Investigation on Planetary Bearing Weak Fault Diagnosis Based on a Fault Model and Improved Wavelet Ridge

Li, Hongkun; Yang, Rui; Wang, Chaoge; He, Chunlin

doi:10.3390/en11051286

Cited by 5 publications

(5 citation statements)

References 36 publications

(52 reference statements)

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“…Through the study of the fault mechanism of rolling bearings, as described in paper [26], the fault signal characteristics of rolling bearings are close to the Morlet wavelet. Therefore, Morlet is used as a wavelet basis for analysis when constructing two-dimensional feature space based on WSC.…”

Section: Construction Of Two-dimensional Feature Spacementioning

confidence: 80%

Data Augmentation in 2D Feature Space for Intelligent Weak Fault Diagnosis of Planetary Gearbox Bearing

Yang

Huang

et al. 2022

Applied Sciences

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Quickly detecting and accurately diagnosing early bearing faults is the key to ensuring the stable operation of high-precision equipment. In actual industrial applications, it is common to face the issues of big data and poor fault identification accuracy. To accurately and automatically realize the diagnostics of rolling bearings, a convolutional neural network algorithm and fault feature enhancement method is proposed. A two-dimensional space feature extraction method based on the Cyclostationary theory and wavelet transform shows good results in noise suppression. Firstly, the cyclic demodulation of wavelet transform coefficients is performed on bearing vibration signals to convert one-dimensional vibration data into a two-dimensional spectrogram for enhancing the weak fault feature. Secondly, the image segmentation theory is introduced, which can obtain more data and improve the calculation accuracy and efficiency on the basis of data dimension reduction. Finally, the augmented 2D spectrograms are inputted into a convolutional neural network. Through the analysis of the actual planetary gearbox bearing data, and compared with other mainstream intelligence algorithms, the effectiveness and superiority of this method are verified.

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Section: Construction Of Two-dimensional Feature Spacementioning

confidence: 80%

Data Augmentation in 2D Feature Space for Intelligent Weak Fault Diagnosis of Planetary Gearbox Bearing

Yang

Huang

et al. 2022

Applied Sciences

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“…13, the events that exceed the dynamic self-learning threshold line have occurred for the following data files: No. 6,No.70,No.79,No.81,No.107,No.115,No.121,No.127,No.132,and No.142. However, starting with data file No.…”

Section: B Engineering Case Verificationmentioning

confidence: 99%

“…In recent years, researchers have proposed many IFD methods which can be divided into physical model-based methods and data-driven methods [3]. In [4][5][6], an IFD method based on prior physical knowledge, which requires the establishment of an accurate physical model, has been investigated. However, the internal stress and other physical quantities of the running rolling bearings are relatively difficult to characterize, which often results in poor model generalization [7].…”

Section: Introductionmentioning

confidence: 99%

Online Incipient Fault Detection Method Based on Improved ℓ₁ Trend Filtering and Support Vector Data Description

et al. 2021

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Poor model generalization, missing or false alarms, and heavy dependence on expert's experience are some of the major problems which exist in traditional incipient fault detection (IFD) methods. An IFD rolling bearing application method based on combination of improved  1 trend filtering (L1TF) and support vector data description (SVDD) is proposed. First, spectral distance index and multi-scale dispersion entropy based on normal vibration data, which is sensitive to incipient faults, are extracted. The improved  1 trend filter (IL1TF) method is employed for processing the feature values and obtaining a trend factor with less fluctuation and better incipient fault indication ability. Then, after determining the kernel function bandwidth of the SVDD by analyzing the characteristics of the training data, a suitable offline SVDD model is trained. Finally, incipient faults are identified by estimating the distance between the trend factor of the real-time data and the center of the hypersphere in the SVDD model. This method employs full performance of SVDD to detect abnormal data files, while reducing the influence of abnormal data files on the model via IL1TF. Furthermore, the method increases the discrimination between the incipient fault data and the normal data. By utilizing Intelligent Maintenance Systems of University of Cincinnati bearing laboratory data and Chinese petrochemical company's centrifugal pump bearing engineering data, the effectiveness of the constructed model is demonstrated. In addition, the proposed method is compared against existing representative IFD methods. The results indicate that the method proposed in this paper can solve false alarms and detect incipient failure data files more accurately without depending on the external expert's experience. This is of great significance for providing guidelines to enterprises which employ predictive maintenance techniques. INDEX TERMS Improved  1 trend filtering, support vector data description, kernel function bandwidth determination, incipient fault detection

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“…Wavelet transform is a signal processing technique, widely applied and developed in many different areas [25][26][27]. Due to its success in many applications, several works attempt to motivate and explain the basic ideas behind wavelets [28].…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

Nonlinear Autoregressive Neural Network Models for Prediction of Transformer Oil-Dissolved Gas Concentrations

et al. 2018

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Transformers are one of the most important part in a power system and, especially in key-facilities, they should be closely and continuously monitored. In this context, methods based on the dissolved gas ratios allow to associate values of gas concentrations with the occurrence of some faults, such as partial discharges and thermal faults. So, an accurate prediction of oil-dissolved gas concentrations is a valuable tool to monitor the transformer condition and to develop a fault diagnosis system. This study proposes a nonlinear autoregressive neural network model coupled with the discrete wavelet transform for predicting transformer oil-dissolved gas concentrations. The data fitting and accurate prediction ability of the proposed model is evaluated in a real world example, showing better results in relation to current prediction models and common time series techniques.

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Investigation on Planetary Bearing Weak Fault Diagnosis Based on a Fault Model and Improved Wavelet Ridge

Cited by 5 publications

References 36 publications

Data Augmentation in 2D Feature Space for Intelligent Weak Fault Diagnosis of Planetary Gearbox Bearing

Data Augmentation in 2D Feature Space for Intelligent Weak Fault Diagnosis of Planetary Gearbox Bearing

Online Incipient Fault Detection Method Based on Improved ℓ₁ Trend Filtering and Support Vector Data Description

Nonlinear Autoregressive Neural Network Models for Prediction of Transformer Oil-Dissolved Gas Concentrations

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Investigation on Planetary Bearing Weak Fault Diagnosis Based on a Fault Model and Improved Wavelet Ridge

Cited by 5 publications

References 36 publications

Data Augmentation in 2D Feature Space for Intelligent Weak Fault Diagnosis of Planetary Gearbox Bearing

Data Augmentation in 2D Feature Space for Intelligent Weak Fault Diagnosis of Planetary Gearbox Bearing

Online Incipient Fault Detection Method Based on Improved ℓ1 Trend Filtering and Support Vector Data Description

Nonlinear Autoregressive Neural Network Models for Prediction of Transformer Oil-Dissolved Gas Concentrations

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Online Incipient Fault Detection Method Based on Improved ℓ₁ Trend Filtering and Support Vector Data Description