Intelligent fault diagnosis method for rotating machinery via dictionary learning and sparse representation-based classification

Han, Te; Jiang, Dongxiang; Sun, Yankui; Wang, Nanfei; Yang, Yizhou

doi:10.1016/j.measurement.2018.01.036

Cited by 64 publications

(24 citation statements)

References 38 publications

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“…If the period of autocorrelation function of the nonstationary signal x(t) is time-varying (the period is T 0 ) which could be represented in equation (5), then the signal could be defined as second-order cyclostationarity:…”

Section: Fscmentioning

confidence: 99%

“…It has great significance in machinery condition monitoring to realize their timely fault feature extraction. Vibration analysis has been used widely in fault feature extraction of rotating machinery [1][2][3][4][5][6] in engineering due to the reasons that vibration signal is easy to collect and it also contains rich fault feature information. However, early weak fault features of rolling bearing or gear are hard to extract using the traditional signal processing method such as envelope demodulation spectral (EDS) [7] and wavelet transform [8] because the early weak fault features are often overwhelmed by strong interference.…”

Section: Introductionmentioning

confidence: 99%

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Fast Spectral Correlation Based on Sparse Representation Self-Learning Dictionary and Its Application in Fault Diagnosis of Rotating Machinery

Wang

2020

Complexity

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Rolling element bearing and gear are the typical supporting or rotating parts in mechanical equipment, and it has important economy and security to realize their quick and accurate fault detection. As one kind of powerful cyclostationarity signal analyzing method, spectral correlation (SC) could identify the impulsive characteristic component buried in the vibration signals of rotating machinery effectively. However, the fault feature such as impulsive characteristic component is often interfered by other background noise, and the situation is serious especially in early weak fault stage. Besides, the traditional SC method has a drawback of low computation efficiency which hinders its wide application to some extent. To address the above problems, an impulsive feature-enhanced method which combines fast spectral correlation (FSC) with sparse representation self-learning dictionary is proposed in the paper. Firstly, the sparse representation self-learning dictionary method-K-means singular value decomposition (KSVD) is improved and the improved KSVD (IKSVD) method is used to denoise the original signal, and the periodic impulses are highlighted. Then, the FSC algorithm is applied on the denoised signal and spectral correlation image could be obtained. Finally, the calculated enhanced envelope spectrum (EES) of the denoised signal is obtained by using the spectral correlation image to identify the accurate fault position. The feasibility and superiority of the proposed method is verified through simulation, experiment, and engineering application.

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Section: Fscmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fast Spectral Correlation Based on Sparse Representation Self-Learning Dictionary and Its Application in Fault Diagnosis of Rotating Machinery

Wang

2020

Complexity

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“…The main challenge in the automatic identification of epileptic seizures is choosing the distinguishing features in order to discriminate between different stages (including ictal, pre-ictal and etc.). However, in most of the previous works, at first, several time, frequency, time-frequency, and statistical features are extracted, then, the best discriminative features are selected either manually or using conventional feature selection methods [22,23], which is a time-consuming procedure demanding high computational complexity due to high dimensions and are usually not robust and are computationally intensive [24,25]. Furthermore, the best features in one case/subject may not be considered as optimum for another one.…”

Section: Introductionmentioning

confidence: 99%

Automatic Identification of Epileptic Seizures From EEG Signals Using Sparse Representation-Based Classification

et al. 2020

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Identifying seizure activities in non-stationary electroencephalography (EEG) is a challenging task since it is time-consuming, burdensome, and dependent on expensive human resources and subject to error and bias. A computerized seizure identification scheme can eradicate the above problems, assist clinicians, and benefit epilepsy research. So far, several attempts were made to develop automatic systems to help neurophysiologists accurately identify epileptic seizures. In this research, a fully automated system is presented to automatically detect the various states of the epileptic seizure. This study is based on sparse representation-based classification (SRC) theory and the proposed dictionary learning using electroencephalogram (EEG) signals. Furthermore, this work does not require additional preprocessing and extraction of features, which is common in the existing methods. This study reached the sensitivity, specificity, and accuracy of 100% in 8 out of 9 scenarios. It is also robust to the measurement noise of level as much as 0 dB. Compared to state-of-the-art algorithms and other common methods, our method outperformed them in terms of sensitivity, specificity, and accuracy. Moreover, it includes the most comprehensive scenarios for epileptic seizure detection, including different combinations of 2 to 5 class scenarios. The proposed automatic identification of epileptic seizures method can reduce the burden on medical professionals in analyzing large data through visual inspection as well as in deprived societies suffering from a shortage of functional magnetic resonance imaging (fMRI) equipment and specialized physician.

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“…resonance [6], time-frequency analysis [7], [8] wavelet transform [9], sparse representation [10], and deep learning [11], have been applied and proposed for gearbox fault diagnosis. For monitoring vibration-based gearbox condition, Sait et al [12] presented a review of several methods and indicators such as crest factor, Root Mean Square (RMS), kurtosis, wavelet transform, residual signal, difference signal, band-pass mesh signal, and Winger-Ville distribution.…”

Section: Introductionmentioning

confidence: 99%

Experimental Investigation of Spur Gear Tooth Crack Location and Depth Detection using Short-time Averaging Method and Statistical Indicators

Rezaei

Poursina

Rezaei

2020

IJE

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Gear systems are one of the most functional power transmission systems in the industry. Crack is one of the common defects in gears which is caused by excessive loading, sudden impact and shortcomings in the gears construction. Initially, the crack will not result in structure collapse, but its growth can lead to irreparable damage. Therefore, detecting the crack and determining its location and depth are very important in this respect. In this paper, two encoders are used to obtain the spur gear pair transmission error speed. Moreover, the short-time averaging method (STAM) has been proposed thereby detecting the crack location and some statistical indicators have been used to estimate the crack depth in the spur gear tooth. For this purpose, a dynamical model in which mesh stiffness varying with time has been deployed to achieve the transmission error speed of the gear system. Additionally, a gear test rig including a single-stage gearbox, two encoders, and also an electronic board has been used. Encoders were installed on input and output shafts and the angular position of each shaft in time was saved in the computer using the electronic board. In addition, the transmission error speed was obtained by analyzing the received signals. Then, short-time averaging method was used to identify the crack location. Ultimately, some indicators such as ABS-max, FM0, Energy Ratio (ER) and Residual Signal Average were applied to the simulated results and experimental signals to fine the crack depth ratio. According to the results of this study, it seems safe to conclude that the STAM is a useful method in cracked tooth detection and the indicators have acceptable accuracy to find the crack depth ratio.

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Intelligent fault diagnosis method for rotating machinery via dictionary learning and sparse representation-based classification

Cited by 64 publications

References 38 publications

Fast Spectral Correlation Based on Sparse Representation Self-Learning Dictionary and Its Application in Fault Diagnosis of Rotating Machinery

Fast Spectral Correlation Based on Sparse Representation Self-Learning Dictionary and Its Application in Fault Diagnosis of Rotating Machinery

Automatic Identification of Epileptic Seizures From EEG Signals Using Sparse Representation-Based Classification

Experimental Investigation of Spur Gear Tooth Crack Location and Depth Detection using Short-time Averaging Method and Statistical Indicators

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