## 4 Transparency and Explainability in AI-Based Predictive Maintenance
Local methods such as LIME and SHAP provide model-agnostic means to explain and interpret the predictions generated by a wide range of machine-learning models. Deep learning models, known for their complexity, benefit from techniques like Grad-CAM and attention mechanisms, which offer insights into the input regions that influence their decisions. Some approaches to achieving model interpretability include techniques such as partial dependence plots (PDP) [163], decision tree visualization [164; 163], attention module [165], and feature importance calculation by using methods such as wrapper, filter, embedded, and dimension reduction techniques [166]. PDPs represent a method of showing the relationship among one or several components and the expected outcomes of machine learning models. They give us an insight into how changes affect model predictions for a given feature. The structure of the decision tree, the observed features split, and the way the tree makes decisions based on the input features are displayed in the decision tree visualization. On the other hand, the attention module contains a mechanism for generating feature weights consisting of parallel hidden layers. Each module part provides a value between 0 and 1, which shall be multiplied by the suitable feature to enter the rest of the network. The module can be visualized. Feature importance helps understand the features with the most weight in the model's predictions. Wrapper methods are trained with various input features to obtain the best results using heuristic and sequential search algorithms, which is time consuming [167]. Filter methods employ statistical metrics such as the Pearson correlation coefficient, mutual information, and X2 test prior to training to identify the importance of features. The techniques are unrelated to the model or its predictions after training, and they have no interest in interactions between features [168]. Embedded methods work on the subsets of the data along with techniques such as random forest, least absolute shrinkage and selection operator (LASSO) regression, ridge regression, regularization, decision tree, and XGBoost [169; 170]. Dimension reduction techniques such as PCA, ICA (individual component analysis), linear discriminant analysis (LDA) reduce the dimensionality of the dataset to obtain a smaller set of principal components that explain most of the variance in the data [171]. In the literature, there are a lot of other methods, such as rule-based systems and sensitivity analysis. Rule-based systems utilize logical rules to describe the decision-making process of a model, making the decision logic explicit and understandable [172; 173]. Lastly, sensitivity analysis helps identify the most influential input variables, contributing to a clearer understanding of a model's predictive behavior. In an era where AI plays an increasingly prominent role, these interpretability techniques and methods are indispensable tools for building trust, improving model performance, and ensuring ethical and accountable AI systems. Table 6 summarizes interpretable and explainable studies between 2018 and 2023 and their application content. In [174; 175; 176; 177; 178; 179; 180; 181; 182; 183], SHAP, LIME, ELIS, integrated gradients, SmoothGrad, and LRP have been used. Refs. [184; 185; 186; 187; 188; 189] use the semantic web rule language, rule-based expert systems, fuzzy systems, quantitative association rule mining (QARM), and data-driven sensitivity analysis. In [190; 191; 192; 193; 194; 195; 196; 197; 198; 199; 200; 201; 202; 203; 204], some techniques and visualizations such as decision trees, graph-based approaches, the attention modules used together with LSTM, generative adversarial networks (GAN), PDPs, and feature importance calculation methods. Refs. [205; 206; 207; 208] give some new algorithms, including interpretability and explainability, such as temporal fusion separable convolutional network, federated learning, HMM, and reinforcement learning.