Fault Detection of DC Electric Motors Using the Bispectral Analysis

Boltežar, Miha; Slavič, Janko

doi:10.1007/s11012-005-5898-0

Cited by 12 publications

(8 citation statements)

References 17 publications

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“…These methods are based on the different information available to describe the behavior of the systems. In most practical situations, the operating diagnostic is usually performed in the presence of measurement noise, disturbances, process noise and modeling errors [2,3].…”

Section: Introductionmentioning

confidence: 99%

“…Although these induction motors are very reliable, but they are subjected to different types of faults and failures. These faults may be inherent to the machine itself or due to operating conditions [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…; u (1), u (2) and u (3): Signals captured on MATLAB; A 0 , A 1 and A 2 : The analog inputs of the Arduino board; T max : MAX acquisition time.…”

mentioning

confidence: 99%

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Real-time diagnosis of three-phase induction machine using Arduino-Uno card based on park’s circle method

Belaid¹,

Cherroun²,

Benbrika³

2018

Diagnostyka

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The main role of diagnosis is the real-time detection of the different types of faults on the physical system. Induction motors are nowadays used more frequently than any other electric motor in various kinds of electric drives. Incipient fault diagnosis is very important because if the fault is undetected, small motor failure can lead to serious motor failure. The objective of this work is the study and the realization of a diagnosis based on signal processing by the Park method of a wound rotor induction machine (in real-time). We have chosen for the realization of this diagnostic strategy the Arduino-UNO board and the ACS-720 current sensor as a signal acquisition system. The results obtained in practice show that this method of diagnosis is a very powerful and very sensitive technique for signaling and identifying the different types of faults and achieving the desired objectives.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Real-time diagnosis of three-phase induction machine using Arduino-Uno card based on park’s circle method

Belaid¹,

Cherroun²,

Benbrika³

2018

Diagnostyka

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“…Currently, there exists quite a variety of diagnostic tools [3][4][5][6][7][8][9][10][11][12][13]. However, the available electric drive diagnostic systems of mining shovels are limited, mainly, to control of such parameters as oil level, bearing temperature, vibration, current of the armature and field windings, position of the shovel, its tilt angle, and others [14].…”

Section: Introductionmentioning

confidence: 99%

Computer System for Electric Drives Fault Diagnosis of Mining Shovels

Kashirskikh¹,

Gargayev²,

Zavyalov³

et al. 2016

Proceedings of the 8th Russian-Chinese Symposium "Coal in the 21st Century: Mining, Processing, Safety"

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It is proposed to conduct fault diagnostic test on electric drives of mining shovels based on the results of monitoring the current values of electromagnetic and mechanical parameters and variables of electric drives obtained in the course of their operation using the modern computer technology. The structure of the developed system of functional diagnostics, allowing to monitor the status of the drive and identify emerging fault is shown in the paper. To determine in real time the current parameters and variables of DC motor which can't be measured during their operation, the dynamic identification was used based on the measured current and voltage of the motor windings, and mathematical estimation methods. Parameters of the mechanical subsystem of electric drive are identified by a mobile measuring system. The authors also give the structure and characteristics of the one-step neural network predictor of current, used to predict the current values in the armature and field windings of motor. The analysis of the technical state of the electric drive by a set of attributes is performed in a special analyzer, built on the basis of pre-trained artificial neural network. The results of these studies support the possibility of creating a diagnostic system for the main electric drives of mining shovels using the estimation methods and apparatus of artificial neural networks.

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“…The authors have shown the combined use of cyclo-stationary and bicoherence analysis on real vibration signals to detect both local and distributed faults in a multiple shaft gearbox. Furthermore, bicoherence or bispectrum analysis has been applied on vibration signals to detect aerodynamic excitation faults and oil whirl faults in a rotor system (Wang et al, 2001), and bearing faults and collector faults in a DC motor (Boltezar and Slavic, 2006).…”

Section: Introductionmentioning

confidence: 99%