• Mr. M.Pavan Babu
• Mr. Ch. Srinivasa Reddy

In today's interconnected world, manufacturers unwilling to embrace the Industrial Internet of Things (IIoT) risk falling behind in the ever-evolving landscape of smart manufacturing. The pervasive influence of the Internet of Things (IoT) has ushered in a new era of intelligent manufacturing applications, underpinned by adaptive intelligence and artificial intelligence. This transformative wave not only presents opportunities for substantial cost savings within manufacturing processes but also heralds the prospect of eradicating costly machine downtime. The linchpin to averting disruptive downtime lies in proactive maintenance planning, a strategic imperative for estimating the operational lifespan of items, components, or systems. The ramifications of operating units consuming excessive energy necessitate a nuanced approach to enhancing efficiency, where even marginal improvements wield substantial influence over operational costs and overall energy consumption. To address these challenges, the integration of Equipment Health Monitoring and Prediction technology with AI-based applications emerges as a pivotal solution. This amalgamation harnesses embedded human knowledge and advanced engineering automation, empowering factories to proactively address issues and align with the dynamic demands of the burgeoning smart manufacturing sector. Notably, this technology becomes a crucial ally in mitigating two primary adversaries of the manufacturing industry: equipment failure and downtime. At the heart of this innovation is a learning algorithm, diligently identifying the most impactful and ineffective parameters within diverse sensor data sets. Leveraging the wealth of data available, businesses can navigate their systems towards optimal performance. Extracting meaningful insights from datasets becomes imperative, laying the foundation for enhancing the efficacy of machine learning algorithms. In essence, manufacturers standing at the crossroads of technological evolution must recognize the imperative of integrating IIoT, AI, and predictive technologies. This convergence not only safeguards against obsolescence but propels enterprises into a realm of heightened efficiency, cost-effectiveness, and resilience in the face of ever-evolving industrial landscapes. The era of smart manufacturing demands a paradigm shift, and those who fail to embrace it risk being relegated to the sidelines of progress.

Equipment Health Monitoring, Internet Of Things, Artificial Intelligence, Energy Consumption.

"Quick Autonomous Projection for Massive Manufacturing Data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 2, page no.e345-e351, February-2024, Available :http://www.jetir.org/papers/JETIR2402451.pdf

"Quick Autonomous Projection for Massive Manufacturing Data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 2, page no. ppe345-e351, February-2024, Available at : http://www.jetir.org/papers/JETIR2402451.pdf