# Cloud-Based Analytics Module for Predictive Maintenance of the Textile Manufacturing Process
### IIoT and Textile Manufacturing  
On a factory production line, different equipment is used for different processes, and equipment settings differ as per process specifics. Speed is an important factor in the warping process [5], and tension is important in the weaving process [6]. In beaming, the  
Figure 4: Beaming machine structure.  
Figure 3: Sizing machine structure.  
Figure 2: Warping machine structure.  
focus is on material density settings. In general, equipment speed and tension are important factors for determining the quality of WIP in warping, beaming, sizing, and weaving. Excessively high tension may cause yarns to fracture, whereas excessively low tension may cause yarn breakage [6; 7]. Equipment settings account both for the type of process and the composition of raw materials. Settings are determined based on the particular specifications of a textile product, including its weave structure and material. Considerable amount of data are generated by complex manufacturing equipment and raw materials used in textile manufacturing. At each stage in the manufacturing process, effective communication and data sharing are important for efficient quality management. IIoT can connect multiple equipment and other physical objects using network technologies via software platforms such as supervisory control and data acquisition (SCADA). Data can be collected from the edge to the cloud, and the user can access the integrated information and make extended applications such as manufacturing visualization, predictive analytics, and operation management. Recently, IIoT technology has been applied to textile manufacturing [2; 8]; textile manufacturing facilities, materials, and other sources of industrial management data can be used in an IIoT application for digitalization, automation, and learning purposes. IIoT technology has been employed in PdM analytics for yarn [9] and seam quality [10]. The primary applications of IIoT in textile manufacturing include equipment and supply chain management; however, quality management is an important issue in the textile industry. To address the challenges of quality management in a complex textile production line, we applied ML algorithms to equipment data for quality prediction. Manufacturing processes and equipment settings can be promptly analyzed and visualized on a cloud platform. The aims of this study are listed below:  
* To predict textile production quality using ML.
* To create a PdM API for cloud data analytics that can be integrated with third party IoT.
* To enhance production line performance and enable smart manufacturing.  
We predicted production quality and employed an integrated trained module for further use. In Section 1.3, we discuss the trend in IIoT technologies and review the PdM-related literature on manufacturing. In Section 2, we describe the primary ML algorithms used to analyze equipment data in a practical factory and the dataset and evaluation criteria. The results of our analyses are presented in Section 3. Section 4 discusses the impact of cloud data analytics and related matters. Finally, Section 5 concludes this study, describing the study's significance and limitations and providing suggestions for future work.