IntelliMaint An Intelligent Component-Agnostic Framework for Health Indicator Generation, and Prognostics for ElectroMechanical Systems
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Abstract
Predictive maintenance of complex mechanical requires robust health monitoring capabilities that can generalize across diverse components and operating conditions. We present a novel component-agnostic framework that unifies Health Indicator (HI) generation and Remaining Useful Life (RUL) prediction through an integrated pipeline comprising: (1) advanced feature engineering, (2) unsupervised health baseline modelling, (3) monotonicity and trendability learning (4) probabilistic degradation detection with confidence-aware RUL estimation.
We validate our framework on two distinct Industrial Case Studies: Firstly, Tool wear monitoring in CNC machine using vibration and spindle current data collected from the real production machine. Our framework achieves early degradation detection of tool life with RUL prediction within ±15% of actual failure time. Flank wear (VB) was measured as a standard parameter for evaluating tool wear. Secondly, bearing degradation assessment using the IMS bearing dataset. This validation demonstrates fault detection 40% earlier than traditional threshold methods with 90% confidence intervals.
Both case studies show strong HI monotonicity (>85%) and reliable uncertainty quantification, establishing the foundation for scalable, explainable predictive maintenance solutions. The framework's component-agnostic design enables rapid deployment across heterogeneous assets without extensive reconfiguration, while its interpretable architecture facilitates root cause analysis and maintenance decision support. These results demonstrate significant advances in scalable, explainable predictive maintenance, offering practitioners a unified solution for diverse industrial health monitoring challenges.
How to Cite
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FlankWear, Remaining Useful Life, Tool Wear, Industrial Application, Health Indicator, Diagnostics, Prognostics, Predictive Maintenance
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