## 3 State-of-the-Art Techniques for Predictive Maintenance
A limited number of studies represent infrastructure, pumping systems, marine, textile, logistics and parcel delivery, steel strip processing, chemical industry, aviation, and home energy management. In addition, Table 3 also shows different PdM tasks and study numbers. RUL, cost, and charge estimation tasks have attracted attention with 11 studies. These tasks cover strategically essential issues such as estimating the useful life of equipment and determining maintenance costs. Fault detection tasks are a vital topic, with 12 studies focusing on the early detection and prevention of malfunctions that may occur in systems. The condition and vibration monitoring task has attracted the attention of nine studies. The anomaly detection task is a distinct area with four studies focusing on detecting unexpected situations and abnormal behavior. Other tasks are represented by only one study each and seem to concentrate on more specific topics. These tasks include product lifecycle management, component fatigue strength prediction, PdM by a digital intelligent assistant, blockchain framework for PdM, and a big data analytics framework. As a result, basic tasks such as failure prediction, RUL estimation, fault detection, and anomaly detection are popular areas of AI and maintenance. In contrast, other tasks are more specialized and focus on specific topics. Although PdM tasks often appear to be primarily classification and regression problems in Table 3, the presence of unknown events and the general nature of the data with ambiguous labels clearly shows the importance of unsupervised learning, reinforcement learning, and statistical and probabilistic applications in PdM applications. In addition, Table 2 includes some PdM tasks without using AI. Ref. [89] includes production quality prediction in the textile industry. Refs. [101; 102; 103] use thermal imaging technology in railways, medical, and hydraulics industries. Ref. [110] applies data management, predictive data analytic toolbox, recommender and decision support dashboard, and semantic-based learning and reasoning for PdM in the automotive manufacturing industry. Ref. [114] introduces the optimal maintenance strategy applying reliability analysis, sensitivity analysis, and a continuous stochastic process in the rail transport industry. Ref. [115] applies a discrete-event simulation framework for post-prognostic decisions in the aviation industry. Ref. [128] introduces a predictive analytics software platform in the manufacturing industry. Moreover, Ref. [91] includes quality management in the vinyl flooring industry using big data analytics and optimization and edge computing without having a PdM task, but it is related indirectly. Figures 4 and 5 show the number of studies carried out using different machine learning methods such as FNN, CNN, LSTM, autoencoder, SVM, RNN, fuzzy, RF, K-means, decision trees, rule-based intelligence system, DR, HMM, Bayes, ELM, and gradient boosting in Web of Science and Google Scholar for 2018 to 2023, respectively. Total study numbers in Web of Science and Google Scholar have been determined as (67, 104, 149, 213, 296, and 255) and (4406, 6506, 10,498, 16,638, 22,619, and 28,069) over 2018-2023, respectively. Both the results in Google Scholar and Web of Science clearly show that the fusion of PdM and AI will continue over the years. Since 2019, there has been a notable increase in the use of deep learning methods, specifically LSTM, CNN, and autoencoder, with a particularly pronounced surge in the utilization of DRL in the last two years. Additionally, SVM has maintained its relevance since 2018, and its usage has continued to grow. Another remarkable result has been observed in decision trees. The use of decision trees has shown  
a consistent upward trend since 2018. Notably, there has been a visible increase in the adoption of fuzzy and rule-based methods. Moreover, HMM, Bayes, gradient boosting, and FNN also exhibit a growing trend in usage since 2018.