## 3 State-of-the-Art Techniques for Predictive Maintenance  
SOTA consists of (i) the AI-based PdM approaches, including machine learning algorithms, deep learning, statistical control, and statistical modeling, (ii) data-driven approaches, including big data analytics, data mining, data visualization, and predictive data analysis, (iii) vibration and thermal analysis, (iv) augmented reality (AR), virtual reality (VR), mixed reality (MR), and their extended versions, and digital intelligent assistants, (v) methods based on prescriptive maintenance, (vi) edge and cloud computing, IoT, federated learning, and blockchain, (vii) energy-based methods, and (viii) methods based on prognostics and health management (PHM) [23; 24; 25; 26; 27; 28; 29; 30; 31]. This section reviews some PdM studies using these approaches. Each paper has been evaluated through the application and the proposed approach. PdM applications employ all AI approaches, including the classification and/or regression problem type of the supervised learning approach, the clustering problem type of the unsupervised learning approach, or a problem type relating to the reinforcement learning (RL) approach to analyze the large volume of data obtained from real-time condition monitoring systems. The approaches have presented magnificent contributions to PdM tasks [23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52]. Deep neural networks (DNNs) covering CNNs, RNNs, and LSTMs have been used to generate the proposed predictive maintenance strategies and algorithms and improve their prediction accuracy [53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64]. In these works, the networks have been applied to extract the significant features from raw sensor data, including images or the data in the form of time series, and to detect, recognize, or predict sudden and expected changes in the system according to the features. Machine learning algorithms have been successfully applied to many PdM applications [65; 66; 67; 68; 69]. Algorithms such as feedforward neural networks (FNNs), decision trees (DT), random forests (RF), and support vector machines (SVMs) have been used to classify sensor data. Refs. [70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84] have shown various works applying statistical and probabilistic modeling approaches such as hidden Markov models (HMMs), Bayesian networks (BNs), Gaussian mixture models (GMMs), extreme gradient boosting (XGBoost), Density-based spatial clustering (DBSC), principal component analysis (PCA), and K-means to PdM tasks. Moreover, they introduced different DNN models, such as LSTM and autoencoders, for the tasks. They have identified the deterioration event by analyzing an interdependent relationship among the data collected from multi-sensors and predicted possible future failures. In [85; 86; 87; 88; 89; 90; 91; 92], data-driven approaches, including big data analytics relating to data from various sources, including sensor data, historical records, external factors, and data mining, have been used to improve the accuracy and comprehensiveness of PdM systems. Significantly, interactive and intuitive data visualization tools have contributed to quickly understanding the equipment's health and making informed decisions. In [93; 94; 95; 96; 97; 98; 99], vibration analysis is a widely used method of PdM. The analytical method uses sensors to measure the vibrations of machinery and identify possible problem areas, such as bearing failure or misalignment on various machines, including motors, pumps, and gearboxes. Thermal imaging is another widely known technique for PdM [100; 101; 102; 103; 104]. The technique uses infrared cameras to perceive potential problems, such as overheating, breakdown, friction, and energy inefficiencies, on a wide range of equipment, including electrical panels, transformers, and motors, by measuring the temperature of equipment.