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The fault diagnosis of electrical machines during startup transients has received increasing attention regarding the possibility of detecting faults early. Induction motors are no exception, and motor current signature analysis has become one of the most popular techniques for determining the condition of various motor components. However, in the case of inverter powered systems, the condition of a motor is difficult to determine from the stator current because fault signatures could overlap with other signatures produced by the inverter, low-slip operation, load oscillations, and other non-stationary conditions. This paper presents a speed signature analysis methodology for a reliable broken rotor bar diagnosis in inverter-fed induction motors. The proposed fault detection is based on tracking the speed fault signature in the time-frequency domain. As a result, different fault severity levels and load oscillations can be identified. The promising results show that this technique can be a good complement to the classic analysis of current signature analysis and reveals a high potential to overcome some of its drawbacks.
Tomas Garcia-Calva; Daniel Morinigo-Sotelo; Vanessa Fernandez-Cavero; Arturo Garcia-Perez; Rene Romero-Troncoso. Early Detection of Broken Rotor Bars in Inverter-Fed Induction Motors Using Speed Analysis of Startup Transients. Energies 2021, 14, 1469 .
AMA StyleTomas Garcia-Calva, Daniel Morinigo-Sotelo, Vanessa Fernandez-Cavero, Arturo Garcia-Perez, Rene Romero-Troncoso. Early Detection of Broken Rotor Bars in Inverter-Fed Induction Motors Using Speed Analysis of Startup Transients. Energies. 2021; 14 (5):1469.
Chicago/Turabian StyleTomas Garcia-Calva; Daniel Morinigo-Sotelo; Vanessa Fernandez-Cavero; Arturo Garcia-Perez; Rene Romero-Troncoso. 2021. "Early Detection of Broken Rotor Bars in Inverter-Fed Induction Motors Using Speed Analysis of Startup Transients." Energies 14, no. 5: 1469.
In this work, a new time-frequency tool based on minimum-norm spectral estimation is introduced for multiple fault detection in induction motors. Several diagnostic techniques are available to identify certain faults in induction machines; however, they generally give acceptable results only for machines operating under stationary conditions. Induction motors rarely operate under stationary conditions as they are constantly affected by load oscillations, speed waves, unbalanced voltages, and other external conditions. To overcome this issue, different time-frequency analysis techniques have been proposed for fault detection in induction motors under non-stationary regimes. However, most of them have low-resolution, low-accuracy or both. The proposed method employs the minimum-norm spectral estimation to provide high frequency resolution and accuracy in the time-frequency domain. This technique exploits the advantages of non-stationary conditions, where mechanical and electrical stresses in the machine are higher than in stationary conditions, improving the detectability of fault components. Numerical simulation and experimental results are provided to validate the effectiveness of the method in starting current analysis of induction motors.
Tomas A. Garcia-Calva; Daniel Morinigo-Sotelo; Oscar Duque-Perez; Arturo Garcia-Perez; Rene De J. Romero-Troncoso. Time-Frequency Analysis Based on Minimum-Norm Spectral Estimation to Detect Induction Motor Faults. Energies 2020, 13, 4102 .
AMA StyleTomas A. Garcia-Calva, Daniel Morinigo-Sotelo, Oscar Duque-Perez, Arturo Garcia-Perez, Rene De J. Romero-Troncoso. Time-Frequency Analysis Based on Minimum-Norm Spectral Estimation to Detect Induction Motor Faults. Energies. 2020; 13 (16):4102.
Chicago/Turabian StyleTomas A. Garcia-Calva; Daniel Morinigo-Sotelo; Oscar Duque-Perez; Arturo Garcia-Perez; Rene De J. Romero-Troncoso. 2020. "Time-Frequency Analysis Based on Minimum-Norm Spectral Estimation to Detect Induction Motor Faults." Energies 13, no. 16: 4102.
Fault detection of rotatory machinery under nonstationary conditions is a topic increasing importance in industrial applications. Induction motors are not the exception and monitoring electric motor current has become a standard option to determine the health of several motor parts. However, in the inverter-fed motor case, the health condition of a motor is difficult to diagnose from electric motor current in certain cases. This paper explores the analysis of instantaneous speed during startup transient operation as a way to enhance the reliability for rotor fault detection. An experimental study of a rotor fault time-frequency evolution is presented. Results are promising and show high potential to overcome some important drawbacks of the classical current signature analysis to track fault-related signatures.
Tomas Alberto Garcia-Calva; Daniel Morinigo-Sotelo; Arturo Garcia-Perez; R. De J. Romero-Troncoso. Rotor Fault Detection in Inverter-Fed Induction Motors Using Speed Analysis of Startup Transient. 2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) 2019, 297 -302.
AMA StyleTomas Alberto Garcia-Calva, Daniel Morinigo-Sotelo, Arturo Garcia-Perez, R. De J. Romero-Troncoso. Rotor Fault Detection in Inverter-Fed Induction Motors Using Speed Analysis of Startup Transient. 2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED). 2019; ():297-302.
Chicago/Turabian StyleTomas Alberto Garcia-Calva; Daniel Morinigo-Sotelo; Arturo Garcia-Perez; R. De J. Romero-Troncoso. 2019. "Rotor Fault Detection in Inverter-Fed Induction Motors Using Speed Analysis of Startup Transient." 2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) , no. : 297-302.
Transient motor current signature analysis has become a mature technique for fault detection in induction motors. By using start-up transients, the whole range of slip in the machine is exploited to generate well-defined fault frequency patterns. However, in the inverter-fed motor case, the fault-patterns are always close to the supply frequency and often of low amplitude. Therefore, it is difficult to distinguish and localize the fault-patterns. In this paper, a novel method is proposed to create a new fault-pattern; the proposed technique can concentrate the fault-harmonic in a specific frequency bandwidth and avoid the spectral leakage by reducing the supply frequency amplitude. The methodology has been validated through experimental tests carried out to detect broken rotor bar in an induction motor started through a voltage source inverter.
Tomas Alberto Garcia-Calva; Daniel Morinigo-Sotelo; Arturo Garcia-Perez; David Camarena-Martinez; Rene De Jesus Romero-Troncoso. Demodulation Technique for Broken Rotor Bar Detection in Inverter-Fed Induction Motor Under Non-Stationary Conditions. IEEE Transactions on Energy Conversion 2019, 34, 1496 -1503.
AMA StyleTomas Alberto Garcia-Calva, Daniel Morinigo-Sotelo, Arturo Garcia-Perez, David Camarena-Martinez, Rene De Jesus Romero-Troncoso. Demodulation Technique for Broken Rotor Bar Detection in Inverter-Fed Induction Motor Under Non-Stationary Conditions. IEEE Transactions on Energy Conversion. 2019; 34 (3):1496-1503.
Chicago/Turabian StyleTomas Alberto Garcia-Calva; Daniel Morinigo-Sotelo; Arturo Garcia-Perez; David Camarena-Martinez; Rene De Jesus Romero-Troncoso. 2019. "Demodulation Technique for Broken Rotor Bar Detection in Inverter-Fed Induction Motor Under Non-Stationary Conditions." IEEE Transactions on Energy Conversion 34, no. 3: 1496-1503.
Fault detection in inverter-fed induction motors (IMs) is an actual industrial need. Many line-fed machines are being replaced by inverter-fed drives for improving control during startup and also for saving energy. Broken rotor bars (BRBs) in IMs is one of the most difficult faults to be detected, particularly when the motor is fed by an inverter in a soft startup. The difficulty of detecting BRBs is that the characteristic fault-related frequencies are very close to the fundamental frequency, and the amplitude of the fundamental is significantly higher than the fault-related frequency components. This paper proposes an effective method that allows the detection of the BRB fault in inverter-fed IMs during a soft startup transient based on a non-uniform resampling algorithm. The proposed algorithm transforms the nonstationary fundamental frequency into a stationary component by non-uniform resampling, whereas the fault-related components are considerably separated from the fundamental one, making easier to follow their evolution during the startup transient. Simulation and experimental results demonstrate the effectiveness of the proposed method to detect the fault.
Tomas Alberto Garcia-Calva; Daniel Morinigo-Sotelo; Rene Romero-Troncoso. Non-Uniform Time Resampling for Diagnosing Broken Rotor Bars in Inverter-Fed Induction Motors. IEEE Transactions on Industrial Electronics 2016, 64, 2306 -2315.
AMA StyleTomas Alberto Garcia-Calva, Daniel Morinigo-Sotelo, Rene Romero-Troncoso. Non-Uniform Time Resampling for Diagnosing Broken Rotor Bars in Inverter-Fed Induction Motors. IEEE Transactions on Industrial Electronics. 2016; 64 (3):2306-2315.
Chicago/Turabian StyleTomas Alberto Garcia-Calva; Daniel Morinigo-Sotelo; Rene Romero-Troncoso. 2016. "Non-Uniform Time Resampling for Diagnosing Broken Rotor Bars in Inverter-Fed Induction Motors." IEEE Transactions on Industrial Electronics 64, no. 3: 2306-2315.