Faults diagnosis for electrical machines based on analysis of motor current

Iorgulescu, Mariana; Beloiu, Robert

doi:10.1109/optim.2014.6850944

Cited by 10 publications

(5 citation statements)

References 18 publications

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“…Some studies used a direct signal evaluation instead of a parameter estimation, as [13] and [14]. They use vibration and current signature analysis for different types of motors, including DC motors, for classifying as a healthy motor, a motor with a short-circuit in the commutator, and a motor with a displaced permanent magnet out of poles in the polar axis.…”

Section: Armature Faults Inspection Problemmentioning

confidence: 99%

Armature fault diagnosis of universal motors using time-series data in neural networks

Silva,

Bertol

2023

Anais Do XVI Congresso Brasileiro De Inteligência Computacional

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A common issue of DC and universal motors is the quality of the armature circuit and the commutation process. Some faults that could happen during the final assembly process, such as a short circuit between two segments or a broken segment in the armatures commutator, may not be detected by a visual inspection or the regular no-load end-of-line test. A more detailed analysis, using the feedback of various auxiliary sensors, usually requires implementing systems with more complex and expensive hardware. It also may take longer to get a precise analysis, which may not be suitable for the production process. As a method to do a fast and accurate detection and classification of faults in the armatures circuit of universal motors, this paper suggests the analysis of the armature voltage and current waveform by an artificial neural network (ANN). The current and voltage signals acquired during the no-load test formed a time-series waveform that was applied to an ANN trained to classify the signal in three possible outcomes: healthy armature, armature with shortcircuited segments, and armature with a broken part. The results achieve effectiveness above 98% in the task of detection and classification of faults. The solution was tested in the production line of universal motors and proved to be fast, reliable, and efficient for the usual no-load test, proving to be suitable in an industrial context.

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Section: Armature Faults Inspection Problemmentioning

confidence: 99%

Armature fault diagnosis of universal motors using time-series data in neural networks

Silva,

Bertol

2023

Anais Do XVI Congresso Brasileiro De Inteligência Computacional

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“…and mechanical degradations (bearings, gears, etc.) suffered by electric drives [3][4][5]. This experimental work aims to exploit different consecutive signatures in order to converge towards an accurate reading of the unbalance fault, which can be masked by other defects in low frequencies.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the mass unbalance of a variable speed induction motor by stator current multiple approaches

Bouras¹,

Bouras²,

Kerfali³

2018

Turk J Elec Eng & Comp Sci

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Generally, rotor mass unbalance is one of the most probable causes of the majority of degradations suffered by electric drives in an industrial environment (current pumping, rolling problem, misalignment, etc.), especially those with high power and variable speed. This document is an experimental contribution to the reliable detection of mass unbalance and changes in its severity if, by necessity of service, the system is subjected to a speed variation. The implementation of the technical orbits Park, strengthened by the application of the Fourier transforms (FFT, STFT) to the Park vector of the stator current allowed the identification of the unbalance defect at low frequency. The sequence of current analytical approaches favors the convergence towards an unambiguous reading of the mass unbalance defect.A comparative table has been drawn up in order to observe the evolution of the gravity of this mechanical defect in the case of a speed variation of the induction motor. The results obtained by this multifaceted approach are very satisfying and can contribute to a self-diagnostic with the assistance of a decision-making technique.

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“…Many recent studies have been made into the various methods for ball bearing fault diagnosis. The methods mainly used involve stator current [ 9 , 10 , 11 , 12 ], audio [ 13 , 14 ] and vibration signals [ 15 , 16 ]. For signal analysis, both discrete Fourier [ 17 ] transform and wavelet [ 18 , 19 , 20 , 21 , 22 , 23 , 24 ] were the most frequent analyses used.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Ball Bearing Fault Diagnosis Using Fractional Lorenz Chaos Extension Detection

Tian

et al. 2018

Sensors

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In this study we used a non-autonomous Chua’s circuit, and the fractional Lorenz chaos system. This was combined with the Extension theory detection method to analyze the voltage signals. The bearing vibration signals, measured using an acceleration sensor, were introduced into the master and slave systems through a Chua’s circuit. In a chaotic system, minor differences can cause significant changes that generate dynamic errors. The matter-element model extension can be used to determine the bearing condition. Extension theory can be used to establish classical and sectional domains using the dynamic errors of the fault conditions. The results obtained were compared with those from discrete Fourier transform analysis, wavelet analysis and an integer order chaos system. The diagnostic rate of the fractional-order master and slave chaotic system could reach 100% if the fractional-order parameter adjustment was used. This study presents a very efficient and inexpensive method for monitoring the state of ball bearings.

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Faults diagnosis for electrical machines based on analysis of motor current

Cited by 10 publications

References 18 publications

Armature fault diagnosis of universal motors using time-series data in neural networks

Armature fault diagnosis of universal motors using time-series data in neural networks

Prediction of the mass unbalance of a variable speed induction motor by stator current multiple approaches

Intelligent Ball Bearing Fault Diagnosis Using Fractional Lorenz Chaos Extension Detection

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