Explainable Thermographic Fault Diagnosis of Three-Phase Induction Motors Using Transient Thermal Signatures: A Case Study

Infrared thermography enables non-contact monitoring of induction motor thermal behavior, but absolute temperature alone may not distinguish faults with similar surface heating. This paper presents a proof-of-concept case study on the explainable thermographic diagnosis of three-phase induction motors using transient thermal signatures. Two faults were imposed on the same Siemens 1LA2080-4AA10 squirrel-cage motor: loss of forced ventilation (hereafter, cooling failure) and a resistive-bank-induced phase unbalance condition denoted in the test bench as 50% phase unbalance. The approach combines motor-specific regions of interest, transient thermal descriptors, hot area expansion, first-order thermal modeling, healthy baseline residuals, and two physically motivated indices: the Cooling Failure Index (CFI) and Phase Unbalance Thermal Index (PUTI). Cooling failure was analyzed from radiometric CSV data, whereas phase unbalance was evaluated from color-mapped thermal video through scale-based temperature reconstruction and is therefore interpreted as an estimated thermal signature. For the baseline self-reference consistency check, the residual-based fault flag remained false. Cooling failure increased the maximum radiometric temperature from 77.2 °C to 91.6 °C, with 43,399 pixels above 80 °C. Phase unbalance showed a localized stator-dominated rise without hot area expansion above 80 °C in the reconstructed sequence. The rule-based layer assigned high CFI to cooling failure and high PUTI to phase unbalance, supporting explainable case-study-based discrimination while avoiding claims of general classifier validation.

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Publication Details

Journal
Machines
Published
2026-07-26
DOI
https://doi.org/10.3390/machines14080843
Primary Topic
Machine Fault Diagnosis Techniques
Type
article
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article

Explainable Thermographic Fault Diagnosis of Three-Phase Induction Motors Using Transient Thermal Signatures: A Case Study

Larisa Dunai, Pedro Fernández de Córdoba, Miguel Martínez, Jose A. Antonino‐Daviu et al.
Machines
Machine Fault Diagnosis Techniques
article

Explainable Thermographic Fault Diagnosis of Three-Phase Induction Motors Using Transient Thermal Signatures: A Case Study

Larisa Dunai, Pedro Fernández de Córdoba, Miguel Martínez, Jose A. Antonino‐Daviu, Humberto Michinel, J. Alberto Conejero, M. J. Picazo-Rodenas
article en

Abstract

Infrared thermography enables non-contact monitoring of induction motor thermal behavior, but absolute temperature alone may not distinguish faults with similar surface heating. This paper presents a proof-of-concept case study on the explainable thermographic diagnosis of three-phase induction motors using transient thermal signatures. Two faults were imposed on the same Siemens 1LA2080-4AA10 squirrel-cage motor: loss of forced ventilation (hereafter, cooling failure) and a resistive-bank-induced phase unbalance condition denoted in the test bench as 50% phase unbalance. The approach combines motor-specific regions of interest, transient thermal descriptors, hot area expansion, first-order thermal modeling, healthy baseline residuals, and two physically motivated indices: the Cooling Failure Index (CFI) and Phase Unbalance Thermal Index (PUTI). Cooling failure was analyzed from radiometric CSV data, whereas phase unbalance was evaluated from color-mapped thermal video through scale-based temperature reconstruction and is therefore interpreted as an estimated thermal signature. For the baseline self-reference consistency check, the residual-based fault flag remained false. Cooling failure increased the maximum radiometric temperature from 77.2 °C to 91.6 °C, with 43,399 pixels above 80 °C. Phase unbalance showed a localized stator-dominated rise without hot area expansion above 80 °C in the reconstructed sequence. The rule-based layer assigned high CFI to cooling failure and high PUTI to phase unbalance, supporting explainable case-study-based discrimination while avoiding claims of general classifier validation.

MachinesVol. 14(8)
Universitat de València (ES), Instituto Tecnológico de la Energía (ES), Universitat Politècnica de València (ES), Universidade de Vigo (ES)
Peace, Justice and strong institutions
Openalex Percentile: Top 11%
Machine Fault Diagnosis Techniques
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