Time Series Fault Prediction in Semiconductor Equipment Using Recurrent Neural Network

Nawaz, Javeria; Arshad, Muhammad Zeeshan; Hong, Sang Jeen

doi:10.1007/978-3-642-39068-5_56

Cited by 1 publication

(1 citation statement)

References 16 publications

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“…A number of researchers [40,41] have proposed the analysis of vibration data as a means by which to monitor the status of machines. In this study, we developed a monitoring system based on an inexpensive single chip controller, Arduino, in conjunction with triaxial accelerometers, infrared sensors, and temperature sensors.…”

Section: Add-on Sensors For the Collection Of Data Related To Machinery Statusmentioning

confidence: 99%

A Low-Cost Add-On Sensor and Algorithm to Help Small- and Medium-Sized Enterprises Monitor Machinery and Schedule Processes

et al. 2019

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Since the concept of Industry 4.0 emerged, an increasing number of major manufacturers have incorporated relevant technologies to monitor machinery and schedule processes so as to increase yield and optimize production. However, most machinery monitoring technologies are far too expensive for small- and medium-sized enterprises. Furthermore, the production processes at small- and medium-sized enterprises are simpler and can thus be optimized without excessively complex scheduling systems. This study therefore proposed the use of cheaper add-on sensors for monitoring machinery and integrated them with an algorithm that can more swiftly produce results that meet multiple objectives. The proposed algorithm is meant to extend the capabilities of small- and medium-sized enterprises in monitoring machinery and scheduling processes, thereby enabling them to contend on an equal footing with larger competitors. Finally, we performed an experiment at an actual spring enterprise to demonstrate the validity of the proposed algorithm.

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Section: Add-on Sensors For the Collection Of Data Related To Machinery Statusmentioning

confidence: 99%

A Low-Cost Add-On Sensor and Algorithm to Help Small- and Medium-Sized Enterprises Monitor Machinery and Schedule Processes

et al. 2019

View full text Add to dashboard Cite

show abstract

Time Series Fault Prediction in Semiconductor Equipment Using Recurrent Neural Network

Cited by 1 publication

References 16 publications

A Low-Cost Add-On Sensor and Algorithm to Help Small- and Medium-Sized Enterprises Monitor Machinery and Schedule Processes

A Low-Cost Add-On Sensor and Algorithm to Help Small- and Medium-Sized Enterprises Monitor Machinery and Schedule Processes

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