Teager Energy Spectrum for Fault Diagnosis of Rolling Element Bearings

Feng, Zhipeng; Wang, Tianjin; Zuo, Ming J.; Chu, Fulei; Yan, Shaoze

doi:10.1088/1742-6596/305/1/012129

Cited by 4 publications

(4 citation statements)

References 5 publications

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“…In all those studies, it was investigated how to identify a big damage size (1mm in depth, 1.5mm width of the groove). Feng et al (2011) utilized the Fourier spectrum of Teager energy to identify the characteristic frequency of faulty bearings (very big defect sizes: 2mm diameter and 1mm depth). Liu et al (2013) presented an approach to bearing fault diagnosis based on the TKEO and the Elman neural network.…”

Section: Introductionmentioning

confidence: 99%

A Novel feature extraction for anomaly detection of roller bearings based on performance improved Ensemble Empirical Mode Decomposition and Teager-Kaiser Energy Operator

Zarringhabaei

Garibaldi

Fasana

et al. 2020

IJPHM

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Although Ensemble empirical mode decomposition (EEMD) method has been successfully applied to various applications, features extracted using EEMD could not detect anomalies for roller bearings, especially when anomalies includes small defects. In this study a novel feature extraction method is proposed to detect the state of roller bearings. Performance improved EEMD, which is a reliable adaptive method to calculate an appropriate noise amplitude is applied to decompose the acceleration signals into zero-mean components called intrinsic mode functions (IMFs). Then, three dimensional feature vectors are created by applying the Teager-Kaiser energy operator (TKEO) to the first three IMFs. The novel features obtained from the healthy bearing signals are utilized to construct the separating hyperplane using one-class support vector machine (SVM). In order to validate the method proposed, a number of operating conditions (shaft speed and load) are considered to generate the data (vibration signals) by means of an assembled test rig. It is shown that the proposed method can successfully identify the states of the new samples (healthy and faulty). The uncertainty of the model prediction is investigated computing Margin and the number of support vectors. It create less complex (less fraction of support vectors) and more reliable (higher Margin) hyperplane than the EEMD method.

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Section: Introductionmentioning

confidence: 99%

A Novel feature extraction for anomaly detection of roller bearings based on performance improved Ensemble Empirical Mode Decomposition and Teager-Kaiser Energy Operator

Zarringhabaei

Garibaldi

Fasana

et al. 2020

IJPHM

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“…Shen et al [13] presented a model that used empirical mode decomposition (EMD) to select silent features and put these into multi-class transductive SVM (TSVM), thereby obtaining an accuracy of 91.62% in diagnosing the faults of a gear reducer. Feng et al [14] proposed a method called Teager energy spectrum to extract the fault induced impulses as features to conduct bearing fault diagnosis, as well as proved the superiority of this method in recognizing transient components in signals and in identifying the characteristic frequency of bearing faults. Cai et al [15] introduced a high order spectrum to reconstruct the signals' power spectrum, and used it to extract fault feature information.…”

Section: Introductionmentioning

confidence: 99%

Deep Fault Recognizer: An Integrated Model to Denoise and Extract Features for Fault Diagnosis in Rotating Machinery

2016

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Fault diagnosis in rotating machinery is significant to avoid serious accidents; thus, an accurate and timely diagnosis method is necessary. With the breakthrough in deep learning algorithm, some intelligent methods, such as deep belief network (DBN) and deep convolution neural network (DCNN), have been developed with satisfactory performances to conduct machinery fault diagnosis. However, only a few of these methods consider properly dealing with noises that exist in practical situations and the denoising methods are in need of extensive professional experiences. Accordingly, rethinking the fault diagnosis method based on deep architectures is essential. Hence, this study proposes an automatic denoising and feature extraction method that inherently considers spatial and temporal correlations. In this study, an integrated deep fault recognizer model based on the stacked denoising autoencoder (SDAE) is applied to both denoise random noises in the raw signals and represent fault features in fault pattern diagnosis for both bearing rolling fault and gearbox fault, and trained in a greedy layer-wise fashion. Finally, the experimental validation demonstrates that the proposed method has better diagnosis accuracy than DBN, particularly in the existing situation of noises with superiority of approximately 7% in fault diagnosis accuracy.

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“…The performance of rolling bearings is influenced by many factors, such as rotation speed, temperature, and lubrication conditions [15,16]. The running state is the final results of those factors.…”

Section: Introductionmentioning

confidence: 99%

“…Vibration analysis is a powerful tool for fault diagnosis and degradation assessment [ 11 – 14 ]. The performance of rolling bearings is influenced by many factors, such as rotation speed, temperature, and lubrication conditions [ 15 , 16 ]. The running state is the final results of those factors.…”

Section: Introductionmentioning

confidence: 99%

A Monotonic Degradation Assessment Index of Rolling Bearings Using Fuzzy Support Vector Data Description and Running Time

Shen

Chen

et al. 2012

Sensors

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Performance degradation assessment based on condition monitoring plays an important role in ensuring reliable operation of equipment, reducing production downtime and saving maintenance costs, yet performance degradation has strong fuzziness, and the dynamic information is random and fuzzy, making it a challenge how to assess the fuzzy bearing performance degradation. This study proposes a monotonic degradation assessment index of rolling bearings using fuzzy support vector data description (FSVDD) and running time. FSVDD constructs the fuzzy-monitoring coefficient ε̄ which is sensitive to the initial defect and stably increases as faults develop. Moreover, the parameter ε̄ describes the accelerating relationships between the damage development and running time. However, the index ε̄ with an oscillating trend disagrees with the irreversible damage development. The running time is introduced to form a monotonic index, namely damage severity index (DSI). DSI inherits all advantages of ε̄ and overcomes its disadvantage. A run-to-failure test is carried out to validate the performance of the proposed method. The results show that DSI reflects the growth of the damages with running time perfectly.

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Teager Energy Spectrum for Fault Diagnosis of Rolling Element Bearings

Cited by 4 publications

References 5 publications

A Novel feature extraction for anomaly detection of roller bearings based on performance improved Ensemble Empirical Mode Decomposition and Teager-Kaiser Energy Operator

A Novel feature extraction for anomaly detection of roller bearings based on performance improved Ensemble Empirical Mode Decomposition and Teager-Kaiser Energy Operator

Deep Fault Recognizer: An Integrated Model to Denoise and Extract Features for Fault Diagnosis in Rotating Machinery

A Monotonic Degradation Assessment Index of Rolling Bearings Using Fuzzy Support Vector Data Description and Running Time

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