Leakage Error Compensation in Motor Current Signature Analysis for Shaft Misalignment Detection in Submersible Pumps

Afrizal, Nurafnida; Ferrero, Roberto

doi:10.1109/tim.2020.2998301

Cited by 15 publications

(5 citation statements)

References 20 publications

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“…When compared to other studies, the main contributions of this research can be summarized as follows: (i) approach capable of estimating load torque even for 1.5% of the nominal load; (ii) a computational model capable of generalizing low-sized images even with just 5 and 10 training epochs; and (iii) an approach capable of estimating load torque without needing other features than magnetic flux. Although this study presents a novel approach for estimating load torque in TIM, yielding good results, future research avenues could enhance its applicability by applying this proposed methodology considering the TIM running under failure conditions, such as broken rotor bars [ 36 ], shaft imbalance [ 37 ], and angular misalignment [ 38 ]. Furthermore, investigations regarding the performance of the model under various ranges of power supply frequency, for instance spanning from 0 to 60 Hz, could be conducted.…”

Section: Discussionmentioning

confidence: 99%

Image-Based Approach Applied to Load Torque Estimation in Three-Phase Induction Motors

Dias,

Fontenele

2024

Sensors

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This paper presents a novel method for load torque estimation in three-phase induction motors using air gap flux measurement and the conversion of this type of time-domain signal into grayscale images for further processing as inputs for an inception-type convolutional neural network. The magnetic flux was measured employing a Hall effect sensor installed inside the machine, near the stator slots, and above the stator windings. In this case, the sensor was able to measure a resultant magnetic flux density, having both rotor and stator magnetic flux contributions. The present methodology does not require motor parameters for torque prediction. The proposed approach successfully estimated load torque using three optimizers across almost the entire motor load operational range, spanning from 1.5% to 93.9% of the rated load. Four model configurations achieved a mean absolute percentage error (MAPE) less than or equal to 3.7%. Specifically, two models for a 40 × 50 pixel image achieved MAPE of 3.7% and 3%, one model for a 40 × 25 pixel image achieved a MAPE of 3.5%, and one model for a 50 × 80 pixel image achieved a MAPE of 3.3%. This research has been experimentally validated with a 7.5 kW squirrel cage induction machine.

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Section: Discussionmentioning

confidence: 99%

Image-Based Approach Applied to Load Torque Estimation in Three-Phase Induction Motors

Dias,

Fontenele

2024

Sensors

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“…The PDFs of rotating speeds from both test rigs obey the Gaussian distribution. Literally, spectral leakage happens in the discrete Fourier transform due to the non-integer number of periods of a periodic signal [11]. The leakage concept is extended to this specific phenomenon because of the quick random oscillation of the signal components.…”

Section: A Squeezed Modulation Signal Bispectrum Methods For Motor Cu...mentioning

confidence: 99%

A Squeezed Modulation Signal Bispectrum Method for Motor Current Signals Based Gear Fault Diagnosis

Tang

Sun

et al. 2022

IEEE Trans. Instrum. Meas.

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Electromechanical systems as the prime power source are widely employed in industry. To ensure the high productivity and safety of the motor-gear system, motor current signature analysis (MCSA) becomes a cost-effective and effective approach to health condition monitoring of motors and gears simultaneously. Generally, working conditions of the motor-gear system can be separated into stationary and nonstationary working conditions. The nonstationary working conditions usually refer to varying speeds, which have been broadly investigated in the angular domain analysis and time-frequency analysis. The stationary working conditions are assumed to have a constant rotating speed which is an ideal scenario but practically the rotating speed varies slightly with randomness. The random speed variation seems neglectable but it actually spreads the energy into adjacent frequency bins, which thus attenuates the amplitude of fault signatures and leads to inaccurate fault diagnosis. To address this issue, a Squeezed Modulation Signal Bispectrum (MSB) approach is developed to concentrate the leaked energy for accurately diagnosing gear faults with motor current signals. The Squeezed MSB concentrates the energy in the frequency domain along the time axis to overcome the random speed oscillation induced energy leakage and then, demodulates and aligns the modulation fault signatures from the squeezed spectra for ensemble averaging to further enhance fault signatures. The simulation study shows the performance of the proposed method under different levels of random speed variation and the experimental studies demonstrate the effectiveness of the Squeezed MSB for diagnosing gear tooth breakage faults under a wide range of working conditions.

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“…It is theoretical that in the absence of a harmonic, we can see that there is only one peak in the frequency spectrum. Current conversion from time to frequency area is achieved using the Fast Fourier Transform (FFT) algorithm [18]- [19].…”

Section: Fig 3 Mcsa Data Collection Process When Monitoring the Induction Motormentioning

confidence: 99%

Analysis of Induction Motor Performance Using Motor Current Signature Analysis Technique

Syahputra

Purwanto

Wiyagi

et al. 2021

JET

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This paper discusses the analysis of the performance of an induction motor using the motor current signature analysis (MCSA) technique. Induction motor is a type of electric machine that is widely used in industry. One of the industries that utilize induction motors is a steam power plant (SPP). The role of induction motors is very vital in SPP operations. Therefore, it is necessary to monitor the performance, stability, and efficiency to anticipate disturbances that can cause damage or decrease the life of the induction motor. MCSA is a reliable technique that can be used to analyze damage to an induction motor. In this technique, the induction motor current signal is detected using a current transducer. The signal is then passed on to the signal conditioning and then into the data acquisition device. The important signal data is analyzed in adequate computer equipment. The results of this analysis determine the condition of the induction motor, whether it is normal or damaged. In this research, a case study was carried out at the Rembang steam power plant, Central Java, Indonesia. The results of the analysis of several induction motors show that most of them are in normal conditions and are still feasible to operate.

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Leakage Error Compensation in Motor Current Signature Analysis for Shaft Misalignment Detection in Submersible Pumps

Cited by 15 publications

References 20 publications

Image-Based Approach Applied to Load Torque Estimation in Three-Phase Induction Motors

Image-Based Approach Applied to Load Torque Estimation in Three-Phase Induction Motors

A Squeezed Modulation Signal Bispectrum Method for Motor Current Signals Based Gear Fault Diagnosis

Analysis of Induction Motor Performance Using Motor Current Signature Analysis Technique

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