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

Abstract

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.

Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review

“…Consequently, many types of failures could appear. According to [103,104] one may observe several health levels of gears by taking into account different defects on gears teeth such as cracked, chipped, missing root, surface defect and healthy gears as addressed by Figure 5b. Additionally, bearing faults such as internal race faults could affect the mechanical transmission process of the drivetrain (Figure 5c) [105].…”

“…One can provide from the literature a set of examples that have dealt with these types of failures. For instance, in the work of Cao et al [103], they studied how to detect different states of health of the sun gear of the WT gearbox (cracked, chipped, missing root, surface defect, and healthy gears). They mainly used multiple time domain features extruded from three different accelerometers installed in different positions of the bearings (vertical, horizontal, and radial).…”

Sensor-driven Learning of Time-Dependent Parameters for Prescriptive Analytics

“…Recently, deep learning has gathered research attention since it achieves to extract and learn features directly from raw time-series or frequency data to avoid feature selection [30], [31]. However, its training time is usually a barrier to its widespread application in this context [24]. Reference [32] proposed a Time-variant Local Autocorrelated Polynomial model with Kalman to model the underlying dynamics of a time series (or signal) and mine the deep pattern of it, except estimating the instantaneous mean function, including: (1) identifying and predicting the peak and valley values of a time series; (2) reporting and forecasting the current changing pattern (increasing or decreasing pattern of the trend, and how fast it changes).…”

Intelligent Fault Diagnosis Method of Wind Turbines Planetary Gearboxes Based on a Multi-Scale Dense Fusion Network

“…Due to the harsh working environment and complex structure, the key gear components in the wind turbine planetary gearboxes are prone to damage, which adversely affect the entire transmission system. Since wind turbines are often installed in places with inconvenient transportation (Lu et al, 2020;Sun et al, 2021), any gear fault of planetary gearboxes may cause the long downtime of the corresponding wind turbine and the high cost of the related operation, maintenance, and reparation (Cao et al, 2019;Sun et al, 2019). During the service life of a wind turbine, the cost of the related maintenance and operation account for about 75% of the total investment (Lin et al, 2018;Zhu et al, 2021).…”

“…These methods can effectively avoid using complex signal processing methods to calculate feature parameters for the expression of fault information. According to vibration signals or statistical feature sets, frequency spectrum, and time-frequency spectrum of vibration signals, DBN (Wang et al, 2018;Kang et al, 2020), LSTM (Cao et al, 2019), SAE (Jiang et al, 2017), RNN (Miao et al, 2020), and CNN (Jiang et al, 2018;Wang et al, 2020a) can obtain relatively high fault diagnosis accuracy.…”