Application of EMD-WVD and particle filter for gearbox fault feature extraction and remaining useful life prediction

Structural Health Monitoring

Feng

et al. 2020

Gearboxes are critical transmission components in the drivetrain of wind turbine, which have a dominant failure rate and the highest downtime loss in all wind turbine subsystems. However, load variations of wind turbine gearbox are far from smooth and usually nondeterministic, which result in inconsistent data distributions. To solve the problem, a novel performance degradation assessment and prognosis method based on maximum mean discrepancy is proposed to test the difference between data distributions and extract the characteristics of multi-source working conditions data. Besides, the increase in sensors will bring more difficulties to establish prediction models in real-world scenarios due to different installation locations. In view of this, a transfer learning strategy called joint distribution adaptation is utilized to adapt data distribution between multi-sensor signals. Nevertheless, the presence of background noise of wind turbine signals restricts the applicability of these algorithms in practice. To further reduce the distribution difference, a novel criterion is proposed to evaluate and measure the data distribution difference between known and tested working conditions based on the witness function of maximum mean discrepancy. The application and superiority of proposed methodology are validated using a wind turbine gearbox life-cycle test data set. Meanwhile, model comparison and cross-verification are conducted between conventional and proposed prediction models. The results indicate that the proposed method has a better performance in performance degradation assessment for wind turbine gearbox.

Section: Given Labeled Source Domainmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance degradation assessment of wind turbine gearbox based on maximum mean discrepancy and multi-sensor transfer learning

Pan

Structural Health Monitoring

Feng

et al. 2020

“…WD was applied to the field of signal analysis and processing by Ville in 1947, and then developed into a representative bilinear time-frequency analysis method, known as Wigner-Ville distribution (WVD). It had a good time-frequency resolution in [ 9 ]. In fact, results of WVD will be affected by false information caused by cross interference terms.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Short-Term Atrial Fibrillation Segments Based on Frequency Slice Wavelet Transform and Machine Learning Techniques

Yue

Liu

et al. 2021

Sensors

Atrial fibrillation (AF) is the most frequently encountered cardiac arrhythmia and is often associated with other cardiovascular and cerebrovascular diseases, such as ischemic heart disease, chronic heart failure, and stroke. Automatic detection of AF by analyzing electrocardiogram (ECG) signals has an important application value. Using the contaminated and actual ECG signals, it is not enough to only analyze the atrial activity of disappeared P wave and appeared F wave in the TQ segment. Moreover, the best analysis method is to combine nonlinear features analyzing ventricular activity based on the detection of R peak. In this paper, to utilize the information of the P-QRS-T waveform generated by atrial and ventricular activity, frequency slice wavelet transform (FSWT) is adopted to conduct time-frequency analysis on short-term ECG segments from the MIT-BIH Atrial Fibrillation Database. The two-dimensional time-frequency matrices are obtained. Furthermore, an average sliding window is used to convert the two-dimensional time-frequency matrices to the one-dimensional feature vectors, which are classified using five machine learning (ML) techniques. The experimental results show that the classification performance of the Gaussian-kernel support vector machine (GKSVM) based on the Bayesian optimizer is better. The accuracy of the training set and validation set are 100% and 93.4%. The accuracy, sensitivity, and specificity of the test set without training are 98.15%, 96.43%, and 100%, respectively. Compared with previous research results, our proposed FSWT-GKSVM model shows stability and robustness, and it could achieve the purpose of automatic detection of AF.

“…An alternative has recently been proposed by many scholars which is based on the random filtering theory. Additionally, a random degradation model is usually been established to predict the RUL and guarantee the reliability of reducers [15][16][17]. Teng et al [18] extracted the vibration signal features of full-cycle data on a gearbox and predicted the RUL using the Hidden Markov Model.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Trend Index‐Driven Remaining Useful Life Prediction Method for Renewable Energy Vehicle Reducers

Weng

et al. 2021

Shock and Vibration

Renewable energy vehicle reducers are now being developed towards achieving high-speeds, high-torque, and high-integration and intelligent trends. Its performance also determines the operation state and reliability of vehicles. Therefore, it is necessary to conduct the online condition assessment and remaining useful life predictions for renewable energy vehicle reducers. In those methods, the trend index construction is one of the most crucial steps. Hence, an adaptive trend index-driven remaining useful life prediction method is proposed to conduct condition assessment and prediction of renewable energy vehicle reducers. Firstly, an adaptive trend index is constructed, where the difference of the Fourier amplitude spectrum between the initial state and the current state is calculated to present the health trend index. Secondly, the reducer’s performance degradation model is built. In order to conduct remaining useful life prediction, the particle filtering is used to update the parameters of the reducer’s performance degradation model with the constructed adaptive trend index. In order to verify the effectiveness of the proposed method, an accelerated life test is conducted on a three-motor test bed to achieve the life-cycle data of reducers. The proposed method is verified with the obtained data and compared with the commonly used ARIMA model. The test results show that the proposed method achieves better results than the traditional methods. It means that the proposed method is a potential one for the real-time monitoring of the health state of renewable energy vehicle reducers.