Motor Bearing Fault Detection Using Spectral Kurtosis-Based Feature Extraction Coupled With <i>K</i>-Nearest Neighbor Distance Analysis

Tian, Jing; Morillo, Carlos; Azarian, Michael H.; Pecht, Michael

doi:10.1109/tie.2015.2509913

Cited by 451 publications

(207 citation statements)

References 41 publications

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“…The changing trend of RMS over time illustrates the intrinsic characteristics of the damage propagating process: small spalls or cracks are formed and later smoothed by the continuous rolling contact in the failure of the initial state. When the damage spreads over a wider area, the vibration level rises again [5]. This paper extracted the data on the damage of the outer race in bearing 1 which chose 19 February 2004 00:42 as the occurrence of the early failure time.…”

Section: Analysis Results Of the Outer Race Fault Signalmentioning

confidence: 99%

“…Noise reduction methods often address the types of noise. For example, Tian et al [5] extracted a motor bearing fault feature based on spectral kurtosis, which considered both white Gaussian noise and impulsive noises from the gear.…”

Section: Introductionmentioning

confidence: 99%

“…Traditional methods for the extraction of signals under Gaussian strong noise can be divided into three categories: time domain, frequency domain, and time-frequency domain methods [6]. Time-frequency domain methods are often appropriate for analyzing the non-stationary signal, which needs to choose the appropriate parameters; for example, the wavelet basis and decomposing level have to be chosen when using discrete wavelet transform (DWT) [5]. The short-time Fourier transform (STFT) has to be offered a suitable window size [2].…”

Section: Introductionmentioning

confidence: 99%

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Development of a Method for Selection of Effective Singular Values in Bearing Fault Signal De-Noising

Gao

Wang

et al. 2016

Applied Sciences

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Singular value decomposition (SVD) is a widely used and powerful tool for signal extraction under noise. Noise attenuation relies on the selection of the effective singular value because these values are significant features of the useful signal. Traditional methods of selecting effective singular values (or selecting the useful components to rebuild the faulty signal) consist of seeking the maximum peak of the differential spectrum of singular values. However, owing to the small number of selected effective singular values, these methods lead to excessive de-noised effects. In order to get a more appropriate number of effective singular values, which preserves the components of the original signal as much as possible, this paper used a difference curvature spectrum of incremental singular entropy to determine the number of effective singular values. Then the position was found where the difference of two peaks in the spectrum declines in an infinitely large degree for the first time, and this position was regarded as the boundary of singular values between noise and a useful signal. The experimental results showed that the modified methods could accurately extract the non-stationary bearing faulty signal under real background noise.

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Section: Analysis Results Of the Outer Race Fault Signalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Development of a Method for Selection of Effective Singular Values in Bearing Fault Signal De-Noising

Gao

Wang

et al. 2016

Applied Sciences

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“…Applications include fall detection [8], activity detection for energy saving at homes or offices [9], 24-hour sleep-wake monitoring in narcolepsy [10], a detection system for motion disorders in Autism patients [11], and other uses leveraging IoTs [12][13][14][15][16][17][18]. The methods introduced in [13,14] leverage body sensor nodes powered by human energy harvesting and wireless sensor networks for remote patient monitoring.…”

Section: Related Workmentioning

confidence: 99%

Internet of Things for Sensing: A Case Study in the Healthcare System

et al. 2018

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Medical healthcare is one of the fascinating applications using Internet of Things (IoTs). The pervasive smart environment in IoTs has the potential to monitor various human activities by deploying smart devices. In our pilot study, we look at narcolepsy, a disorder in which individuals lose the ability to regulate their sleep-wake cycle. An imbalance in the brain chemical called orexin makes the sleep pattern irregular. This sleep disorder in patients suffering from narcolepsy results in them experience irrepressible sleep episodes while performing daily routine activities. This study presents a novel method for detecting sleep attacks or sleepiness due to immune system attacks and affecting daily activities measured using the S-band sensing technique. The S-Band sensing technique is channel sensing based on frequency spectrum sensing using the orthogonal frequency division multiplexing transmission at a 2 to 4 GHz frequency range leveraging amplitude and calibrated phase information of different frequencies obtained using wireless devices such as card, and omni-directional antenna. Each human behavior induces a unique channel information (CI) signature contained in amplitude and phase information. By linearly transforming raw phase measurements into calibrated phase information, we ascertain phase coherence. Classification and validation of various human activities such as walking, sitting on a chair, push-ups, and narcolepsy sleep episodes are done using support vector machine, K-nearest neighbor, and random forest algorithms. The measurement and evaluation were carried out several times with classification values of accuracy, precision, recall, specificity, Kappa, and F-measure of more than 90% that were achieved when delineating sleep attacks.

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“…Calculate IMFCM of the roller bearing vibration signal in each group according to the aforementioned process. To verify the fault diagnosis accuracy of IMFCM, the IMF energy moment matrix (IMFEMM) [42], IMF fuzzy entropy matrix (IMFFEM) [43], and IMF spectral kurtosis matrix (IMFSKM) [44] are considered as controlled objects which are extracted in the same process as the IMFCM, as shown in Figure 6. It is obvious that the features under the fourth component possess less feature diversity than the first four components in Figure 7 so they naturally have less contribution to the fault divisibility.…”

Section: Stationary Operating Situationsmentioning

confidence: 99%

Fast EEMD Based AM-Correntropy Matrix and Its Application on Roller Bearing Fault Diagnosis

Jia

Qin

et al. 2016

Entropy

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Abstract:Roller bearing plays a significant role in industrial sectors. To improve the ability of roller bearing fault diagnosis under multi-rotating situation, this paper proposes a novel roller bearing fault characteristic: the Amplitude Modulation (AM) based correntropy extracted from the Intrinsic Mode Functions (IMFs), which are decomposed by Fast Ensemble Empirical mode decomposition (FEEMD) and employ Least Square Support Vector Machine (LSSVM) to implement intelligent fault identification. Firstly, the roller bearing vibration acceleration signal is decomposed by FEEMD to extract IMFs. Secondly, IMF correntropy matrix (IMFCM) as the fault feature matrix is calculated from the AM-correntropy model of the primary vibration signal and IMFs. Furthermore, depending on LSSVM, the fault identification results of the roller bearing are obtained. Through the bearing identification experiments in stationary rotating conditions, it was verified that IMFCM generates more stable and higher diagnosis accuracy than conventional fault features such as energy moment, fuzzy entropy, and spectral kurtosis. Additionally, it proves that IMFCM has more diagnosis robustness than conventional fault features under cross-mixed roller bearing operating conditions. The diagnosis accuracy was more than 84% for the cross-mixed operating condition, which is much higher than the traditional features. In conclusion, it was proven that FEEMD-IMFCM-LSSVM is a reliable technology for roller bearing fault diagnosis under the constant or multi-positioned operating conditions, and as such, it possesses potential prospects for a broad application of uses.

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Motor Bearing Fault Detection Using Spectral Kurtosis-Based Feature Extraction Coupled With K-Nearest Neighbor Distance Analysis

Cited by 451 publications

References 41 publications

Development of a Method for Selection of Effective Singular Values in Bearing Fault Signal De-Noising

Development of a Method for Selection of Effective Singular Values in Bearing Fault Signal De-Noising

Internet of Things for Sensing: A Case Study in the Healthcare System

Fast EEMD Based AM-Correntropy Matrix and Its Application on Roller Bearing Fault Diagnosis

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