## 5 Challenges and Limitations of Using AI for PdM Autonomy  
Several problems and limitations that need to be overcome to realize AI's potential benefits are posed by its use for PdM autonomy. Some key challenges and limitations are (i) transparency and explainability, (ii) integration with existing systems and workflows, (iii) data quality and quantity, (iv) the lack of real-world data, (v) the lack of standard evaluation metrics, (vi) ethical considerations, and (vii) effective human-machine interaction.  
Transparency and explainability could make understanding and believing the decisions adopted in this system difficult for human operators, leading to mistrust and lack of acceptance among users. All developments about transparency and explainability are given in Section 4. More development should be undertaken to gain the trust of human operators in PdM applications.  
The seamless integration into existing systems and workflows requires specialized software and expertise. Moreover, the different types of data formats, communication methods, and protocols create difficulties in integrating AI systems into current systems. There are some approaches for the seamless integration of AI-based PdM systems into current systems and workflows [209; 210; 211; 212; 213; 214]. In [209; 220; 221], a modular design has been applied. The design allows the AI-based system to be flexibly integrated with existing systems and workflows and adaptable using standard interfaces and protocols, such as Modbus, Snap7,  
Figure 8: The number of studies relating to PdM and explainable/interpretable AI in Web of Science between 2018 and 2023.  
Figure 9: The number of studies relating to PdM and explainable/interpretable AI in Google Scholar between 2018 and 2023.  
and OPC-UA, to communicate with existing systems and develop APIs. Refs. [223; 224] has leveraged a service-oriented architecture (SOA) for integration. Web services, such as RESTful APIs, to access the AI-based system and message-oriented middleware, such as MQTT, to communicate with existing systems have been applied. Additionally, some researchers have proposed using edge computing and fog computing to integrate AI-based PdM systems into existing systems [219; 220; 221].  
Data quality and quantity are crucial for training and validating AI-based PdM models. High-quality data provides accurate predictions. In contrast, the time and costs of gathering and cleaning data may be considerable. In addition, it is essential to have large amounts of data for learning deep learning models, and in certain cases, they may be challenging to obtain [85; 86; 129; 192; 210]. AI-based PdM systems should be tested and validated under various conditions, such as different types of equipment, different operating conditions, and different levels of data quality for successful real-world applications. The lack of real-world data makes it difficult to test the system under realistic conditions and to evaluate its performance and accuracy. Researchers have proposed various methods for simulating real-world scenarios to overcome this challenge, such as using virtual environments and testbeds [26; 25; 27; 26; 28; 29; 37; 42; 35; 37; 42; 35; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58;  
they can be constantly updated with real-time equipment data, enabling them to learn the patterns associated with healthy operational functioning.