we used the K-fold cross-validation to evaluate the trained model because it reduces the possibility of bias from the data split. The K-fold cross-validation recursively split an original set into several subgroups and computes an average value across all partitions. Figure 8 shows the procedure for the K-fold cross-validation. Herein, we used 10-fold cross-validation (K = 10). At every epoch, data are divided into 10 subsets. Nine of the total subsets are used as the training dataset, and the remaining subset is used as the test dataset.