Vibration Analysis in Turbomachines Using Machine Learning Techniques

Pinheiro, Allan Alves; Brandao, Iago Modesto; Costa, Cesar da

doi:10.24018/ejers.2019.4.2.1128

Cited by 20 publications

(11 citation statements)

References 14 publications

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“…Bearing Vibration monitoring [5][6][7][8] Stator winding Motor current signature analysis [9,10] Rotor bar Motor current signature analysis [11][12][13] Eccentricity Vibration monitoring [14] Unbalance Vibration monitoring [15] Induction Motor…”

Section: Types Of Faults Monitoring Techniquementioning

confidence: 99%

Condition Monitoring and Fault Diagnosis of Induction Motor in Electric Vehicle

Gundewar

Kane

2021

Lecture Notes in Mechanical Engineering

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The twenty-first century is witnessing the growth of electric vehicles due to the declining level of petroleum products and legal concern for clean technology to take care of environmental pollution. Battery and electric motor are the two important components in the electric vehicle. An electric motor is a prime component responsible for the propulsion of a vehicle. Because of the continuous operation and load variation, the motor is subjected to different types of faults. Thus, condition monitoring and on-board diagnosis of an electric motor in the electric vehicle is essential to avoid catastrophic failure. In India, it is observed that the induction motor is commonly used in electric vehicles for propulsion. This article proposes the methodology for condition monitoring and fault identification of components in the induction motor using an on-board diagnostics in an electric vehicle.

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Section: Types Of Faults Monitoring Techniquementioning

confidence: 99%

Condition Monitoring and Fault Diagnosis of Induction Motor in Electric Vehicle

Gundewar

Kane

2021

Lecture Notes in Mechanical Engineering

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“…Da Costa et al present a study on a method of diagnosing failures in rotary machines using machine learning techniques [10]. In this study, a support vector machine -SVM algorithm was proposed for fault diagnosis of the rotational misalignment in the rotor.…”

Section: Introductionmentioning

confidence: 99%

“…This paper is an extended version of the contribution presented in [10], [15], [16]. It includes a broader range of intelligent fault diagnosis algorithms with additional experimental tests on rotor misalignment.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Rotary Machines Using Machine Learning

Brandao¹,

Costa²

2022

Eletrônica de Potência

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Classification of faults in rotary machines using machine learning is gaining attention in the field of science and engineering. In rotating machinery, misalignment is a common fault. This type of fault has been extensively studied in the literature using the vibration signals produced by rotary machines. This study proposes an approach based on machine learning techniques to diagnose misalignments in rotary machines under various conditions. A personalized diagnostic fault approach is proposed to detect misalignment faults. The approach includes three steps. First, the data acquisition model is developed to obtain signals (fault samples). The rotor vibration signals in stationary rotation conditions were obtained by two inductive proximity sensors with analog output, and the data were collected by a data acquisition device. Then, to generate the faulty training samples, each data acquisition signal is transformed to the frequency domain using Fast Fourier Transform (FFT). Finally, using the samples obtained through the feature selection techniques, machine learning algorithms Random Forest, Naïve Bayes and SVM were evaluated, resulting in classifications with different efficiencies. The results show that the SVM algorithm outperforms the Naïve Bayes and Random Forest algorithms when the same number of features is used. The proposed personalized diagnostic approach was applied to detect faults of rotating electrical machines misalignment with success.

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“…Ahmad et al, 2021 [5)]). Pinheiro et al (2019) developed an automatic predictive maintenance model for the diagnosis of incipient failures in rotary machines, based on support vector machines (SVM) [6)]. Dhanraj and Sugumaran (2020) employed a machine-learning approach using vibration signals through statistical features for condition monitoring of wind turbine blades [7)].…”

Section: Introductionmentioning

confidence: 99%