The CHINMAY (Condition-Based Health Intelligence for Neural Monitoring and Analytics Yield) Framework in Predictive Maintenance with TOEE–MOEE Metrics

Pranita Bhosale; Sangeeta Jadhav

doi:10.48084/etasr.16855

Authors

Pranita Bhosale Department of Electronics and Telecommunication Engineering, Dr. D. Y. Patil Institute of Technology, Pimpri, Savitribai Phule Pune University, Pune, Maharashtra, India | Department of Electronics & Telecommunication Engineering, Army Institute of Technology, Dighi Hills, Savitribai Phule Pune University, Pune, Maharashtra, India
Sangeeta Jadhav Department of Information & Technology Engineering, Army Institute of Technology, Dighi Hills, Savitribai Phule Pune University, Pune, Maharashtra, India

Volume: 16 | Issue: 3 | Pages: 35136-35142 | June 2026 | https://doi.org/10.48084/etasr.16855

Received: 10 December 2025 | Revised: 6 January 2026 and 12 January 2026 | Accepted: 14 January 2026 | Online: 19 April 2026

Corresponding author: Pranita Bhosale

Abstract

Modern-day industrial environments impose significant pressure on manufacturers to ensure product quality, minimize unforeseen breakdowns, and maintain continuous control over key performance indicators. These requirements have driven industries toward more advanced operational and maintenance strategies. In this context, Overall Equipment Effectiveness (OEE) has emerged as a widely adopted metric for assessing manufacturing efficiency. However, conventional OEE primarily provides a retrospective evaluation based on availability, speed, and defect-free output, offering limited insights into the underlying machine health and operational dynamics. This limitation becomes particularly critical in plastic extrusion plants, where operating conditions can vary rapidly, and nominal operation may abruptly transition into degradation without visible external symptoms. To address these challenges, the present study introduces the Condition-based Health Intelligence to Neural Monitoring and Analytics Yield (CHINMAY) framework as a novel, predictive extension of traditional OEE. Conceptualized as a cognitively enhanced framework, CHINMAY incorporates additional contextual dimensions, including machine functional health and stakeholder-driven operational requirements. The framework integrates machine learning and deep learning models to enable proactive diagnostics and early anomaly detection, thereby reducing unexpected disruptions and improving operational continuity. The proposed approach is validated using real-time experimental data obtained from a Plastic Extrusion Machine (EX06), demonstrating its applicability in an actual industrial environment. Through this implementation, the study aims to move beyond episodic and reactive evaluation practices toward a comprehensive Predictive Maintenance (PdM) paradigm. The CHINMAY framework facilitates early detection of equipment anomalies, leading to reduced material wastage and enhanced system reliability. Overall, CHINMAY allows transitioning from conventional reactive maintenance approaches to intelligent, proactive, and sustainable smart manufacturing practices.

Keywords:

CHINMAY (Condition-Based Health Intelligence for Neural Monitoring and Analytics Yield), TOEE (Traditional OEE), MOEE (Modified OEE), POEE (Predictive OEE), Data Driven Decision Making Structure

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