Wind-powered electricity generation has grown significantly over the past decade. While there are many components that might impact their useful life, the gearbox and generator bearings are among the most fragile components in wind turbines. Therefore, the prediction of remaining useful life (RUL) of faulty or damaged wind turbine bearings will provide useful support for reliability evaluation and advanced maintenance of wind turbines. This paper proposes a data-driven method combining the interval whitenization method with a Gaussian process (GP) algorithm in order to predict the RUL of wind turbine generator bearings. Firstly, a wavelet packet transform is used to eliminate noise in the vibration signals and extract the characteristic fault signals. A comprehensive analysis of the real degradation process is used to determine the indicators of degradation. The interval whitenization method is proposed to reduce the interference of non-stationary operating conditions to improve the quality of health indicators. Finally, the GP method is utilized to construct the model which reflects the relationship between the RUL and health indicators. The method is assessed using actual vibration datasets from two wind turbines. The prediction results demonstrate that the proposed method can reduce the effect of non-stationary operating conditions. In addition, compared with the support vector regression (SVR) method and artificial neural network (ANN), the prediction accuracy of the proposed method has an improvement of more than 65.8%. The prediction results verify the effectiveness and superiority of the proposed method.
The reliability of vibration signals acquired from a planetary gear system (the indispensable part of wind turbine gearbox) is directly related to the accuracy of fault diagnosis. The complex operation environment leads to lots of interference signals which are included in the vibration signals. Furthermore, both multiple gears meshing with each other and the differences in transmission rout produce strong nonlinearity in the vibration signals, which makes it difficult to eliminate the noise. This article presents a combined adaptive filter method by taking a delayed signal as reference signal, the SelfAdaptive Noise Cancellation method is adopted to eliminate the white noise. In the meanwhile, by applying Gaussian function to transform the input signal into high-dimension feature-space signal, the kernel least mean square algorithm is used to cancel the nonlinear interference. Effectiveness of the method has been verified by simulation signals and test rig signals. By dealing with simulation signal, the signal-to-noise ratio can be improved around 30 dB (white noise) and the amplitude of nonlinear interference signal can be depressed up to 50%. Experimental results show remarkable improvements and enhance gear fault features.
A portable fault diagnosis device for electric locomotive, which is online operational, is introduced in this paper. It realizes fault analysis and diagnosis, fault information storage and fault treatment method prompting for locomotive. Both software and hardware structure of this device is elaborated in this paper, especially the realization of fault diagnosis methods, data acquisition and communication. The device can effectively improve the speed of fault judgment and process, meanwhile reduce the performance impact of different kinds of fault, therefore, meets the actual demand of locomotive operation and the development trend of automation.
With the rapid increasing of total install capacity and operating time of wind turbine, the fatigue failures and the maintenance quantity are dramatically increased. It is urgently required to analyze the wind turbine condition timely and accurately to improve the reliability and reduce the maintenance frequency. So the research on reliability and residual lifetime predictive is proposed. At first, through SCADA system, the raw data is transmitted to the state database, on which the site monitoring data is analyzed, and some key parameters and the basic characteristics of site data are extracted. And then, the fault diagnosis of certain part of wind turbine is progressed by integrating possible site data characteristics. Since the fault of certain part is possibly induced by another part, the fault causal network is constructed in order to analyze the interaction of different part of wind turbine. The Fault Tree and Parsimonious Covering Theory are utilized to establish the fault causal network. After that, the Risk Priority Number Theory is utilized to assess the risk of different analysis conclusions obtained by the causal network. Finally, the possible residual life of wind turbine is studied by using accumulation theory and life prediction methods. The reliability of wind turbine could be improved by using the presented method. The rational arrangement of maintenance schedule and economy of management costs will also be improved.
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