Data-Driven Framework for Tool Health Monitoring and Maintenance Strategy for Smart Manufacturing

Predictive Maintenance and Intelligent Sensors in Smart Factory: Review

“…The second cluster is focused on Cloud-related technologies. It means that sensors based on RFID [ 92 ] and programmable logic controller [ 107 ] are used for cloud or edge computing [ 99 ] and analysis of big data [ 109 ]. The third cluster concerned the IoT technologies based on CPS systems [ 38 ], SCADA [ 94 ], and data for deep and machine learning.…”

“…These sensors use magnetic, ultrasonic, or thermal detectors to monitor the current intensity in the pipeline. Chien and Chen [ 109 ] used mass flow controllers for monitoring mode and position of silane reactant flow. Their research is related to a data-driven framework for monitoring equipment’s health condition (RUL).…”

“…Results of the analysis must lead to conclusions, which requires a properly chosen tool for data visualisation. Other possible research directions are related to virtual metrology [ 109 ].…”

“…Chien and Chen [109] This study developed an effective approach that integrated partial least square and exponentially weighted moving averages approaches for tool health status monitoring and prediction to effectively derive the optimal maintenance strategies via transforming and analysing big data collected from the sensors, including the SVID data and FDC (fault detection and classification) parameters.…”

“…Method Data Source Al-Jlibawi et al [107] simulation DCS (distributed control systems), PLC, or SCADA in refinery Barbieri et al [99] case study Alternating current (AC) motor (machinery), Pronistia dataset Bekar et al [108] case study Machine motor Farooq et al [38] case study SCADA in spinning factory, spinning frame JWF1562 Goodall et al [92] simulation RFID in remanufacturing facility Chien and Chen [109] case study health status of plasma enhanced chemical vapor deposition (PECVD) chamber tool in TFT(thin film transistor) and LCD (liquid crystal display) company Kiangala and Wang [94] experiment SCADA, conveyor motors Kozlowski et al [110] case study CNC cutter machine sensors for milling of thin-walled aircraft engine components Kumar et al [102] case study CNC machine sensors Lao et al [111] simulation chemical product concentration and temperature profiles Li et al [96] experiment test data from IoT devices and detectors Lin et al [103] experiment test data from IoT in smart factory Musselman and Djurdjanovic [104] experiment automated storage/retrieval systems (belt-driven material handling device) in semiconductor industry Park et al [112] experiment servo motor testing data in smart factory Peng et al [113] experiment NI-PXI (PCI extensions for instrumentation) and NI-Compact data acquisition from production lines in China Steel Corporation Peng and Tsan [98] experiment production line machines Sadiki et al [105] case study industrial machine behaviour Shan et al [114] simulation welding robot in automotive production line Tarashioon et al [115] experiment LED (light-emitting diode) lighting system technologies Tsao et al [116] simulation production system and production lines Uhlmann et al [117] experiment ball and screw monitoring of machine tools Villalobos et al [118] case study melting and extruder machines in plastic bottles production plant (Capital Equipment Manufacturer) Vlasov et al [119] case study the supporting bearing of electric machines (AC motors)…”