# Cloud-Based Analytics Module for Predictive Maintenance of the Textile Manufacturing Process
### Current Trends in IIoT and PdM  
As per an International Data Corporation (IDC) report, the global sales of IoT-related technologies reached USD 726 billion in 2019 and are expected to reach USD 1.1 trillion in 2023, a 51% increase, based on the latest forecast by IDC, with manufacturing operations accounting for 13% market share [11]. IIoT is projected to rapidly grow with the market size reaching USD 949 billion by 2025, as per a report by Grand View Research [12]. Worldwide spending on IIoT-related software and hardware is projected to increase from USD 68.8 billion in 2019 to USD 98.2 billion in 2024 as per a report by a market research firm [13]. Most IoT market reports indicate a significant potential for IoT technologies and predict growth for the rapidly developing IIoT market. IIoT-related technologies include data connection, cyber networks, cloud platforms, data storage, and software applications. [PERSON] et al. analyzed the effects of energy consumption on data processing and system design [14]. Other studies examined 5G communication technology for IIoT [15]. Recently, certain researchers have focused on data analytics to address IIoT challenges using computational approaches such as ML and data mining [16; 17]. Technologies for secure device communication have been employed in IIoT system development [18; 19], and many studies have been dedicated for deploying cloud systems in IoT application layers such as a Blockchain-based platform [20], production-logistics systems [21], and visualization applications [22]. IIoT technologies are associated with software, hardware, and firmware. IIoT systems include devices, networks, security, data analytics, and software applications. Most previous studies independently focused on one of the above issues. In this study, for PdM analysis, we combined data analytics and system design to develop a restful API. The high expense and complexities of maintaining the sophisticated equipment used in modern manufacturing drive the demand for enhanced production efficiency. PdM for Industry 4.0 involves the prediction of equipment failure by analyzing data (e.g., equipment, production, and environmental data) to identify patterns and predict issues before anomalies are noticed. PdM has attracted considerable attention in the manufacturing industry, and global companies, such as Microsoft, GE, and Bosch, already provide PdM solutions for manufacturing. We reviewed 1154 PdM-related studies for the last five years, thus analyzing the time trends and disciplinary distributions of emerging PdM-related topics. We reported that intelligence and automation are the primary concerns driving PdM-related studies with the most common forms of research being system design, systematic analysis, and critical review. High-frequency keywords for PdM-related studies include \"Cloud\", \"Industry\", \"IoT\", \"Big Data\", and \"Intelligent.\" For a better understanding of the PdM-related trend, we analyzed the comprehensive profile of PdM by reviewing studies from Google Scholar. Publish or Perish (PoP) software was used to survey relevant and related studies from 2010 to 2019. PdM is extensively employed in manufacturing processes (Table 1). PdM-related studies focused on equipment vibration and temperature monitoring [23], systemic failure of electric power transformers [24], remaining useful life prediction for machining equipment [25], optimization of maintenance decision variables [26], fault diagnosis [27], car maintenance [30], airline maintenance scheduling [31], and wind turbine maintenance [32]. Moreover, we examined existing cloud solutions for implementing PdM in a cloud-based ML module. The results show that cloud computing solutions offer convenient tools for users to analyze their equipment data and develop maintenance strategies. Recently, PdM has been employed for maintaining textile manufacturing equipment, including sewing machines [33], textile machines [34], and maintenance scheduling [35].