# Improved Ensemble-Learning Algorithm for Predictive Maintenance in the Manufacturing Process
## 3 IIoT-Based PdM-Related Studies
### Application of Ensemble Learning in Predictive Maintenance
\begin{table}
\begin{tabular}{c c} \hline \hline
**Year** & **Description** \\ \hline
2016 & [PERSON] et al. presented IIoT potential applications, including data mining, predictive analytics, statistical approaches, and other application opportunities, to increase manufacturing productivity and efficiency [15]. \\
2016 & [PERSON] et al. implemented the Industry 4.0 framework with a multilayered framework and demonstrated how such an approach affects current manufacturing systems [16]. \\
2017 & [PERSON] et al. introduced future trends of cyber manufacturing systems and IIoT in Industry 4.0 [17]. \\
2015 & [PERSON] et al. reported that Industry 4.0 significantly affects corporate strategies and competition in many industries [18]. \\
2017 & [PERSON] et al. provided an overview of smart manufacturing and Industry 4.0 and programs and analyzed the application potential of cyber–physical systems starting from the production process [19]. \\
2017 & [PERSON] et al. proposed the hierarchical architecture of a smart factory with three layers and discussed the significant problems and potential solutions to vital emerging technologies [20]. \\
2018 & [PERSON] et al. discussed that big data play a vital role in supporting smart manufacturing and proposed the conceptual framework for big data analytics [21]. \\
2018 & [PERSON] et al. highlighted the opportunities created by the challenges and potential of IIoT [22]. \\
2016 & [PERSON] et al. investigated the disciplinary distribution of emerging Industry 4.0-related topics [23]. \\
2017 & [PERSON] et al. proposed a big data solution for active preventive analytics in the manufacturing industry [24]. \\
2019 & [PERSON] et al. confirmed that Industry 4.0 is associated with front–end technologies, which comprise four parts: smart supply chain, smart working, smart manufacturing, and smart products [25]. \\
2015 & [PERSON] et al. introduced IoT development; IoT-related technologies are enabled to connect massive cyber–physical systems [26]. \\
2018 & [PERSON] et al. surveyed current research on Industry 4.0 and provided a simple guide for the process of Industry 4.0 transition [27]. \\
2016 & [PERSON] et al. surveyed the effect of IIoT on the current business model [28]. \\
2017 & [PERSON] et al. provided a systemic overview of the industrial internet. They focused on applications, technologies, and existing challenges [29]. \\
2017 & [PERSON] et al. referred to the standardization and development of cyber connectivity applications to achieve the goal of IIoT implementation [30]. \\
2019 & [PERSON] et al. surveyed the literature concerned with how IoT impacts supply chain management [31]. \\
2017 & [PERSON] et al. argued that IIoT provides prospects and emerging business models for enterprises [32]. \\
2017 [PERSON] et al. introduced the advanced IoT (IIoT) application and proposed a monitoring system for predictive maintenance manufacturing [33]. \\
2017 [PERSON] et al. introduced the development of IoT, IoT-related definitions, and IoT-related application areas [34]. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: IIoT-based PdM-related studies from 2015 to 2020.  
have used the decision jungle algorithm to predict software defects [38]. A boosted decision tree is useful in the event of pipe failure [39]. The results of these previous works demonstrated that the ensemble-learning solution provides users with convenient tools to analyze equipment data and develop maintenance strategies.