Remaining useful life prediction of aero-engine enabled by fusing knowledge and deep learning models

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Highlight

  • A novel framework for Prognostics and Health Management situations with numerous sensors is proposed.

  • The framework fuses knowledge and deep learning models for RUL prediction.

  • Knowledge is utilized to guide the construction of deep learning models.

  • The proposed approach is compared with state-of-the-art deep learning methods on the C-MPASS dataset.

  • Results indicate that constructed model has higher interpretability and higher prediction accuracy.

Abstract

The remaining useful life (RUL) prediction of a complex engineering system is extremely significant for ensuring system reliability. The conventional prediction of the RUL based on only extracted degradation features of sensor data is tedious for decreasing costs and providing a decision-making foundation. However, knowledge is available for improving RUL prediction accuracy. This paper proposes a novel RUL prediction approach that combines knowledge and deep learning models. The proposed approach represents the sensor relationships as flow charts to be transformed as embedding vectors for clustering. These clustering results are subsequently utilized to guide the sensor data arrangement and hybrid deep learning model construction. Compared to various deep learning models, the robustness and reliability of the proposed method are demonstrated on the NASA open dataset C-MAPSS. The results show that the proposed approach had improved prediction accuracy by 5.5% compared to the best prediction from the literature methods. Furthermore, the constructed deep learning model by utilizing knowledge can be interpretable. Most importantly, the prediction results reveal the feasibility and reliability of fusing knowledge and deep learning models. And the proposed approach is promising for widespread application to other prediction situations with data from numerous sensors.

Introduction

As the key component of aircraft with high-reliability requirements, the engine usually develops Prognostics and Health Management (PHM) to increase reliability [1]. One important task in PHM is establishing effective approaches to better estimate the remaining useful life (RUL) [2]. Deep learning achieves success in PHM applications because the non-linear degradation characteristics in the collected sensor data can be revealed. As a result, several deep learning approaches have been proposed for accurately predicting RUL [3], [4], [5].

During the long service life of an aero-engine, fatigue, corrosion, and scale accumulation will cause the performance to degrade slowly. The factors affecting the performance degradation of rotating parts are different, but they are reflected in the change of the engine's efficiency and flow capacity, which are collectively referred to as health parameters [6]. The health parameters are usually difficult to obtain directly, but the sensor data such as temperature, pressure, rotor speed, and fuel flow rate can be easily measured. Usually, sensor data is used as a direct input for different novel deep learning models to predict RUL. For instances, dual long short-term memory (Dual-LSTM) model [7], deep convolution neural networks (DCNNs) model [8], bidirectional gated recurrent neural network (BGRU) model [9], and convolutional neural network (CNN) with bi-directional long short-term memory (BDLSTM) model [10] were constructed for accurate RUL prediction. These studies focused on the optimization and innovation of various deep learning architectures. However, the quality and size of the dataset are also key factors in determining the accuracy bottleneck of the deep learning model.

To address the problem that the accuracy of a deep learning model is limited by its structure and the quality of the dataset, scholars have begun to combine the raw data from sensors and other data. For example, many scholars have proposed hybrid prediction methods that combine data-driven and physically based model approaches [11], [12], [13]. It follows that data-driven solutions can be advantageous for enhancing or replacing inaccurate differential or algebraic equations of physically based models. The above approach requires the establishment of physically based models with higher reliability. For aero-engines, the characteristics of rotating components can shift significantly while the blades corrode and wear. Thus, it is difficult to establish high-precision physical models. This problem limits their practical application for combining data-driven and physically based approaches.

When sufficient failure data is available, some examples of recent deep learning models for prognostics and RUL combine both time-domain and frequency-domain features [14], [15], [16]. The method proposed in [17] formulated RUL prediction as a bi-level optimization problem, in which the lower level was used to forecast the time-series data and the upper level was used to predict the RUL by integrating available up-to-date measurements and predicted values by the lower-level prediction. Where the method in [18] first trained a deep belief network to extract the hidden characteristics corresponding to the fault state of a system, and the distance between the degraded state and failed state was used to construct a health indicator. After, the RUL before failure was predicted. Besides, the LSTM-Fusion architecture proposed in [19] fused multi-sensor data with variable time window sizes to capture the short-range (local) and long-range (global) characteristics of the raw and compressed dataset.

In another variation of machine learning, several approaches acknowledge that selected features of the data and sensors have greater effects on the accuracy of the data-driven model [20]. For example, the combination of inputs such as the operation settings provides an unexpected output [21]. Thus, not all the sensor data indicate the health degradation effectively, and it is important to implement sensor selection to achieve accurate predictions. Characteristics from massive source data were learned to adjust the parameters of the neural networks accordingly [22]. Monotonicity and correlation of the signal data were analyzed to achieve sensor selection [23]. Some approaches eliminated sensors that had constant outputs throughout the lifetime of the engine [24,25] because those sensors could not provide any useful information to facilitate the prediction.

The above-mentioned solution ideas utilize the original raw sensor data multiple times for different purposes, but they are not based on the essence of the information. In addition to sensor data, the information represented by the measured quantities of the sensors is also important knowledge that is worth utilizing. In particular, the mechanism of a complex engineering system can also be used for an accurate machine learning model. Therefore, another approach uses knowledge to guide machine learning, i.e., enhancing the input space.

In contrast to the hybrid models mentioned above, we focused on insights based on the combination of knowledge and deep learning models. The dataset of the run-to-failure degradation trajectories not only included measured data but also contained a large amount of information, such as the relationships between the sensors. Making the data reflect the intrinsic features of the data instead of just inputting it into deep learning models is important. For this reason, this paper used knowledge to explore the ontogenetic relationships between sensors to guide the sensor data arrangement and deep learning structure. By developing the method of knowledge representation, knowledge cluster, and deep learning model construction principles, the problem of knowledge utilization is solved for the problem of RUL estimation. Furthermore, the deep learning model constructed by our approach is interpretable. The knowledge cluster results could be illustrated in the physical significance of the sensors and the principle of model construction could explain the reason for the layers of the model. Moreover, the feasibility of our approach was demonstrated by taking NASA C-MAPSS [26] as the study case. The proposed approach outperforms the state-of-art deep learning model. At the same time, different experiment deep learning models prove that knowledge has a positive effect on RUL prediction accuracy.

In the remainder of the paper, the RUL prediction method that combines sensor knowledge and deep learning is proposed in Section 2. The evaluation of this method using a case study is presented in Section 3. Furthermore, ablation studies are utilized to prove the importance of knowledge in Section 4. Then, the discussion of comparison with other literature methods is adopted in Section 5. Finally, the results of our method and future work are concluded in Section 6.

Section snippets

Methodology for fusing knowledge and deep learning

In this section, the proposed RUL prediction method based on knowledge and deep learning is presented. First, the framework of the proposed method is introduced. Then, the representation of the aero-engine sensor knowledge and knowledge transformation are described. Finally, the deep learning model construction principle is introduced.

Case study

This section is divided into four parts to evaluate our method. First, the dataset used in the case and its challenges are explained. Second, the data preprocessing and labeling process are explicated. Then, the evaluation score is presented, and finally, the validity of the application results of our proposed method is analyzed. Moreover, comparisons with results from state-of-the-art methods for the common dataset are presented to demonstrate the superiority of our method.

Ablation studies

This section utilizes two ablation studies to provide further insights into the knowledge importance. First, the impact of the knowledge cluster is evaluated by a test experiment. Second, the impact of the uncertainties of the knowledge is analyzed.

Discussion

For complex engineered systems, the application of deep learning is limited due to the quality of the input data and model uninterpretability. Our approach proposed a novel direction to break through these limitations by embedding knowledge. Compared with other knowledge embedding methods, the differences and innovations of the proposed approach are:

  • The ways of using knowledge are different. Usually, the knowledge is used at the data pre-processing stage, such as obtaining additional data

Conclusions

In this paper, a novel RUL prediction approach that fuses knowledge and deep learning models is proposed. The proposed approach utilized the knowledge of sensor relationships to guide the sensor data arrangement and hybrid deep learning model construction. This approach improves the accuracy of remaining useful life prediction in reliability engineering and solves the problem of model interpretability.

  • 1)

    Fusing knowledge and deep learning models is feasible.

The effectiveness of the proposed

CRediT authorship contribution statement

Yuanfu Li: Conceptualization, Data curation, Formal analysis, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. Yao Chen: Conceptualization, Investigation, Writing – review & editing. Zhenchao Hu: Conceptualization, Data curation, Validation. Huisheng Zhang: Conceptualization, Resources, Writing – review & editing, Supervision, Funding acquisition, Project administration.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work was supported by the National Natural Science Foundation of China [grant numbers 51876116 and 51906138], National Fundamental Research Project [grant numbers JCKY2019204B009, JCKY2020208B004, JCKY2020208B036, MKF20200020], and the National Science and Technology Major Project [grant numbers 2017-I-0002-0002].

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