A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings

Recent progress of chatter prediction, detection and suppression in milling

Bearing faults are one of the most common inducements for machine failures. Therefore, it is very important to perform bearing fault diagnosis reliably and rapidly. However, it is fundamental but difficult to extract impulses buried in heavy background noise for bearing fault diagnosis. In this paper, a novel adaptive enhanced sparse period-group lasso (AdaESPGL) algorithm for bearing fault diagnosis is proposed. The algorithm is based on the proposed enhanced sparse group lasso penalty, which promotes the sparsity within and across groups of the impulsive feature of bearing faults. Moreover, a periodic prior is embedded and updated dynamically through each iteration of the optimization procedure. Additionally, we formed a deterministic rule about how to set the parameters adaptively. The main advantage over conventional sparse representation methods is that AdaESPGL is parameter free (forming a deterministic rule) and rapid (extracting the impulsive information directly from the time domain). Finally, the performance of AdaESPGL is verified through a series of numerical simulations and the diagnosis of a motor bearing. Results demonstrate its superiority in extracting periodic impulses in comparison to other state-of-the-art methods.

Enhanced Sparse Period-Group Lasso for Bearing Fault Diagnosis

Latest developments in gear defect diagnosis and prognosis: A review

Intelligent Fault Diagnosis of Rotating Machinery via Wavelet Transform, Generative Adversarial Nets and Convolutional Neural Network

Sparse representation based on parametric impulsive dictionary design for bearing fault diagnosis

Gear fault diagnosis based on the structured sparsity time-frequency analysis

The most common and effective way of rotating machinery diagnostics is to extract fault impact features from the vibration signals and to carry out further processing. As for planetary gear sets, because of the simultaneous mesh of multiple gears and the effect of the carrier rotation, the fault features are submerged in the strong harmonic signals and other noise. Due to the lack of prior information on the faults, the conventional fault diagnostic methods often fail to achieve satisfactory results. To address this problem, we give the prior information of a chipped planetary gear set by dynamics simulation, and utilize the information in the time domain to improve the diagnosis performance. Firstly, a pure torsional lumped parameter model is used to simulate the system vibration response with different degrees of failure. Through statistical analysis, the margin factor, the most sensitive indicator, is selected as prior information to reflect the local gear fault among several indexes. Finally, a weighted sparse representation method based on the prior information provided above is proposed to extract the impact features. Moreover, it is found that the extracted components have a strong–weak cyclic impact feature in the chipped planetary gear sets. The features and effectiveness of the method are verified by an experiment on a planetary gearbox test rig. To validate the superiority of the proposed method, comparisons are made among several state-of-the-art feature extraction methods.

https://iopscience.iop.org/article/10.1088/1361-6501/ab02d8/pdf

Weighted sparse representation based on failure dynamics simulation for planetary gearbox fault diagnosis

Mechanical fault diagnosis under nonstationary conditions is one of the hotspots in the condition monitoring research field, presenting still several difficulties. Vibration signals of faulty bearings exhibit second-order cyclostationarity when rotating speed and load are constant. Due to the time-varying rotating speed, the original cyclostationary signal becomes a special type of nonstationary signal. Considering the superiority of cyclostationary analysis in bearing fault diagnosis, it is interesting to extend the cyclostationary paradigm to make it suitable for particular signals that show irregular statistical cyclicity (ISC). Modeling bearing vibrations with speed fluctuation, however, is a controversial topic, which further sparked a debate on how to process bearing signals with ISC. Therefore, in this article, a detailed comparison of two signal models is made, the pace irregularity and the time warping, to illustrate the advantages and disadvantages of the modeling ideas. Based on these models, two novel tacholess diagnostic methods, the synchronization-sequence method and the dewarping method, are proposed for bearing diagnosis in nonstationary operations. By identifying the impact points with the same cyclic feature, the synchronization-sequence method shifts the impulse patches to the same interval, so the signal becomes cyclostationary. Using another strategy, the dewarping method recovers the regular statistical property by maximizing the squared modulus of the cyclic autocorrelation of specific feature points. The results show that both models can help diagnose local bearing faults, and the classical cyclostationary diagnostic tools become more powerful when extending to nonstationary operation conditions. In addition, the speed fluctuation curve can also be estimated by the two methods without using a tachometer. The characteristic of the proposed methods for speed estimation is that only the second-order cyclostationary information is utilized. Besides, the methods are robust to noise, so they are suitable for processing bearing data in the incipient failure stage. Numerical simulations and experimental data analyses corroborate the theoretical results.

Cyclostationary Analysis of Irregular Statistical Cyclicity and Extraction of Rotating Speed for Bearing Diagnostics With Speed Fluctuations

Fault diagnosis under time-varying operational conditions is always the challenge in industrial systems. The chirplet transform (CT) provides an effective first-order approximation approach, but the slow operation speed and the drawback for high-order signal analysis limit its uses. Focusing on this problem, a fast nonlinear chirplet dictionary-based sparse decomposition (FNC-SD) method for nonlinear signal analysis is proposed. By replacing the time–frequency inclination parameter in the CT with a frequency bending parameter, the proposed method can track the nonstationary signals by arbitrary order polynomial law with time by an additional degree. Considering that too many parameters will slow down the operation speed, a frequency bending operator estimation algorithm is also proposed in this paper based on the calculated rotating frequency. Therefore, a laser vibrometer is needed in the experiment to collect the impulse signals, which can represent rotating speed signals. Then, the FNC-SD method is used to construct a redundant dictionary for signal sparse time–frequency representation. The high similarity between signals and atoms can make the method prefer to choose the right atoms, instead of being influenced by noise. Therefore, the proposed method also shows a strong noise robustness. Two simulation experiments are constructed to verify the performance of the proposed method. Finally, the FNC-SD method is used to diagnose a faulty motor with broken bar. The analyzed results and comparisons with respect to the state-of-the-art methods are illustrated in detail, as well as the convergence accuracy and speed, which highlight the superiority of the proposed method.

Ruo-Bin Sun

Papers

Sparse representation based on parametric impulsive dictionary design for bearing fault diagnosis

Gear fault diagnosis based on the structured sparsity time-frequency analysis

Weighted sparse representation based on failure dynamics simulation for planetary gearbox fault diagnosis

Cyclostationary Analysis of Irregular Statistical Cyclicity and Extraction of Rotating Speed for Bearing Diagnostics With Speed Fluctuations

Fast Nonlinear Chirplet Dictionary-Based Sparse Decomposition for Rotating Machinery Fault Diagnosis Under Nonstationary Conditions