Develops an integrated estimation-prognosis-reliability framework for induction-motor stator inter-turn faults that exploits the affine severity and spatial periodicity structure via an augmented state observer.
The paper "From Winding-Fault Geometry to Reliability: Estimation and Prognosis of Stator Inter-Turn Faults" (arXiv:2609.13448, published September 11, 2026) by Bambang L. Widjiantoro, Syahrul Munir, Katherin Indriawati, and Moh Kamalul Wafi develops an integrated framework for stator inter-turn fault management in induction motors.
Core Methodology Geometric Modeling: The fault is characterized by fault severity ($\eta_{cc}$, fraction of short-circuited turns) and spatial orientation ($\gamma_{cc}$). The model exploits the fact that the fault-induced output signature is affine in fault severity and exhibits spatial periodicity. Augmented-State Particle Filter: An augmented-state particle filter jointly estimates the nonlinear electromechanical state and the unknown fault severity, providing posterior uncertainty quantification while maintaining physical constraints ($\eta_{cc} \in [0,1]$). * Prognosis Mechanisms: The estimated degradation is propagated using four models: linear trend, Holt exponential smoothing, Bayesian degradation, and particle-based forecasting, enabling both deterministic and probabilistic predictions.
Reliability Assessment The framework connects prognostic forecasts to four complementary reliability measures: 1. Threshold-Crossing Remaining Useful Life (RUL). 2. First-Passage Reliability. 3. Degradation-Dependent Hazard Reliability. 4. Weibull Lifetime Benchmark.
Key Findings The framework achieves accurate online fault estimation and output reconstruction across distinct fault orientations. It provides a unified probabilistic pipeline from online diagnosis to forward-looking health assessment, maintaining consistent reliability predictions under gradual, accelerating, and abrupt degradation patterns.
Scope. The material addresses stator inter-turn faults in induction motors, an early-stage winding insulation failure in which one or more turns are shorted. Rather than treating the fault only as a generic anomaly, it frames the problem through winding geometry: the severity of the short and its position within the periodic stator winding determine how the fault alters the motor’s electrical behavior and what signatures become observable.
Key contribution. The paper develops an integrated estimation–prognosis–reliability framework centered on an augmented state observer. It exploits two structural properties: the fault severity enters the motor model affinely, and the stator winding has a spatially periodic layout. These properties allow the observer to estimate the evolving fault severity from electrical measurements, and the estimated severity trajectory can then be used to forecast degradation and quantify reliability or failure risk.
Why it matters. This work bridges condition monitoring, physics-based fault modeling, and reliability engineering for electric machines. By making inter-turn fault severity explicitly estimable and prognosticatable from winding-structure-aware models, it supports earlier intervention, remaining-useful-life estimation, and risk-based maintenance decisions before localized turn-to-turn faults progress into more severe insulation, thermal, or mechanical damage.