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Accurately predicting the remaining useful life of wind turbine gearbox bearing online is essential for ensuring the safe operation of the whole machine in the long run. In recent years, quite a few data-driven approaches have been proposed that use the sensor-collected data to deal with this problem, achieving good results. However, their effects are heavily dependent on the massive degradation data due to the nature of data-driven methods. In practice, the complete data collection is expensive and time-consuming, especially for newly built or small-scale wind farms, which brings the problem of using limited data into sharp focus. To this end, in this paper, a novel idea of first using the prior knowledge of an empirical model for data augmentation based on the raw limited samples and then using the deep neural network to learn from the augmented data is proposed. This helps the neural network to safely approach the degradation characteristics, avoiding overfitting. In addition, a new neural network, namely, pre-interaction long short-term memory (PI-LSTM), is designed, which is able to better capture the sequential features of time-series samples, especially in the periods in which the continuous features are interrupted. Finally, a fine-tuning process is conducted using the limited real data for eliminating the introduced knowledge bias. Through a case study based on real sensor data, both the idea and the PI-LSTM are proved to be effective and superior to the state-of-art.
Wind energy has become one of the largest renewable energy sources in the world. Wind turbines (WT) play a key role in the utilization of wind energy and environmental sustainability. With the rapid development of the wind power industry, an increasing number of WTs have been operating in remote regions, such as deserts and offshore and high-altitude areas. These areas are rich in wind energy, but the harsh environments can easily trigger the rapid degradation or even sudden failures of parts and components contained in WTs. Once an unexpected failure occurs, the WT will undergo a long downtime due to a series of time-consuming responses including faults detection and location, maintenance facilities transportation and components repair or replacement, resulting in serious economic losses to wind farms. Hence, it is essential for wind farm managers and engineers to accurately predict the remaining useful life (RUL) online, namely, how much longer the critical components of WTs are able to operate until a failure happens [1]. Furthermore, predictive maintenance strategies can be developed; thus, the WTs can be guaranteed to operate in a safe and reliable way. Among the critical components contained in WTs, the failure of gearboxes subjected to continual variable operational speed and loads leads to the maximum downtime, and inner bearing failures are detected as the majority of gearbox failures due to white structure flaking, scuffing and micropitting [2], which makes the RUL prediction of WT gearbox bearings a focused area of research.
Traditional RUL prediction methods [3] are mainly based on physical formulas describing the performance degradation process of gearbox bearings, such as Paris’ Law for crack growth models [4], = Forman’s Law for crack growth models [5], contact stress analysis [6] and the damage mechanics based on stiffness analysis [7] or empirical models including the Wiener process model [8, 9] and Gamma process model [10]. These methods are collectively known as the model-based methods since they construct meaningful mathematical function expressions according to the aforementioned formulas or models. Essentially, the applied “model” in model-based methods represents the introduced prior knowledge about the regularity of the performance degradation process summarized by humans. Using only small amounts of sensor-collected data, the parameters in models can be estimated; then, the RUL can be calculated. However, the knowledge contained in the models does not always accord with facts, namely, it can be biased because of incomplete factors or a lack of stochastic process descriptions.
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In recent years, the widespread application of smart sensors and the rise of data transmission and storage technology have made it possible to obtain real-time performance-related data through a method of low-cost and incremental accumulation. Many modern WTs have been equipped with the Supervisory Control and Data Acquisition (SCADA) system, which collects various environmental data and monitored WT-related time-series data through dozens of sensors installed on critical functional components. The obtained data hold a lot of valuable information about the health status of WT components, providing golden opportunities for data-driven methods to track the performance degradation process in a statistical sense. Since the data also record the stochastic fluctuations of performance and the comprehensive impacts from relevant components and external environments, the distribution of random noise can be effectively captured and learned by data-driven methods, which makes up for the biased prior knowledge of model-based methods. In addition, typified by the deep neural networks (DNNs), data-driven methods can achieve high accuracies in predicting the RUL of WT gearbox bearings, as proved by extensive research, owing to their strong ability to describe complex nonlinear relations. However, since the number of trainable parameters in DNNs is huge and they required to be updated at limited stride lengths iteratively, the satisfiable results are always based on sufficient data. Coupled with the random sensor failures or data transmission breaks, collecting the complete degradation process of time-series data samples is expensive and time-consuming in practice [11]. Therefore, how to accurately predict the RUL of WT components with limited samples using DNNs has become a challenging and practical issue [11, 12].
Generally, a dataset with limited samples means that the quantity of the life cycles of the WT gearbox bearings included in the historical data is insufficient, and this can raise two specific challenges. The first one is the low repetition times of information about the performance degradation processes. Since DNNs adopt the training rules based on the back propagation algorithm, it is hard to capture the key features with few occurrences. Instead, DNNs tend to automatically focus on the unimportant or noisy information and end up overfitting. Secondly, since the harsh natural environment and the automatic control strategies of modern WTs, such as yawing and pitch control, force the operating condition of WT components to change frequently, the sequential features of the time-series data are usually interrupted, which aggravates the lack of key features and degrades the accuracy of RUL prediction.
To overcome the critical challenges, in this paper, two main contributions are made for predicting the RUL of WT gearbox bearings with limited samples. First and foremost, the training set of the presented DNN is obtained through data augmentation based on the model-based Wiener process method instead of raw data samples. The core idea is that the augmented data contain the common rules of the degradation trend of bearings based on the prior knowledge of the independent incremental process assumption addressed by the Wiener model. In addition, the rules can be adapted through parameter estimation based on data samples; thus, the trained Wiener model is consistent with the historical bearings. The repeated sequential features of the degradation process contained in the augmented dataset help the DNN to easily identify the key information and converge with good generalization ability.
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The other improvement is the novel DNN, Pre-Interaction Long Short-Term Memory (PI-LSTM), for effectively extracting the sequential features from augmented samples, especially in the periods in which the continuous features are interrupted. The PI-LSTM is an improved version of LSTM. Since the current input and the previous hidden state of each timestep in the standard LSTM are independent of each other, the model becomes much less effective in the face of time-series data with low sequential continuity. It is also a fundamental weakness of most variants of LSTM [13]. To this end, the PI-LSTM introduces two trainable interaction matrices before the processing of memory cells, which represents the interaction mode between the originally independent two parts, thus helping to better capture sequential features. Therefore, the entire proposed approach is an integration of the prior knowledge provided by the Wiener process model and the strong ability for
The other improvement is the novel DNN, Pre-Interaction Long Short-Term Memory (PI-LSTM), for effectively extracting the sequential features from augmented samples, especially in the periods in which the continuous features are interrupted. The PI-LSTM is an improved version of LSTM. Since the current input and the previous hidden state of each timestep in the standard LSTM are independent of each other, the model becomes much less effective in the face of time-series data with low sequential continuity. It is also a fundamental weakness of most variants of LSTM [13]. To this end, the PI-LSTM introduces two trainable interaction matrices before the processing of memory cells, which represents the interaction mode between the originally independent two parts, thus helping to better capture sequential features. Therefore, the entire proposed approach is an integration of the prior knowledge provided by the Wiener process model and the strong ability for
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