Evaluating the Progression and Orientation of Scratches on Outer-Raceway Bearing Using a Pattern Recognition Method

Pandarakone, Shrinathan Esakimuthu; Mizuno, Yukio; Nakamura, Hisahide

doi:10.1109/tie.2018.2833025

Cited by 39 publications

(23 citation statements)

References 23 publications

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“…One limitation is that the method can only be used to separate two classes, but combinations are possible [35]. SVM is a popular tool for bearing faults classification [9,36,37]. This classifier also relies on the backpropagation training algorithm that uses optimization of a convex quadratic function for maximum practical performance.…”

Section: Support Vector Machinesmentioning

confidence: 99%

Non‐invasive sound‐based classifier of bearing faults in electric induction motors

Santos

Scalassara

Endo

et al. 2021

IET Science Measure & Tech

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Induction motors play a major role in the industry nowadays due to their simple construction, uncomplicated maintenance, and cost efficiency. As the motor operates for repeated hours, some faults may occur and, depending on the process sensitivity, can cause significant losses to the industrial production. In this context, an alternative method is proposed to detect and classify bearing faults using acoustic emission signals generated by the machines and simple features obtained from them. A pair of condenser microphones was used to acquire these signals and audio feature extraction is performed to obtain time and frequency patterns to characterize healthy and faulty machines. The major differences of the acoustic signals regarding the fault frequency signatures are discussed by analyzing specific peaks observed in their spectra. Several coupled load values and levels of power supply voltage unbalance were considered in the experimental tests, which emulate common situations encountered in industrial environments, obtaining accuracy rates over 97% of success. Finally, a comparison is presented of machine learning techniques for bearing faults classification under different load values and voltage unbalance levels.

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Section: Support Vector Machinesmentioning

confidence: 99%

Non‐invasive sound‐based classifier of bearing faults in electric induction motors

Santos

Scalassara

Endo

et al. 2021

IET Science Measure & Tech

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“…For decades, researchers have made valuable achievements in intelligent fault diagnosis using machine learning techniques such as shallow network models [5][6][7]. In the past several years, growing attention has been paid to the application of deep learning-based diagnosis techniques [8][9][10][11][12], which can eliminate the dependence on manual feature engineering, thus facilitating the automation of diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of a Rotor-Bearing System Under Variable Rotating Speeds Using Two-Stage Parameter Transfer and Infrared Thermal Images

Shao

Xia³

et al. 2021

IEEE Trans. Instrum. Meas.

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Current fault diagnosis methods for rotor-bearing system are mostly based on analyzing the vibration signals collected at steady rotating speeds. In those methods, the data collected under one operating condition cannot be accurately used for diagnosis under a different condition. Moreover, in vibration monitoring, installing the necessary sensors will affect the equipment structure and hence the vibration response itself. The present paper proposes a new method based on two-stage parameter transfer and infrared thermal images for fault diagnosis of rotor-bearing system under variable rotating speeds. The method of parameter transfer enables the use of data (or parameters) acquired under one operating condition (called the source domain) to be extended for use in a different operating condition (called the target domain). First, scaled exponential linear unit (SELU) and modified stochastic gradient descent (MSGD) are used to construct an enhanced convolutional neural network (ECNN). Second, a stacked convolutional auto-encoder (CAE) trained based on unlabeled source-domain thermal images is employed to initialize a source-domain ECNN. Third, model parameters from the pre-trained source-domain ECNN are transferred to the target-domain ECNN to adapt to the characteristics of the target domain. The collected thermal images for a rotor-bearing system under variable speeds are used to test the transfer diagnosis performance of the proposed method. The experimental results demonstrate the performance improvement and the advantages of the proposed method.

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“…Most research for detecting bearing faults is based on vibration analysis [2][3][4][5][6][7][8][9]. Motor current signal analysis (MCSA) has also been proposed [10][11][12][13][14][15], and acoustic emission (AE)-based techniques are receiving increasing attention [16].…”

Section: Introductionmentioning

confidence: 99%

A Diagnosis Method of Bearing and Stator Fault in Motor Using Rotating Sound Based on Deep Learning

Nakamura¹,

Asano

Usuda

et al. 2021

Energies

Self Cite

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Various industrial fields use motors as key power sources, and their importance is increasing. In motor manufacturing, various tests are conducted for each motor before shipping. The no-load test is one such test, in which, for instance, the current flowing into the motor and temperature of the bearing is measured to confirm whether they are within specific values. Reducing labor, cost, and time in identifying an initially defective product requires a simple and reliable method. This study proposes a new diagnosis to identify the motor conditions based on the rotating sound of the motor in the no-load test. First, the rotating sounds of motors were measured using several healthy motors and motors with faults. Second, their sound characteristics were analyzed, and it was found that the characteristic signals appeared in a specific frequency range periodically. Then, considering these phenomena, a diagnostic method based on deep learning was proposed to judge the faults using long short-term memory (LSTM). Finally, the effectiveness of the proposed method was verified through experiments.

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Evaluating the Progression and Orientation of Scratches on Outer-Raceway Bearing Using a Pattern Recognition Method

Cited by 39 publications

References 23 publications

Non‐invasive sound‐based classifier of bearing faults in electric induction motors

Non‐invasive sound‐based classifier of bearing faults in electric induction motors

Fault Diagnosis of a Rotor-Bearing System Under Variable Rotating Speeds Using Two-Stage Parameter Transfer and Infrared Thermal Images

A Diagnosis Method of Bearing and Stator Fault in Motor Using Rotating Sound Based on Deep Learning

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