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Fault diagnosis of wind turbine (WT) gearboxes can reduce unexpected downtime and maintenance costs. In this paper, a new fault diagnosis framework is proposed based on deep bi-directional Long Short-term Memory (DB-LSTM). Even though deep learning has been used in fault diagnosis of rotating machines, deep learning diagnosis models with the input of raw time-series or frequency data face computational challenges. Additionally, the deviation between datasets can be triggered easily by operating condition variation, which will highly reduce the performance of fault diagnosis models. However, in most studies, several constant operating conditions (e.g., selected some rotational speeds and loads) are used in the experiments, which may not reflect time-varying non-stationary operating conditions of WT gearbox and cannot be applicable in real-life applications. In this work, the experiments are designed that the real rotor speed of WT spindle input to the WT drivetrain test rig to simulate the actual time-varying non-stationary operating conditions. Ten common time-domain features are all fed into the DB-LSTM network to construct fault diagnosis model, which eliminates the need for selecting suitable features manually and improves training time. Vibration data collected by three accelerometers are used to validate the effectiveness and feasibility of the proposed method. The proposed method is also compared with four existing diagnosis models, and the results are discussed.INDEX TERMS Fault diagnosis, wind turbine gearbox, bi-directional long short-term memory, time varying non-stationary operating condition.

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.

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