## 3 State-of-the-Art Techniques for Predictive Maintenance
In [105; 106; 107; 108], the technologies of AR, VR, MR, digital intelligent assistant, digital twin, and IoT sensors have been leveraged. The IoT sensor has enabled real-time data collection from various sensors attached to equipment in PdM. The technologies of AR, VR, and MR increase the capabilities of maintenance technicians with visual guidance, remote assistance, real-time information, and a virtual view of equipment status to perform PdM, improving efficiency and accuracy. Ref. [109] showed an approach for maintenance experts and operators to interact with a PdM system by AI intelligent assistant through natural language processing (NLP) and user feedback about the success of maintenance interventions. In [110, 111, 112, 113, 114, 115], PdM is realized through prescriptive maintenance. It provides specific recommendations for maintenance actions going beyond prediction. It includes detailed instructions for technicians on what steps to take. This approach predicts when maintenance is needed and recommends the most effective and efficient maintenance actions. Refs. [116, 117, 118, 130, 131] have used edge computing and cloud computing. Edge computing is used to proceed closer to the data source through IoT sensors for real-time analysis rather than in a centralized data center, which reduces latency and enables real-time analysis. To analyze historical events, cloud computing stores and manages enormous amounts of data. In [119, 120, 121], federated learning, blockchain, and industrial IoT have been used. Federated learning allows multiple parties to train a machine learning model collaboratively without sharing their data. Blockchain ensures data integrity, while industrial IoT sensors provide real-time equipment condition data. It may be indicative of problems if unusual energy consumption is observed. In [122, 123, 124, 125], PdM has been provided by analyzing their energy consumption patterns to gain information on the health of such devices. In [126, 127], PdM has been carried out by focusing on PHM. PHM techniques take a different approach to assessing equipment health from predicting failures. Advanced modeling and data analysis are generally included in these methods, enabling further RUL estimation of an asset and providing valuable insight into maintenance planning. Ref. [128] shows an example of a predictive analytics software platform for PdM. In order to make implementing PdM easier, numerous software platforms and tools have been developed. Premade models and features for data integration are frequently included in these platforms. This article reviews real-world simulation and experimental applications in tables, while some utilize benchmark datasets. Table 1 describes the benchmark datasets such as the National Aeronautics and Space Administration (NASA) Turbofan [132, 133], PHM 2008 [134], NASA Ames Milling [135], NASA Bearing Dataset [136], CWRU Bearing [137], FEMTO Ball Bearing [138], Roll Bearing [139], Backblaze [140], PAKDD2020 Alibaba AI OPS [141], NASA Ames Prognostics [142], Lithium-ion Battery of University of Maryland [143], MOSFET Thermal Overstress Aging [144], MFAULDA [145], Microsoft Azure PdM [146], GEFCOM [147], and The UCI SECOM [148] in multivariate time-series forms commonly employed in PdM applications.