Optimized Spectral Fault Receptive Fields for Diagnosis-Informed Prognosis

TL;DR

This paper presents Spectral Fault Receptive Fields (SFRFs), a biologically inspired spectral feature extraction method optimized for fault diagnosis and RUL prediction in machinery, demonstrating improved early fault detection and prognosis accuracy.

Contribution

The paper introduces SFRFs inspired by retinal biology, combined with an evolutionary optimization framework for enhanced fault diagnosis and RUL estimation in rotating machinery.

Findings

SFRFs effectively detect early-stage faults in vibration signals.

Optimized SFRFs improve RUL prediction accuracy.

Method confirms robustness under variable operating conditions.

Abstract

This paper introduces Spectral Fault Receptive Fields (SFRFs), a biologically inspired technique for degradation state assessment in bearing fault diagnosis and remaining useful life (RUL) estimation. Drawing on the center-surround organization of retinal ganglion cell receptive fields, we propose a frequency-domain feature extraction algorithm that enhances the detection of fault signatures in vibration signals. SFRFs are designed as antagonistic spectral filters centered on characteristic fault frequencies, with inhibitory surrounds that enable robust characterization of incipient faults under variable operating conditions. A multi-objective evolutionary optimization strategy based on NSGA-II algorithm is employed to tune the receptive field parameters by simultaneously minimizing RUL prediction error, maximizing feature monotonicity, and promoting smooth degradation trajectories. The method is demonstrated on the XJTU-SY bearing run-to-failure dataset, confirming its suitability for constructing condition indicators in health monitoring applications. Key contributions include: (i) the introduction of SFRFs, inspired by the biology of vision in the primate retina; (ii) an evolutionary optimization framework guided by condition monitoring and prognosis criteria; and (iii) experimental evidence supporting the detection of early-stage faults and their precursors. Furthermore, we confirm that our diagnosis-informed spectral representation achieves accurate RUL prediction using a bagging regressor. The results highlight the interpretability and principled design of SFRFs, bridging signal processing, biological sensing principles, and data-driven prognostics in rotating machinery.