Monitoring Journal-Bearing Faults: Making Use of Motor Current Signature Analysis for Induction Motors

Jung, Junyeong; Park, Yonghyun; Lee, Sang Bin; Cho, Chang‐Hee; Kim, Kwon-Hee; Wiedenbrug, E.; Teska, Mike

doi:10.1109/mias.2016.2600725

Cited by 28 publications

(11 citation statements)

References 18 publications

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“…For instance, permanent magnet synchronous motor (PMSM) [93] plays an important role in many industrial applications such as EV traction steering, robotics and wind power due to its high efficiency, reliability, wide working range and high torque density [87]. Induction machine popularizes in EV powertrains as well [94]. Therefore, ensuring their correct operation is the key task of the motors fault diagnosis.…”

Section: A Ai Approaches For Detecting Faults Of Electric Machinementioning

confidence: 99%

Feature Engineering and Artificial Intelligence-Supported Approaches Used for Electric Powertrain Fault Diagnosis: A Review

Zhang

Deng

et al. 2022

IEEE Access

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Electric powertrain is constituted by electric machine transmission unit, inverter and battery packs, etc., is a highly-integrated system. Its reliability and safety are not only related to industrial costs, but more importantly to the safety of human life. This review is the first contribution to comprehensively summarize both the feature engineering methods and artificial intelligence (AI) algorithms (including machine learning, neural networks and deep learning) in electric powertrain condition monitoring and fault diagnosis approaches. Specifically, this paper systematically divides the AI-supported method into two main steps: feature engineering and AI approach. On the one hand, it introduces the data and feature processing in AI-supported methods, and on the other hand it summarizes input signals, feature methods and AI algorithms included in the AI method in cases. Therefore, firstly this review is to guide how to choose the appropriate feature engineering method in further research. Secondly, the up-to-date AI algorithms adopted for powertrain health monitoring are presented in detail. Finally, such current approaches are discussed and future trends are proposed.

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Section: A Ai Approaches For Detecting Faults Of Electric Machinementioning

confidence: 99%

Feature Engineering and Artificial Intelligence-Supported Approaches Used for Electric Powertrain Fault Diagnosis: A Review

Zhang

Deng

et al. 2022

IEEE Access

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“…Even if the bearing is made of superior material and installed properly, the rolling bearing appears to wear, gap, or fatigue delamination in normal use under standard operation. Failure types of rolling bearings are mainly caused by fatigue spalling, wear, fracture, corrosion, adhesion, and indentation [15]. The fault analysis methods of rolling bearing mainly include vibration analysis, oil analysis, temperature detection, and acoustic emission.…”

Section: Figure 1 Structure Of Rolling Bearingmentioning

confidence: 99%

Application of Improved Bp Neural Network Using Genetic Algorithm in Fault Assessment of Bearing in Electromechanical System

2021

IJOMAM

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The research aims at the serious economic loss and life-threatening problems caused by mechanical and electrical system accidents and bearing failures. The fault detection and identification of bearings in mechanical and electrical systems are discussed. The structure, fault cause, vibration mechanism, and characteristic frequency of rolling bearing are analyzed, and the noise of vibration signals is removed and eliminated in light of the characteristics of the initial fault signal of rolling bearing. Because of the shortcomings of the wavelet, wavelet packet transform is proposed to characterize the normal state of rolling bearing, rolling element fault, inner ring fault, and outer ring fault signal. Based on the characteristics of global optimization of GA (genetic algorithm), the algorithm falls into local optimal value following the defects of BP (backpropagation) neural network and uses GA to optimize the BP neural network algorithm for fault diagnosis of rolling bearings. According to the experimental results, when the evolution algebra of the fault diagnosis model GA-BP is 8 at the drive end, the optimal classification accuracy of the population reaches 98.83%. In this case, a rolling element fault in the test data is misclassified. When the evolution algebra of the GA-BP fault diagnosis model is 2 at the fan side, the overall optimal classification accuracy reaches 97.62% in total. Under this condition, a rolling element fault and an outer ring fault are misclassified in the test data. Through the comparison experiment with the traditional optimized BP neural network, it is found that the GA-BP neural network algorithm model is suitable for the fault classification of rolling bearings.

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“…where σ denotes total leakage factor, τ S is the stator time constant, and T el is the electrical torque. Figure 1 shows the block scheme of the simplified model of IM with BRB resulting from Equations (4)- (12) [42], where T l stands for load torque. The model has been tested on a motor (see Table A1 in Appendix A) with a different number of bars drilled.…”

Section: Model Of Im With a Faulty Rotormentioning

confidence: 99%

“…Nowadays, more and more industrial electrical drives tend to be supervised by such techniques [7]. Among possible IM faults, researchers mostly deal with stator winding faults [8][9][10], or different mechanical faults [11] like bearing faults [12,13], rotor eccentricities [14,15], and rotor electrical asymmetry due to broken rotor bars (BRB) or end-ring segments [16,17]. Some of the methods could also be used for the detection of mixed faults [18,19].…”

Section: Introductionmentioning

confidence: 99%

Induction Motor Broken Rotor Bar Detection Based on Rotor Flux Angle Monitoring

et al. 2019

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This paper presents a method for the detection of broken rotor bars in an induction motor. After introducing a simplified dynamic model of an induction motor with broken cage bars in a rotor field reference frame which allows for observation of its internal states, a fault detection algorithm is proposed. Two different motor estimation models are used, and the difference between their rotor flux angles is extracted. A particular frequency component in this signal appears only in the case of broken rotor bars. Consequently, the proposed algorithm is robust enough to load oscillations and/or machine temperature change, and also indicates the fault severity. The method has been verified at different operating points by simulations as well as experimentally. The fault detection is reliable even in cases where traditional methods give ambiguous verdicts.

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Monitoring Journal-Bearing Faults: Making Use of Motor Current Signature Analysis for Induction Motors

Cited by 28 publications

References 18 publications

Feature Engineering and Artificial Intelligence-Supported Approaches Used for Electric Powertrain Fault Diagnosis: A Review

Feature Engineering and Artificial Intelligence-Supported Approaches Used for Electric Powertrain Fault Diagnosis: A Review

Application of Improved Bp Neural Network Using Genetic Algorithm in Fault Assessment of Bearing in Electromechanical System

Induction Motor Broken Rotor Bar Detection Based on Rotor Flux Angle Monitoring

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