Neural network based motor bearing fault detection

Eren, Levent; Karahoca, Adem; Devaney, M.J.

doi:10.1109/imtc.2004.1351399

Cited by 30 publications

(21 citation statements)

References 6 publications

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“…Stack et al [15] examine single-point defects and generalized roughness. Schoen's model components are also analyzed by using parametric [20] and nonparametric [2], [9] spectral analysis, neural networks, and/or wavelet transform [10], [21].…”

Section: Bearing Fault Detection By Stator Current Analysismentioning

confidence: 99%

Models for Bearing Damage Detection in Induction Motors Using Stator Current Monitoring

Blodt

Granjon

Raison

et al. 2008

IEEE Trans. Ind. Electron.

479

193

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Section: Bearing Fault Detection By Stator Current Analysismentioning

confidence: 99%

Models for Bearing Damage Detection in Induction Motors Using Stator Current Monitoring

Blodt

Granjon

Raison

et al. 2008

IEEE Trans. Ind. Electron.

479

193

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“…Time-domain [4], frequency-domain [5][6][7][8], enhanced frequency [9][10][11][12], and time-scale analysis [13][14][15][16] are four main areas where signal processing techniques are used in the feature extraction [17]. The extracted features are used to both train and operate Artificial Neural Networks (ANNs) or other decision support systems (DSS) [18][19][20][21][22][23][24][25][26][27][28][29][30][31]. Extracting fixed features each time data is analyzed by DSS may require significant amount of computational effort.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Detection by One‐Dimensional Convolutional Neural Networks

Eren

2017

Mathematical Problems in Engineering

197

106

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Bearing faults are the biggest single source of motor failures. Artificial Neural Networks (ANNs) and other decision support systems are widely used for early detection of bearing faults. The typical decision support systems require feature extraction and classification as two distinct phases. Extracting fixed features each time may require a significant computational cost preventing their use in realtime applications. Furthermore, the selected features for the classification phase may not represent the most optimal choice. In this paper, the use of 1D Convolutional Neural Networks (CNNs) is proposed for a fast and accurate bearing fault detection system. The feature extraction and classification phases of the bearing fault detection are combined into a single learning body with the implementation of 1D CNN. The raw vibration data (signal) is fed into the proposed system as input eliminating the need for running a separate feature extraction algorithm each time vibration data is analyzed for classification. Implementation of 1D CNNs results in more efficient systems in terms of computational complexity. The classification performance of the proposed system with real bearing data demonstrates that the reduced computational complexity is achieved without a compromise in fault detection accuracy.

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“…For example in the application of [52], using an ordinary computer, when the performance of the 1D CNN was tested, a fault detection speed of 45-times faster than real-time speed was achieved. As another example, Figure 17 presents the average classification times of the 1D CNN and the 6 competing methods for the application of motor fault detection where the competing methods are from [75][76][77][78]. .…”

Section: Computational Complexity Analysis Of 1d-cnnsmentioning

confidence: 99%

1-D Convolutional Neural Networks for Signal Processing Applications

Kıranyaz

İnce

Abdeljaber

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

274

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During the last decade, Convolutional Neural Networks (CNNs) have become the de facto standard for various Computer Vision and Machine Learning operations. CNNs are feed-forward Artificial Neural Networks (ANNs) with alternating convolutional and subsampling layers. Deep 2D CNNs with many hidden layers and millions of parameters have the ability to learn complex objects and patterns providing that they can be trained on a massive size visual database with ground-truth labels. With a proper training, this unique ability makes them the primary tool for various engineering applications for 2D signals such as images and video frames. Yet, this may not be a viable option in numerous applications over 1D signals especially when the training data is scarce or application-specific. To address this issue, 1D CNNs have recently been proposed and immediately achieved the state-of-the-art performance levels in several applications such as personalized biomedical data classification and early diagnosis, structural health monitoring, anomaly detection and identification in power electronics and motor-fault detection. Another major advantage is that a real-time and low-cost hardware implementation is feasible due to the simple and compact configuration of 1D CNNs that perform only 1D convolutions (scalar multiplications and additions). This paper presents a comprehensive review of the general architecture and principals of 1D CNNs along with their major engineering applications, especially focused on the recent progress in this field. Their state-of-the-art performance is highlighted concluding with their unique properties. The benchmark datasets and the principal 1D CNN software used in those applications are also publically shared in a dedicated website.

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Neural network based motor bearing fault detection

Abstract: Ab.vfrad ~ Bearing ,farrlt.s ore the higgesl single rnrrse of nrolor ,failr~rez The hwring d Show more

Cited by 30 publications

References 6 publications

Models for Bearing Damage Detection in Induction Motors Using Stator Current Monitoring

Models for Bearing Damage Detection in Induction Motors Using Stator Current Monitoring

Bearing Fault Detection by One‐Dimensional Convolutional Neural Networks

1-D Convolutional Neural Networks for Signal Processing Applications

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