Weiguo Huang

Adaptive spectral kurtosis filtering based on Morlet wavelet and its application for signal transients detection

Spectral kurtosis (SK) provides a valuable tool for detecting the signal transients buried in noise, which makes it very powerful for designing a filter to extract the signal transients. However, SK requires the selection of a time-frequency frame for decomposition based on Short Time Fourier Transform (STFT). This paper presents an adaptive spectral kurtosis filtering technique to extract the signal transients based on Morlet wavelet. The Morlet wavelet is used as a filter bank whose center frequency is defined by the wavelet correlation filtering. Different bandwidth filter in the filter bank is used to select the optimal filter for extracting the signal transients as the one that maximizes the SK. Effectiveness of the proposed technique is verified through the transient extraction of a simulate signal. For the gear fault feature detection of vehicle transmission gearbox, the proposed technique is applied in the extraction of the signal transients that shows the gear fault, which proves the effectiveness of the proposed technique in extracting the signal transients in the practical application.

Adaptive parameter identification based on morlet wavelet and application in gearbox fault feature detection

Localized defects in rotating mechanical parts tend to result in impulse response in vibration signal, which contain important information about system dynamics being analyzed. Thus, parameter identification of impulse response provides a potential approach for localized fault diagnosis. A method combining the Morlet wavelet and correlation filtering, named Cyclic Morlet Wavelet Correlation Filtering (CMWCF), is proposed for identifying both parameters of impulse response and the cyclic period between adjacent impulses. Simulation study concerning cyclic impulse response signal with different SNR shows that CMWCF is effective in identifying the impulse response parameters and the cyclic period. Applications in parameter identification of gearbox vibration signal for localized fault diagnosis show that CMWCF is effective in identifying the parameters and thus provides a feature detection method for gearbox fault diagnosis.

Research on the Sparse Representation for Gearbox Compound Fault Features Using Wavelet Bases

The research on gearbox fault diagnosis has been gaining increasing attention in recent years, especially on single fault diagnosis. In engineering practices, there is always more than one fault in the gearbox, which is demonstrated as compound fault. Hence, it is equally important for gearbox compound fault diagnosis. Both bearing and gear faults in the gearbox tend to result in different kinds of transient impulse responses in the captured signal and thus it is necessary to propose a potential approach for compound fault diagnosis. Sparse representation is one of the effective methods for feature extraction from strong background noise. Therefore, sparse representation under wavelet bases for compound fault features extraction is developed in this paper. With the proposed method, the different transient features of both bearing and gear can be separated and extracted. Both the simulated study and the practical application in the gearbox with compound fault verify the effectiveness of the proposed method.

The elimination of cross-terms based on the fusion of time-frequency features and its application in machine fault diagnosis

Time-frequency representation, as a commonly method for non-stationary signals processing, includes linear time-frequency representation and bilinear time-frequency representation. Though low in time-frequency concentration, there are no cross-terms in linear time-frequency representation; in contrast, bilinear time-frequency representation, high in resolution, suffer seriously from the cross-terms, which shall lead to the ambiguous representation of a signal in the time-frequency domain. A fusion strategy for elimination of cross-terms based on linear time-frequency feature and bilinear time-frequency feature was proposed, which not only reduces the cross-terms but also preserves the superb time-frequency concentration of the bilinear time-frequency analysis. Application in bearing fault diagnosis shows that this strategy is effective in exacting the time-frequency features in vibration signal.

Transients Extraction Based on Averaged Random Orthogonal Matching Pursuit Algorithm for Machinery Fault Diagnosis

Sparse representation based on matching pursuit (MP) algorithm is one of the effective methods for the extraction of weak feature contaminated by heavy noise. However, the optimal iterative threshold and iterations of MP algorithm are difficult to be determined and the sparsest representation is difficult to obtain for pursuit algorithms. In order to reduce the influence of the threshold and iterations on the performance of MP and obtain appropriate results without seeking the sparsest representation, a new transient feature extraction technique named averaged random orthogonal MP (AROMP) algorithm is proposed. In the proposed method, random orthogonal MP algorithm, which is a greedy algorithm to match the atoms with probability, is utilized repeatedly to generate a group of competitive representations for the mechanical vibration signal. Then by averaging these solutions, the estimated representation vector can be obtained to represent the vibration signal and then transients can be extracted from the noisy signal. The simulation study and experimental analysis show that transients can be extracted effectively from the noisy vibration signal. And comparison results between the proposed method and orthogonal MP show that the proposed algorithm is less dependent on iterations and can obtain a better performance for transients extraction. Comparisons between the proposed algorithm and spectral kurtosis also show the superiority of the proposed method.

A parameterized Doppler distorted matching model for periodic fault identification in locomotive bearing

The locomotive bearings support the whole weight of the train under the high speed and its continuous running is a key factor of the train safety. The collected Doppler distorted signal greatly increases the difficulties in detecting the whelmed fault information. To overcome this disadvantage, a novel Doppler distorted correlation matching model using the single side Laplace wavelet and acoustic theories is constructed to recognize the bearing fault-related impulse intervals. The parameterized Doppler distorted model is assessed by the correlation coefficient with the bearing fault signal in waveform. The optimal Doppler distorted model, that is the Doppler distorted correlation matching model which matches the fault component in the acoustic signal could be used for identifying the fault impulse interval. Then, the bearing fault can be successfully detected from the parameters of the Doppler distorted correlation matching model due to its maximum similarity to the real signal. Except for the simulation study, the proposed model is also employed to match the fault components in some real acoustic bearing fault signals. The recognized fault impulse period in the Doppler distorted correlation matching model’s initial model keep in good accordance with the correct fault impacts, which represent the locomotive bearing fault characteristics.

Adaptive deep feature learning network with Nesterov momentum and its application to rotating machinery fault diagnosis

Abstract The effective fault diagnosis of rotating machinery is critical to ensure the continuous operation of equipment and is more economical than scheduled maintenance. Traditional signal processing-based and artificial intelligence-based methods, such as wavelet packet transform and support vector machine, have been proved effective in fault diagnosis of rotating machinery, which prevents unexpected machine breakdowns due to the failure of significant components. However, these methods have several disadvantages that make them unable to automatically and effectively extract valid fault features for the effective fault diagnosis of rotating machinery. A novel adaptive learning rate deep belief network combined with Nesterov momentum is developed in this study for rotating machinery fault diagnosis. Nesterov momentum is adopted to replace traditional momentum to enable declining in advance and to improve training performance. Then, an individual adaptive learning rate method is used to select a suitable step length for accelerating descent. To confirm the utility of the proposed deep learning network architecture, two examinations are implemented on datasets from gearbox and locomotive bearing test rigs. Results indicate that the method achieves impressive performance in fault pattern recognition. Comparisons with existing methods are also conducted to demonstrate that the proposed method is more accurate and robust.

Hierarchical diagnosis network based on sparse deep neural networks and its application in bearing fault diagnosis

As a breakthrough in artificial intelligence, deep learning allows for the automatic extraction of features without considerable prior knowledge and the determination of the complex non-linear relationship of the input parameters. Owing to these advantages, deep neural networks (DNNs) are superior to traditional artificial neural networks with shallow architectures, and are thus becoming widely used in the fault diagnosis field. To overcome the difficulties in bearing fault type classification and severity diagnosis, a two-layer hierarchical fault diagnosis network based on sparse DNNs is presented in this paper. In the proposed hierarchical fault diagnosis network, the first layer is responsible for fault type identification, and the second layer is responsible for fault severity diagnosis. An autoregressive (AR) model is established using empirical mode decomposition (EMD) to obtain the AR parameters as the input vectors of the proposed diagnosis network. The AR parameters are regarded as the low-level features which are helpful for the diagnosis network mine more useful high-level features. Experiments and comparison analyses are conducted to verify the performance of the proposed hierarchical fault diagnosis network. Results fully demonstrate the superiority of the proposed hierarchical fault diagnosis network in fault type classification or fault severity diagnosis.

Sparse representation of gearbox compound fault features by combining Majorization-Minimization algorithm and wavelet bases

Bearing and gear are essential components in gearbox, which is easily damaged and breaks down. Thus gearbox compound fault diagnosis has become a challenging topic in recent decades. Both bearing and gear faults in the gearbox tend to result in different transient impulse responses in the vibration signal and therefore it is necessary to present a method whose main task is to extract the different fault features. In this paper, a sparse representation method combining majorization minimization (MM) algorithm and different wavelet bases is proposed to resolve the problem. Through the proposed method, different transients buried in the noisy signal can be converted into sparse coefficients. Both the simulation study and the practical application show the proposed method is effective in extracting gearbox compound fault features.

Extraction of transients via the combination of wavelet basis and majorization minimization algorithm

Ill-posed linear inverse problems (IPLIP), such as restoration and reconstruction, are core topics of transient feature extraction in cyclostationary signal processing. This paper presents a novel method for extracting transients through exploiting both the wavelet basis and the majorization minimization algorithm. To solve the objective function, majorization minimization (MM) algorithm is applied with a designed strictly convex quadratic function. To implement the sparsity of the results, an optimal basis with its atom selected by correlation filtering is proposed. Through an iterative optimization procedure, transients hidden in the noisy signal can be converted into sparse coefficients. Simulated study concerning cyclic transient signal shows the effectiveness of this method. Applications on a rotating machine test rig with bearing fault also demonstrate the validity of the technique. Both the simulated study and the applications show that the proposed method is powerful in the representation of transients and is an effective tool to extract the transient features of bearings.