ArticleSensors (Basel, Switzerland)2018
Fault Diagnosis of Induction Machines in a Transient Regime Using Current Sensors with an Optimized Slepian Window.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- An efficient bearing fault detection strategy based on a hybrid machine learning technique.Scientific reports · 2025Article
- Current and Stray Flux Combined Analysis for Sparking Detection in DC Motors/Generators Using Shannon Entropy.Entropy (Basel, Switzerland) · 2024Article
- Induction Motor Stator Winding Inter-Tern Short Circuit Fault Detection Based on Start-Up Current Envelope Energy.Sensors (Basel, Switzerland) · 2023Article
- Mechanical Fault Diagnostic in PMSM from Only One Current Measurement: A Tacholess Order Tracking Approach.Sensors (Basel, Switzerland) · 2020Article
- Fault Diagnosis in the Slip-Frequency Plane of Induction Machines Working in Time-Varying Conditions.Sensors (Basel, Switzerland) · 2020Article
- Heat Source Forecast of Ball Screw Drive System Under Actual Working Conditions Based on On-Line Measurement of Temperature Sensors.Sensors (Basel, Switzerland) · 2019Article
- Cost-Effective Reduced Envelope of the Stator Current via Synchronous Sampling for the Diagnosis of Rotor Asymmetries in Induction Machines Working at Very Low Slip.Sensors (Basel, Switzerland) · 2019Article
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5 authors.
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Abstract
The aim of this paper is to introduce a new methodology for the fault diagnosis of induction machines working in the transient regime, when time-frequency analysis tools are used. The proposed method relies on the use of the optimized Slepian window for performing the short time Fourier transform (STFT) of the stator current signal. It is shown that for a given sequence length of finite duration, the Slepian window has the maximum concentration of energy, greater than can be reached with a gated Gaussian window, which is usually used as the analysis window. In this paper, the use and optimization of the Slepian window for fault diagnosis of induction machines is theoretically introduced and experimentally validated through the test of a 3.15-MW induction motor with broken bars during the start-up transient. The theoretical analysis and the experimental results show that the use of the Slepian window can highlight the fault components in the current's spectrogram with a significant reduction of the required computational resources.
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