Experimental study for sensorless broken bar detection in induction motors

Çalış, Hakan; Çakir, Abdülkadir

doi:10.1016/j.enconman.2007.06.030

Cited by 37 publications

(8 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Faults in an induction motor are diagnosed mainly by using characteristic motor signals, such as vibration signals, motor currents [3][4][5][6][7][8][9], acoustic signals [10], and thermal images [11]. In the method that employs vibration signals [3][4][5], the vibration signals generated by an induction motor are measured, and fault diagnosis is performed by frequency analysis of the measured signals.…”

Section: Introductionmentioning

confidence: 99%

“…In the method that employs vibration signals [3][4][5], the vibration signals generated by an induction motor are measured, and fault diagnosis is performed by frequency analysis of the measured signals. In the method that employs motor current [6][7][8], a fixed magnetic current is obtained, and fault diagnosis is performed by frequency analysis of the measured signals. In the method that employs acoustic signals, the acoustic signals generated by an induction motor are measured, and fault diagnosis is performed by frequency analysis of the measured signals.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fault Diagnosis of Induction Motor Using Convolutional Neural Network

2019

View full text Add to dashboard Cite

Induction motors are among the most important components of modern machinery and industrial equipment. Therefore, it is necessary to develop a fault diagnosis system that detects the operating conditions of and faults in induction motors early. This paper presents an induction motor fault diagnosis system based on a CNN (convolutional neural network) model. In the proposed method, vibration signal data are obtained from the induction motor experimental environment, and these values are input into the CNN. Then, the CNN performs fault diagnosis. In this study, fault diagnosis of an induction motor is performed in three states, namely, normal, rotor fault, and bearing fault. In addition, a GUI (graphical user interface) for the proposed fault diagnosis system is presented. The experimental results confirm that the proposed method is suitable for diagnosing rotor and bearing faults of induction motors.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Induction Motor Using Convolutional Neural Network

2019

View full text Add to dashboard Cite

show abstract

“…When the induction motor faults occur, they would cause the variation of the airgap during the radial rotor movement, influence the flux density, and change the three-phase stator currents. e fault-induced frequencies on the current spectrum would emerge as symmetric side lobes alongside the VFD frequency, a phenomenon called the frequency modulation [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Fusion of Vibration and Current Signatures for the Fault Diagnosis of Induction Machines

Liu

Tran

Weng

2019

Shock and Vibration

View full text Add to dashboard Cite

Induction machines are widely used in the industry as one of the major actuators, such as water pumps, air compressors, and fans. It is necessary to monitor and diagnose these induction motors to prevent any sudden shut downs caused by premature failures. Numerous fault detection and isolation techniques for the diagnosis of induction machines have been proposed over the past few decades. Among these techniques, motor current signature analysis (MCSA) and vibration analysis are two of the most common signal-based condition monitoring methods. They are often adopted independently, but each method has its strengths and weaknesses. This research proposed a systemic method to integrate the information received from the vibration and current measurements. We applied the wavelet packet decomposition to extract the time-frequency features of the vibration and current measurements and used the support vector machines as classifiers for the initial decision-making. The significant features were identified, and the performances of several classifiers were compared. As a result, the decision-level sensor fusion based on the Sugeno fuzzy integral was proposed to integrate the vibration and current information to improve the accuracy of the diagnosis.

show abstract

“…An important variety of strategies have been proposed as options to the already accepted mechanical vibration analysis. Fault detection techniques based on current and voltage measurement, powers and fluxes have proved efficiency in fault detection and diagnosis processes, not only for faults originated electromagnetically but also mechanically [3][4][5][6][7][8][9]. Among these strategies, the motor current signature analysis (MCSA) is the most widespread, due its simplicity and minimum number of required sensors [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%