Fu et al 2022a - Revision history

Rimni at 13:37, 16 May 2023

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Rimni at 13:37, 16 May 2023

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Rimni at 13:35, 16 May 2023

2023-05-16T13:35:57Z

Rimni: /* References */

2023-05-16T13:34:16Z

‎References

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Rimni at 11:52, 16 May 2023

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Rimni at 12:41, 12 May 2023

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Rimni at 11:00, 12 May 2023

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Rimni: /* 4.5 Experimental results and analysis */

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‎4.5 Experimental results and analysis

Rimni at 10:57, 12 May 2023

2023-05-12T10:57:00Z

Rimni: /* 4.5 Experimental results and analysis */

2023-05-12T10:52:40Z

‎4.5 Experimental results and analysis

← Older revision		Revision as of 13:37, 16 May 2023
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	:3) We conduct experiments on real datasets to evaluate the effectiveness of our proposed method. The experimental results show that the proposed method shows a considerable improvement in aeroengine performance estimation.		:3) We conduct experiments on real datasets to evaluate the effectiveness of our proposed method. The experimental results show that the proposed method shows a considerable improvement in aeroengine performance estimation.
−

	The rest of this paper is organized as follows. Section 2 reviews the related works. Section 3 describes the suggested method based on deep learning, and section 4 includes the computational experiments and an analysis of the results. Finally, section 5 concludes this work and gives future studies.		The rest of this paper is organized as follows. Section 2 reviews the related works. Section 3 describes the suggested method based on deep learning, and section 4 includes the computational experiments and an analysis of the results. Finally, section 5 concludes this work and gives future studies.

← Older revision		Revision as of 13:37, 16 May 2023
Line 52:		Line 52:

	:3) We conduct experiments on real datasets to evaluate the effectiveness of our proposed method. The experimental results show that the proposed method shows a considerable improvement in aeroengine performance estimation.		:3) We conduct experiments on real datasets to evaluate the effectiveness of our proposed method. The experimental results show that the proposed method shows a considerable improvement in aeroengine performance estimation.
−	:	+
−	:The rest of this paper is organized as follows. Section 2 reviews the related works. Section 3 describes the suggested method based on deep learning, and section 4 includes the computational experiments and an analysis of the results. Finally, section 5 concludes this work and gives future studies.	+
		+	The rest of this paper is organized as follows. Section 2 reviews the related works. Section 3 describes the suggested method based on deep learning, and section 4 includes the computational experiments and an analysis of the results. Finally, section 5 concludes this work and gives future studies.

	==2. Related works==		==2. Related works==

@@ Line 73: / Line 73: @@
 ===3.1 Problem description===
-From the perspective of health management, aeroengine EGTM prediction can be regarded as a time series problem. The EGTM prediction problem can be defined as follows. The input is <math display="inline">{X}_{t}^{k}</math>, where <math display="inline">k\in R^n</math>, <math display="inline">t = (1,2, \ldots ,T)</math>. In addition, <math display="inline">{X}_{t}^{k}</math>represents the collected input data during aeroengine operation, <math>n</math> represents the number of features, and <math>T</math> represents the length of the time step. The corresponding output is the EGTM prediction result <math display="inline">{Y}_{t}</math> for each time step. EGTM is predicted in real-time by establishing the mapping relationship between the output and input data. The mapping relationship is expressed as follows:
+From the perspective of health management, aeroengine EGTM prediction can be regarded as a time series problem. The EGTM prediction problem can be defined as follows. The input is <math display="inline">{X}_{t}^{k}</math>, where <math display="inline">k\in R^n</math>, <math display="inline">t = (1,2, \ldots ,T)</math>. In addition, <math display="inline">{X}_{t}^{k}</math> represents the collected input data during aeroengine operation, <math>n</math> represents the number of features, and <math>T</math> represents the length of the time step. The corresponding output is the EGTM prediction result <math display="inline">{Y}_{t}</math> for each time step. EGTM is predicted in real-time by establishing the mapping relationship between the output and input data. The mapping relationship is expressed as follows:
 {| class="formulaSCP" style="width: 100%; text-align: center;"

@@ Line 427: / Line 427: @@
 [14]  Bai S.,  Huang H.-Z.,  Li Y.-F.,  Yu A.,  Deng Z. A modified damage accumulation model for life prediction of aero-engine materials under combined high and low cycle fatigue loading. Fatigue & Fracture of Engineering Materials & Structures,  44(11):3121–3134, 2021. doi: 10.1111/ffe.13566.
-[15] J. Lin, J. Zhang, G. Zhang, G. Ni, and F. Bi, Aero-engine blade fatigue analysis based on nonlinear continuum damage model using neural networks, Chin. J. Mech. Eng.,  25,  2,  338–345, Mar. 2012, doi: 10.3901/CJME.2012.02.338.
+[15]  Lin J.,  Zhang J.,  Zhang G.,  Ni G.,   Bi F. Aero-engine blade fatigue analysis based on nonlinear continuum damage model using neural networks. Chin. J. Mech. Eng.,  25(2):338–345, 2012. doi: 10.3901/CJME.2012.02.338.
-[16] D. Chen and J. Sun, Fuel and emission reduction assessment for civil aircraft engine fleet on-wing washing, Transportation Research Part D: Transport and Environment,  65,  324–331, Dec. 2018, doi: 10.1016/j.trd.2018.05.013.
+[16]  Chen D.,   Sun J. Fuel and emission reduction assessment for civil aircraft engine fleet on-wing washing. Transportation Research Part D: Transport and Environment,  65:324–331, 2018. doi: 10.1016/j.trd.2018.05.013.
-[17] D. Giesecke, U. Igie, P. Pilidis, K. Ramsden, and P. Lambart, Performance and Techno-Economic Investigation of On-Wing Compressor Wash for a Short-Range Aero Engine, Jul. 2013,  235–244. doi: 10.1115/GT2012-68995.
+[17]  Giesecke D.,  Igie U.,  Pilidis P.,  Ramsden K., Lambart P. Performance and techno-economic investigation of on-wing compressor wash for a short-range aero engine. Conference Proceedings at the ASME Turbo Expo 2012: Turbine Technical Conference and Exposition, pp. 235–244, 2013. doi: 10.1115/GT2012-68995.
-[18] S. Madsen and L. E. Bakken, Gas Turbine Operation Offshore: On-Line Compressor Wash Operational Experience, presented at the ASME Turbo Expo 2014: Turbine Technical Conference and Exposition, Sep. 2014. doi: 10.1115/GT2014-25272.
+[18]  Madsen S.,   Bakken L.E. Gas turbine operation offshore: On-line compressor wash operational experience. Conference Proceedings at the ASME Turbo Expo 2014: Turbine Technical Conference and Exposition, pp. 11, 2014. doi: 10.1115/GT2014-25272.
-[19] L. Wang, Z. Yan, F. Long, X. Shi, and J. Tang, Parametric study of online aero-engine washing systems, in 2016 IEEE International Conference on Aircraft Utility Systems (AUS), Oct. 2016,  273–277. doi: 10.1109/AUS.2016.7748058.
+[19]  Wang L.,  Yan Z.,  Long F.,  Shi X., Tang J. Parametric study of online aero-engine washing systems. In 2016 IEEE International Conference on Aircraft Utility Systems (AUS), pp. 273–277, 2016. doi: 10.1109/AUS.2016.7748058.
-[20] U. Igie, P. Pilidis, D. Fouflias, K. Ramsden, and P. Laskaridis, Industrial Gas Turbine Performance: Compressor Fouling and On-Line Washing, Journal of Turbomachinery,  136,  10, Jun. 2014, doi: 10.1115/1.4027747.
+[20] Igie U.,  Pilidis P.,  Fouflias D.,  Ramsden K., Laskaridis P. Industrial gas turbine performance: Compressor fouling and on-line washing. Journal of Turbomachinery,  136(10), pp. 13, 2014. doi: 10.1115/1.4027747.
-[21] S. Adibhatla, T. Lewis, S. Adibhatla, and T. Lewis, Model-based intelligent digital engine control (MoBIDEC), in 33rd Joint Propulsion Conference and Exhibit, American Institute of Aeronautics and Astronautics, 1997. doi: 10.2514/6.1997-3192.
+[21]  Adibhatla S.,  Lewis T.,  Adibhatla S.,   Lewis T. Model-based intelligent digital engine control (MoBIDEC). In 33rd Joint Propulsion Conference and Exhibit, American Institute of Aeronautics and Astronautics, Seattle, WA, USA, 6-9 July 1997. doi: 10.2514/6.1997-3192.
-[22] P. S. Maybeck, Multiple model adaptive algorithms for detecting and compensating sensor and actuator/surface failures in aircraft flight control systems, International Journal of Robust and Nonlinear Control,  9,  14,  1051–1070, 1999, doi: 10.1002/(SICI)1099-1239(19991215)9:14<1051::AID-RNC452>3.0.CO;2-0.
+[22]  Maybeck P.S. Multiple model adaptive algorithms for detecting and compensating sensor and actuator/surface failures in aircraft flight control systems. International Journal of Robust and Nonlinear Control,  9(14):1051–1070, 1999. doi: 10.1002/(SICI)1099-1239(19991215)9:14<1051::AID-RNC452>3.0.CO;2-0.
-[23] F. Lu, Y. Chen, J. Huang, D. Zhang, and N. Liu, An integrated nonlinear model-based approach to gas turbine engine sensor fault diagnostics, Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering,  228,  11,  2007–2021, Sep. 2014, doi: 10.1177/0954410013511596.
+[23]  Lu F.,  Chen Y.,  Huang J.,  Zhang D.,  Liu N. An integrated nonlinear model-based approach to gas turbine engine sensor fault diagnostics. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering,  228(11):2007–2021, 2014. doi: 10.1177/0954410013511596.
-[24] Y. S. Chati and H. Balakrishnan, Data-Driven Modeling of Aircraft Engine Fuel Burn in Climb Out and Approach, Transportation Research Record,  2672,  29,  1–11, Dec. 2018, doi: 10.1177/0361198118780876.
+[24]  Chati Y.S.,   Balakrishnan H. Data-driven modeling of aircraft engine fuel burn in climb out and approach. Transportation Research Record,  2672(29):1–11, 2018. doi: 10.1177/0361198118780876.
-[25] A. Bobrinskoy, M. Gatti, O. Guerineau, F. Cazaurang, B. Bluteau, and E. Recherche, Model-based fault detection and isolation design for flight-critical actuators in a harsh environment, in 2012 IEEE/AIAA 31st Digital Avionics Systems Conference (DASC), Oct. 2012,  7D5-1-7D5-8. doi: 10.1109/DASC.2012.6382423.
+[25]  Bobrinskoy A.,  Gatti M.,  Guerineau O.,  Cazaurang F.,  Bluteau B.,  Recherche  E. Model-based fault detection and isolation design for flight-critical actuators in a harsh environment. In 2012 IEEE/AIAA 31st Digital Avionics Systems Conference (DASC), 7D5-1-7D5-8, Williamsburg, VA, USA, 2012. doi: 10.1109/DASC.2012.6382423.
 [26] Z. Cui, S. Zhong, and Z. Yan, Fuel savings model after aero-engine washing based on convolutional neural network prediction, Measurement,  151, p. 107180, Feb. 2020, doi: 10.1016/j.measurement.2019.107180.

@@ Line 383: / Line 383: @@
 |}
-==5 Conclusion and Future Work==
+==5. Conclusion and future work==
 This paper proposes an attention-based multiscale CNN-LSTM framework for aeroengine performance prediction. First, a multiscale CNN is developed to extend the feature information of the raw input data to a longer time scale. Then, LSTM is used to learn the time-series features. An attention mechanism is adopted to obtain the importance of the time series data and assign different importance weights to the features for improving the prediction accuracy. Experiments on real data sets verify the effectiveness of the designed method in aeroengine performance prediction.
@@ Line 389: / Line 389: @@
 In prognostics and health management (PHM), accurate prediction performance is significant for ensuring the reliability of aeroengines and making maintenance plans and flight schedules. The price of data collection for the whole life of an aeroengine is relatively high, as it may take a long period of several years to track the degradation process. However, there are individual differences among aeroengines due to different manufacturing, assembly, and even model types. As such, they likely work under various scenarios. Hence, data-driven methods perform worse than expected. To address this problem and meet the actual needs of airline management and maintenance, the knowledge of transfer learning can be used to enhance the generalization ability of a data-driven prognostic model [44], so the trained model can be applied to the performance prediction of other aeroengines.
-<span id='_Hlk103065805'></span><span style="text-align: center; font-size: 75%;">'''Funding:''' This research was funded by the Science and Technology Foundation of Sichuan, grant number 2022YFG0356, the Key Research and Development Plan Foundation of Tibet, grant number XZ202101ZY0017G, and the Aeroengine Operational Safety and Control Technology Research Center Project of CAFUC, grant number JG2019-02-03 and D202105.</span>
+==Funding==
 '''Conflicts of Interest:''' The authors declare no conflict of interest.

@@ Line 350: / Line 350: @@
 <div id='img-9'></div>
-{| class="wikitable" style="margin: 1em auto 0.1em auto;border-collapse: collapse;width:auto;"
+{| class="wikitable" style="margin: 1em auto 0.1em auto;border-collapse: collapse;width:60%;"
 |-style="background:white;"
 |style="text-align: center;padding:10px;"| [[Image:Review_393375948024-image11.png|354px]]
@@ Line 367: / Line 367: @@
 |+
 |-
-|style="text-align: center;padding:10px;width: 49%;"| [[Image:Review_393375948024-image12.png|264px]]
+|style="text-align: center;width: 49%;"| [[Image:Review_393375948024-image12.png|264px]]
-|style="text-align: center;padding:10px;width: 47%;"|[[Image:Review_393375948024-image13.png|264px]]
+|style="text-align: center;width: 47%;"|[[Image:Review_393375948024-image13.png|264px]]
 |-
 |style="text-align: center;font-size: 75%;padding-bottom:10px;"|(a) Proposed method

@@ Line 360: / Line 360: @@
 To analyze the predicted accuracy of our model with regard to EGTM, we compare the predicted EGTM with the actual EGTM of the aeroengine and plot the results in [[#img-10|Figure 10]]. The trajectory of the predicted EGTM is similar to the real EGTM of the aeroengine when compared with other methods. This supports effectiveness of our proposed model in learning aeroengine degradation information. When compared with other stages, the error tends to be larger as EGTM is between 70 and 75, which may be a result of how the aeroengine begins to degenerate rapidly in this state.
-{| style="width: 100%;margin: 1em auto 0.1em auto;border-collapse: collapse;"
+<div id='img-10'></div>
 |-
-|  style="text-align: center;vertical-align: top;width: 49%;"|[[Image:Review_393375948024-image12.png|264px]]
+|style="text-align: center;padding:10px;width: 49%;"| [[Image:Review_393375948024-image12.png|264px]]
-|  style="text-align: center;vertical-align: top;"|
+|style="text-align: center;padding:10px;width: 47%;"|[[Image:Review_393375948024-image13.png|264px]]
-|  colspan='2'  style="text-align: center;vertical-align: top;width: 47%;"|[[Image:Review_393375948024-image13.png|264px]]
 |-
-|  style="text-align: center;vertical-align: top;"|(a) proposed method
+|style="text-align: center;font-size: 75%;padding-bottom:10px;"|(a) Proposed method
-|  colspan='2'  style="text-align: center;vertical-align: top;"|
+|style="text-align: center;font-size: 75%;padding-bottom:10px;"|(b) CNN-LSTM
-|  style="text-align: center;vertical-align: top;"|(b) CNN-LSTM
 |-
-|  style="text-align: center;vertical-align: top;width: 49%;"|[[Image:Review_393375948024-image14.png|270px]]
+|style="text-align: center;padding:10px;width: 49%;"| [[Image:Review_393375948024-image14.png|270px]]
-|  colspan='2'  style="text-align: center;vertical-align: top;"|
+|style="text-align: center;padding:10px;width: 46%;"|[[Image:Review_393375948024-image15.png|264px]]
-|  style="text-align: center;vertical-align: top;width: 46%;"|[[Image:Review_393375948024-image15.png|264px]]
 |-
-|  style="text-align: center;vertical-align: top;"|(c) multiscale CNN-LSTM
+|style="text-align: center;font-size: 75%;padding-bottom:10px;"|(c) Multiscale CNN-LSTM
-|  colspan='2'  style="text-align: center;vertical-align: top;"|
+|style="text-align: center;font-size: 75%;padding-bottom:10px;"|(d) Traditional CNN-LSTM with attention
-|  style="text-align: center;vertical-align: top;"|(d) traditional CNN-LSTM with attention
+|}
 |}
 ==5 Conclusion and Future Work==

@@ Line 336: / Line 336: @@
 Furthermore, ablation experiments are conducted for the proposed method to evaluate the effectiveness of the proposed multiscale CNN structure and attention mechanism in improving the prediction accuracy of EGTM. Specifically, we conducted experiments on a traditional CNN-LSTM, a traditional CNN-LSTM with an attention mechanism, a multiscale CNN-LSTM, and our proposed method.
-For training, we set the epoch to 300 and the mean square error (MSE) as the loss function. The parameters used in each model are identical. As shown in Figure 8, the red line indicates that the proposed method produces the smallest degree of error throughout the epochs.
+For training, we set the epoch to 300 and the mean square error (MSE) as the loss function. The parameters used in each model are identical. As shown in [[#img-8|Figure 8]], the red line indicates that the proposed method produces the smallest degree of error throughout the epochs.
-<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">
+<div id='img-8'></div>
- [[Image:Review_393375948024-image10-c.png|408px]] </div>
+{| class="wikitable" style="margin: 1em auto 0.1em auto;border-collapse: collapse;width:auto;"
-As shown in Figure 9, the performance of a multiscale CNN-LSTM and a traditional CNN-LSTM integrated with an attention mechanism performs better than a traditional CNN-LSTM. In addition, the proposed method in this paper has the highest accuracy in EGTM prediction when compared with other methods. This verifies the effectiveness of our proposed method in enhancing the performance of extracting data features from the input data.
+As shown in [[#img-9|Figure 9]], the performance of a multiscale CNN-LSTM and a traditional CNN-LSTM integrated with an attention mechanism performs better than a traditional CNN-LSTM. In addition, the proposed method in this paper has the highest accuracy in EGTM prediction when compared with other methods. This verifies the effectiveness of our proposed method in enhancing the performance of extracting data features from the input data.
-<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">
+<div id='img-9'></div>
- [[Image:Review_393375948024-image11.png|354px]] </div>
+{| class="wikitable" style="margin: 1em auto 0.1em auto;border-collapse: collapse;width:auto;"
-To analyze the predicted accuracy of our model with regard to EGTM, we compare the predicted EGTM with the actual EGTM of the aeroengine and plot the results in Figure 10. The trajectory of the predicted EGTM is similar to the real EGTM of the aeroengine when compared with other methods. This supports effectiveness of our proposed model in learning aeroengine degradation information. When compared with other stages, the error tends to be larger as EGTM is between 70 and 75, which may be a result of how the aeroengine begins to degenerate rapidly in this state.
+To analyze the predicted accuracy of our model with regard to EGTM, we compare the predicted EGTM with the actual EGTM of the aeroengine and plot the results in [[#img-10|Figure 10]]. The trajectory of the predicted EGTM is similar to the real EGTM of the aeroengine when compared with other methods. This supports effectiveness of our proposed model in learning aeroengine degradation information. When compared with other stages, the error tends to be larger as EGTM is between 70 and 75, which may be a result of how the aeroengine begins to degenerate rapidly in this state.
 {| style="width: 100%;margin: 1em auto 0.1em auto;border-collapse: collapse;"