PERFORMANCE MONITORING AND RELIABLE OPERATIONS OF INDUSTRIAL AUTOMATION USING LOW COST EMBEDDED PLATFORMS.

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Title:	PERFORMANCE MONITORING AND RELIABLE OPERATIONS OF INDUSTRIAL AUTOMATION USING LOW COST EMBEDDED PLATFORMS.
Authors:	MALWATKAR, GAJANAN M.¹ gajanan.malwatkar@gcoej.ac.in, GUND, ANKUSH M.¹ ankush.gund@bvcoenm.edu.in, SHILEDAR, SHAKTIKUMAR R.² shaktikumar.shiledar@gcoej.ac.in
Source:	Reliability: Theory & Applications. Mar2026, Vol. 21 Issue 1, p369-382. 14p.
Subjects:	Process optimization, Embedded computer systems, Operating costs, Machine learning, Automation, Real-time computing
Abstract:	Industrial process optimization and cost reduction are achieved through the monitoring and advancement of automation systems using low cost embedded platforms. The system integration of affordable sensors, transducers, controllers, and advanced control algorithms enhances system efficiency, stability, and responsiveness. Scalable real time monitoring and optimization frameworks improve industrial productivity while minimizing downtime. The proposed implementation incorporates real time data acquisition, fractional order PID (FOPID) control, and mixed integer programming (MIP) based optimization to ensure efficient operation and adaptive performance evolution. The FOPID controller enhances control precision through fractional derivatives and integrals, while Ziegler Nichols tuning (ZNT) optimizes PID stability and response. Machine learning (ML) algorithms are used for data analysis and visualization of the plant parameters, supporting better insight into process dynamics and control decisions. The research demonstrates that the proposed architecture, developed using open-source software and low cost controller boards, effectively improves system performance when integrated with existing hardware. Validation of the approach was carried out using a multi loop process trainer setup available in the laboratory. Future work involves incorporating artificial intelligence (AI) for autonomous control and further enhancement of system adaptability. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

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Abstract:	Industrial process optimization and cost reduction are achieved through the monitoring and advancement of automation systems using low cost embedded platforms. The system integration of affordable sensors, transducers, controllers, and advanced control algorithms enhances system efficiency, stability, and responsiveness. Scalable real time monitoring and optimization frameworks improve industrial productivity while minimizing downtime. The proposed implementation incorporates real time data acquisition, fractional order PID (FOPID) control, and mixed integer programming (MIP) based optimization to ensure efficient operation and adaptive performance evolution. The FOPID controller enhances control precision through fractional derivatives and integrals, while Ziegler Nichols tuning (ZNT) optimizes PID stability and response. Machine learning (ML) algorithms are used for data analysis and visualization of the plant parameters, supporting better insight into process dynamics and control decisions. The research demonstrates that the proposed architecture, developed using open-source software and low cost controller boards, effectively improves system performance when integrated with existing hardware. Validation of the approach was carried out using a multi loop process trainer setup available in the laboratory. Future work involves incorporating artificial intelligence (AI) for autonomous control and further enhancement of system adaptability. [ABSTRACT FROM AUTHOR]
ISSN:	19322321
DOI:	10.24412/1932-2321-2026-190-369-382