## 4 Transparency and Explainability in AI-Based Predictive Maintenance
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ref.** & **The Approach** & **Application** \\ \hline
[174] & SHAP & PdM in a coal crusher operating at the boiler of the real power plant and gantries in a steelworks converter, a transport line in a steelworks converter \\ \hline
[175] & SHAP & PdM in a coal crusher operating presented in [174] \\ \hline
[176] & LIME, SHAP, and ELI5 & Solar photovoltaic energy forecasting by GEFCOM dataset [147] \\ \hline
[177] & Integrated gradients, SHAP, and SmoothGrad & Anomaly detection in press machine data of a production line \\ \hline
[178] & SHAP and LIME. & RUL estimation of hard disk drive in the Backblaze dataset [140] \\ \hline
[179] & SHAP & RUL estimation of the engines on the NASA turbofan engine dataset [132,133] \\ \hline
[180] & Integrated gradients & PdM to reveal the most sensitive gearbox operations in the MW load range in a petrochemical plant to a specific abnormality \\ \hline
[181] & SHAP & RUL estimation of NASA turbofan engine dataset [132,133] \\ \hline
[182] & LIME and NLP & Maintenance work orders \\ \hline
[183] & LRP & Bearing health condition estimation on the NASA bearing dataset [136] \\ \hline
[184] & Knowledge-based system including domain ontologies and semantic web rule language rules & The detection of future machinery failures as well as the prediction of their time of occurrence in semiconductor manufacturing process by the UCI SECOM dataset [148] \\ \hline
[185] & Rule-based expert system & PdM of a real hybrid bus \\ \hline
[186] & Real type-2 fuzzy-based XAI & PdM within the water pumping industry \\ \hline
[187] & Rule-based model called logic language model & RUL estimation of the engines on the NASA turbofan engine dataset [132,133] \\ \hline
[188] & QARM algorithm & RUL prediction of a drilling machine of an automotive manufacturer \\ \hline
[189] & A data-driven sensitivity analysis & The prediction of the future reliability of components in a large gas distribution network \\ \hline
[190] & Bagged decision trees & A synthetic dataset that reflects real predictive maintenance data encountered in the industry \\ \hline
[191] & Gradient boosting decision tree & The prediction machine errors or tool failures on Microsoft Azure dataset [146] \\ \hline
[192] & A premier transparent, interpretable, and self-explainable automated machine learning software, including methods like random forest and gradient boosting \\ \hline
[193] & A virtual knowledge graph-based approach & PdM in the hydraulic systems \\ \hline
[194] & Attention and LSTM-GAN & PdM to reduce maintenance costs and downtime of machines in the intelligent manufacturing system \\ \hline
[195] & Attention and bidirectional LSTM & RUL estimation on the NASA turbofan engine dataset [132,133] \\ \hline
[196] & Bidirectional self-attention gated recurrent unit & The prediction of the health index on the NASA rolling bearing dataset from IEEE PHM challenge [138] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of different transparency and explainability approaches used in AI-based PdM. An extensive application range of PdM consists of a coal crusher operating at the boiler of the real power plant and gantries in a steelworks converter, a transport line in a steelworks converter, the MW load range in a petrochemical plant, a real hybrid bus, hydraulic systems, a modular aero-propulsion system, an intelligent manufacturing system, the water pumping industry, a large gas distribution network, rolling bearings and a rotating machine, PdM for autonomous underwater vehicles (AUV), a water injection pump, wind turbine systems, the IoT-based manufacturing environment, hard disk drives, the turbofan engine, the drilling machine of an automotive manufacturer, solar photovoltaic energy systems, maintenance work orders, manufacturing, and structural health monitoring. Figures 8 and 9 show numbers relating to PdM and explainable/interpretable AI in Web of Science and Google Scholar for 2018 to 2023, respectively.