Fault Diagnosis of Wind Turbine Gearbox Based on Deep Bi-Directional Long Short-Term Memory Under Time-Varying Non-Stationary Operating Conditions

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.