Feature engineering in big data analytics for IoT-enabled smart manufacturing – Comparison between deep learning and statistical learning

Review on Fault Detection and Diagnosis Feature Engineering in Building Heating, Ventilation, Air Conditioning and Refrigeration Systems

“…Online dynamic automated feature engineering and fault detection and diagnosis analysis should be investigated in-depth. Owing to the potentially large computational costs, these methods should be executed using decentralized computing algorithms and cloud platforms within the Internet of Things [301]. This is especially true for future building energy systems with the advent of the new big-data era.…”

Use of Big Data Tools and Industrial Internet of Things: An Overview

“…e proposed framework is beneficial for additive manufacturing industry leaders to take the right decision at the beginning stage of the product life cycle [37]. e big data characteristic of the testbed was studied by using an inhouse-developed IoT-enabled manufacturing testbed [38]. A distributed service-oriented architecture was provided for the solution of problem of product tracing [39].…”

“…Figure 9 shows the article types and percentages of publication in the Springer library. [9] Big data analytics tool based on statistical process monitoring for smart manufacturing 2 [11] Multimedia big data computation and applications of IoT 3 [12] IoT, big data, and HPC-based smart flood management framework 4 [15] Big data analytics for manufacturing processes 5 [17] An algorithmic implementation of entropic ternary reduct soft sentiment set using soft computing technique on big data sentiment analysis for optimal selection of a decision based on real-time update in online reviews 6 [18] Architecture for Cognitive IoT and big data 7 [20] Challenges and opportunities for publishing IIoT data in manufacturing 8 [21] A comprehensive review of big data analytics throughout product life cycle to support sustainable smart manufacturing 9 [22] Role of big data analytics in IIoT 10 [23] Big data and natural environment 11 [30] Intelligent manufacturing production line data monitoring system for IIoT 12 [31] A secure and efficient data sharing scheme based on blockchain in IIoT 13 [32] Data management techniques for IoT 14 [33] Scalable data pipeline architecture to support the IIoT 15 [34] Industry 4.0-based process data analytics platform 16 [35] Optimization of IIoT data processing latency 17 [36] Big data and stream processing platforms for Industry 4.0 requirements mapping for a predictive maintenance use case 18 [37] Framework of big data for sustainable and smart additive manufacturing 19 [38] Feature engineering in big data analytics for IoT-enabled smart manufacturing 20 [39] An architecture for aggregating information from distributed data nodes for IIoT 21 [40] Application of big data analysis technique on high-velocity airblast atomization 22 [23] Interactive data exploration as a service for the smart factory 23 [41] Smart city services using machine learning, IoT, and big data 24 [43] Digital forensics challenges to big data in the cloud 25 [44] On fault prediction based on industrial big data 26 [45] Apache spark-based distributed self-organizing map algorithm for sensor data analysis 27…”

The investigation on infoecology in the field of smart manufacturing

“…They studied about the planned investments in SMS and its performance dimensions. Shah et al (2020), Epureanu et al (2020), Zhang et al (2020a), Alfeo et al (2020) and Lin et al (2020) studied about application deep learning in SM. Soualhi et al (2020), Bagheri et al (2020) and Bragazzi (2020) studied the application of SM in the health care system.…”