Condition Monitoring and Fault Diagnosis of Serial Wound Starter 
 Motor with Learning Vector Quantization Network

Bayır, Raif

doi:10.3923/jas.2008.3148.3156

Cited by 16 publications

(2 citation statements)

References 13 publications

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“…The main motor-and-tractor starter malfunctions are: breakages and short circuits of electric motor coils (13%), free-wheeling clutch slipping (10%), starter drive mechanism gumming (12%), wear of bushing and brush barrels (21%), failure of the cushioning spring (5%) and the in-built reducer (9%), damage of the gear teeth (7%), traction relay malfunction (18%) and others [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Developing the model of normal operation of starter motor traction relays

Puzakov

2019

E3S Web Conf.

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Malfunction of motor starter traction relays cause the impossibility of the reliable start of internal combustion engines which, if the worst comes to the worst, can create the risk of traffic accident occurrence. Electric resistances of traction relay coils are structural and diagnostic parameters at the same time. Emergence and development of failures causes the change of magnetomotive force generated by the traction relay coils. Critical values of electric resistances correspond to failures of traction relay coils, i.e., inability to perform the built-in functions. The developed mathematical model of traction relay normal operation includes the amount of effort generated and voltage loss on contacts as controlled values for the drive mechanism and the starter electric motor, respectively. The results obtained contribute to the development of the method of motor-and-tractor starter diagnostics without removing it from the engine.

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Section: Introductionmentioning

confidence: 99%

Developing the model of normal operation of starter motor traction relays

Puzakov

2019

E3S Web Conf.

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“…A fuzzy logic based fault detection system was developed by Bayır and Bay for implementation on emergency vehicles [ 23 ]. Bayır developed a graphical user interface software for real-time condition monitoring and fault diagnosis of serial wound starter motors using a Learning Vector quantization neural network [ 24 ]. Vijay et al evaluated seven wavelet based denoising schemes based on the performance of the artificial neural network (ANN) and the support vector machine (SVM), for the bearing condition classification [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Monitoring and Fault Diagnosis of a Low Power Hub Motor Using Feedforward Neural Network

Simsir

Bayır

Uyaroğlu

2016

Computational Intelligence and Neuroscience

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Low power hub motors are widely used in electromechanical systems such as electrical bicycles and solar vehicles due to their robustness and compact structure. Such systems driven by hub motors (in wheel motors) encounter previously defined and undefined faults under operation. It may inevitably lead to the interruption of the electromechanical system operation; hence, economic losses take place at certain times. Therefore, in order to maintain system operation sustainability, the motor should be precisely monitored and the faults are diagnosed considering various significant motor parameters. In this study, the artificial feedforward backpropagation neural network approach is proposed to real-time monitor and diagnose the faults of the hub motor by measuring seven main system parameters. So as to construct a necessary model, we trained the model, using a data set consisting of 4160 samples where each has 7 parameters, by the MATLAB environment until the best model is obtained. The results are encouraging and meaningful for the specific motor and the developed model may be applicable to other types of hub motors. The prosperous model of the whole system was embedded into Arduino Due microcontroller card and the mobile real-time monitoring and fault diagnosis system prototype for hub motor was designed and manufactured.

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