Multistage Fault Feature Extraction of Consistent Optimization for Rolling Bearings Based on Correlated Kurtosis

Zhao

Wang

et al. 2022

Sensors

Self Cite

For the sake of addressing the issue of extracting multiple features embedded in a noise-heavy vibration signal for bearing compound fault diagnosis, a novel model based on improved adaptive chirp mode decomposition (IACMD) and sparse representation, namely IACMDSR, is developed in this paper. Firstly, the IACMD is employed to simultaneously separate the distinct fault types and extract multiple resonance frequencies induced by them. Next, an adaptive bilateral wavelet hyper-dictionary that digs deeper into the periodicity and waveform characteristics exhibited by the real fault impulse response is constructed to identify and reconstruct each type of fault-induced feature with the help of the orthogonal matching pursuit (OMP) algorithm. Finally, the fault characteristic frequency can be detected via an envelope demodulation analysis of the reconstructed signal. A simulation and two sets of experimental results confirm that the developed IACMDSR model is a powerful and versatile tool and consistently outperforms the leading MCKDSR and MCKDMWF models. Furthermore, the developed model has satisfactory capability in practical applications because the IACMD has no requirement for the input number of the signal components and the adaptive bilateral wavelet is powerfully matched to the real fault-induced impulse response.

Section: Comparison and Analysis Of Experimental Results (1) The Mckd...mentioning

confidence: 99%

Multiple Enhanced Sparse Representation via IACMDSR Model for Bearing Compound Fault Diagnosis

Zhao

Wang

et al. 2022

Sensors

Self Cite

“…We present a time-series prediction model for anomaly detection, and this kind of problem can be found in many industrial scenarios. [30][31][32] Since the model proposed in this article performs well in WT blades icing detection, it is conceivable that our proposed model should also be applicable to those problems, which needs to be further verified in the future.…”

Section: Discussionmentioning

confidence: 99%

A PCC-Ensemble-TCN model for wind turbine icing detection using class-imbalanced and label-missing SCADA data

Ding

Wang

International Journal of Distributed Sensor Networks

et al. 2021

Blade icing problems are ubiquitous for wind turbines located in cold climate zones. Data-driven indirect icing detection methods based on supervisory control and data acquisition system have shown strong potential recently. However, the supervisory control and data acquisition data is annotated through manual observation, which will cause the data between normal condition and icing condition to be unlabeled. In addition, the amount of normal data is far more than icing data. The above two issues restrict the performance of most current data-driven models. In order to solve the label missing problem, this article proposes a Pearson correlation coefficient–based algorithm for measuring the degree of blade icing, which calculates the similarity between the unlabeled data and the icing data as its label. Aiming at the class-imbalance problem, this article constructs multiple class-balanced subsets from the original dataset by under-sampling the normal data. Temporal convolutional networks are trained to extract features and make predictions on each subset. The final prediction result is obtained by ensembling the prediction results of all temporal convolutional network models. The proposed model is validated using the actual supervisory control and data acquisition data collected from a wind farm in northern China, and the results indicate that ensuring the consecutiveness and class-balance of the data are quite advantageous for improving the detection accuracy.

“…e fault feature extraction results of the practical combined failure signals were verified [21]. Zhang et al employed MCKD to weaken the influence of the transmission path followed by TQWT, and then the signals were decomposed into multiple sub-bands in different Q values to enhance the fault features [22]. e number of shock pulses extracted by MCKD was increased, but only a limited number of shocks could be extracted locally.…”

Section: Introductionmentioning

confidence: 94%

“…To illustrate the effect of L on the CYCBD results, a simulated signal y is constructed, as shown in formula (22). e signal consists of periodic pulses and random noise:…”

Section: Effects Of Filter Lengths L On Cycbdmentioning

confidence: 99%

Research on Fault Extraction Method of CYCBD Based on Seagull Optimization Algorithm

Pan

Qing

et al. 2021

Shock and Vibration

Maximum cyclostationarity blind deconvolution (CYCBD) can recover the periodic impulses from mixed fault signals comprised by noise and periodic impulses. In recent years, blind deconvolution has been widely used in fault diagnosis. However, it requires a preset of filter length, and inappropriate filter length may cause the inaccurate extraction of fault signal. Therefore, in order to determine filter length adaptively, a method to optimize CYCBD by using the seagull optimization algorithm (SOA) is proposed in this paper. In this method, the ratio of SNR to kurtosis is used as the objective function; firstly, SOA is used to search the optimal filter length in CYCBD by iteration, and then it uses the optimal filter length to perform CYCBD; finally, the frequency-domain waveform is determined through Fourier transformation. The method proposed is applied to the fault extraction of a simulated signal and a test vibration signal of the closed power flow gearbox test bed, and the fault frequency is successfully extracted, in addition, using maximum correlation kurtosis deconvolution (MCKD) and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) to compare with CYCBD-SOA, which validated availability of the proposed method.