Application of multi-domain sparse features for fault identification of planetary gearbox

Zhao, Chuan; Feng, Zhipeng

doi:10.1016/j.measurement.2017.03.016

Cited by 42 publications

(19 citation statements)

References 22 publications

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“…Subsequently, a physical interpretation to sparse filtering was achieved, in which trained weight vectors could be compared directly to the Gabor filters. Zhao et al [29] used sparse filtering to extract multi-domain sparse features and adopted it for fault identification in a planetary gearbox. Jiang et al [30] proposed a multiscale representation learning (MSRL) framework, which was based on sparse filtering, to learn useful features from a direct interpretation of raw vibration signals; the aim was to capture rich and complementary fault pattern information at different scales.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Fault Diagnosis Approach Considering the Elimination of the Weight Matrix Multi-Correlation

Wang

et al. 2018

Applied Sciences

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Faults in bearings and gearboxes, which are widely used in rotating machines, can lead to heavy investment and productivity loss. Accordingly, a fault diagnosis system is necessary to ensure a high-performance transmission. However, as mechanical fault diagnosis enters the era of big data, it can be difficult to apply traditional fault diagnosis methods because of the massive computation cost and excessive reliance on human labor. Meanwhile, unsupervised learning has been shown to have excellent performance in processing machanical big data. In this paper, an unsupervised learning method known as sparse filtering is applied, the multi-correlation of a weight matrix is investigated, and a method that is more suitable for the feature extraction of signals is proposed. The main contribution of our work is the modification of original method. First, to understand the non-monotonicity testing accuracies of the original method, the physical interpretation of input dimensions is studied. Second, using the physical interpretation, an overfitting phenomenon is discovered and examined. Third, to reduce the overfitting phenomenon, a method which eliminates the multi-correlation of the weight matrix is proposed. Finally, bearing and gear datasets are employed to verify the effectiveness of the proposed method; experimental results show that the proposed method can achieve a superior performance in comparison to the original sparse filtering model.

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Section: Introductionmentioning

confidence: 99%

An Intelligent Fault Diagnosis Approach Considering the Elimination of the Weight Matrix Multi-Correlation

Wang

et al. 2018

Applied Sciences

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“…So, it can effectively alleviate or avoid a series of problems that exist in EMD, and has higher operation efficiency. VMD is widely used in various engineering fields [21][22][23][24][25]. An X. L. et al applied VMD to the bearing fault diagnosis of the wind turbine, and realized the effective discrimination of the bearing fault [26].…”

Section: Introductionmentioning

confidence: 99%

Variational mode decomposition denoising combined with the Euclidean distance for diesel engine vibration signal

Ren¹,

Jin²,

Mei³

et al. 2018

J. vibroeng.

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Variational mode decomposition (VMD) is a recently introduced adaptive signal decomposition algorithm with a solid theoretical foundation and good noise robustness compared with empirical mode decomposition (EMD). There is a lot of background noise in the vibration signal of diesel engine. To solve the problem, a denoising algorithm based on VMD and Euclidean Distance is proposed. Firstly, a multi-component, non-Gauss, and noisy simulation signal is established, and decomposed into a given number K of band-limited intrinsic mode functions by VMD. Then the Euclidean distance between the probability density function of each mode and that of the simulation signal are calculated. The signal is reconstructed using the relevant modes, which are selected on the basis of noticeable similarities between the probability density function of the simulation signal and that of each mode. Finally, the vibration signals of diesel engine connecting rod bearing faults are analyzed by the proposed method. The results show that compared with other denoising algorithms, the proposed method has better denoising effect, and the fault characteristics of vibration signals of diesel engine connecting rod bearings can be effectively enhanced.

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“…Among the advantages of planetary gear transmission system, high transmission ratio, strong load capacity, small size, and light weight contributed to its success in many industrial applications such as wind power equipment, engineering machinery, and aerospace [1,2]. However, planetary gear transmission systems are subjected to hostile long-lasting working conditions, e.g., variable load, frequent start and stop, and high speed, which inevitably leads to different types of failures of the critical parts of planetary gearboxes [3].…”

Section: Introductionmentioning

confidence: 99%

Research on Fault Evolution Feature Extraction and Identification of Sun Gear Cracks in Planetary Gearbox Based on Volterra High-Order Kernel Generalized Frequency Response Graphic Analysis

Wang

Kang

Shi

et al. 2018

Mathematical Problems in Engineering

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In this paper, relying on the Volterra series nonlinear system model and the high-order kernel Hilbert’s reconstructed kernel fast solved algorithm, a fault feature frequency domain identification method based on Volterra high-order kernel generalized frequency response graph analysis is proposed. Firstly, the method uses the system input and output vibration signals to determine the Volterra model. Then, the Volterra high-order kernel function is solved quickly by reproducing kernel Hilbert space method, and the generalized frequency response function is used to identify the model. Finally, multidimensional high-order spectral pattern analysis is used to separate and extract the fault and degree characteristic information implied by frequency and phase coupling in the third-order kernel function. Following the theoretical approach, in the experimental part, this paper uses the planetary gearbox fault loading test rig to complete the data collection and establishes the Volterra experimental model through the measured data. The generalized frequency responses of each order kernel function are compared and analyzed and the capability of distinguishing and the adaptability of different order kernel functions for the degree of crack failure are discussed. The effects of changing the memory length of the Volterra model and the order of the kernel function on the recognition result are verified. The final experimental results show that the use of reproducing kernel Hilbert space can effectively avoid the dimension disaster problem that occurs in the high-order kernel solution process. Moreover, the third-order kernel can describe more intuitively the nonlinear system model under multifactor coupling than the second-order kernel. Finally, Volterra series model the third-order kernel’s generalized frequency response can effectively distinguish between nondefective and faulty gears, and its resolution is enough to distinguish the degree of failure of gear cracks.

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Application of multi-domain sparse features for fault identification of planetary gearbox

Cited by 42 publications

References 22 publications

An Intelligent Fault Diagnosis Approach Considering the Elimination of the Weight Matrix Multi-Correlation

An Intelligent Fault Diagnosis Approach Considering the Elimination of the Weight Matrix Multi-Correlation

Variational mode decomposition denoising combined with the Euclidean distance for diesel engine vibration signal

Research on Fault Evolution Feature Extraction and Identification of Sun Gear Cracks in Planetary Gearbox Based on Volterra High-Order Kernel Generalized Frequency Response Graphic Analysis

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