A data-driven degradation prognostic strategy for aero-engine under various operational conditions

Wang, Cunsong; Zhu, Zheng H.; Lu, Ningyun; Cheng, Yuehua; Jiang, Bin

doi:10.1016/j.neucom.2021.07.080

Cited by 35 publications

(16 citation statements)

References 31 publications

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“…23 Therefore, when constructing the source domain, it includes the sensor data under various working conditions, such as engine start and stop, taxiing, climb, cruise, and landing, as well as several typical fault data. 24 These data can be historical engine data or other types of engine data. The source domain incorporates data with multiple distribution characteristics.…”

Section: Data Preprocessingmentioning

confidence: 99%

“…Due to its special operating state and operating environment, the sensor data have high noise and complex distribution 23 . Therefore, when constructing the source domain, it includes the sensor data under various working conditions, such as engine start and stop, taxiing, climb, cruise, and landing, as well as several typical fault data 24 . These data can be historical engine data or other types of engine data.…”

Section: Data Preprocessingmentioning

confidence: 99%

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Multihorizons transfer strategy for continuous online prediction of time‐series data in complex systems

Zhou

Wang

2022

Int J of Intelligent Sys

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The sustainability online prediction is of great significance for higher horizon time-series prediction in the future, and it embodies higher application value in equipment fault prediction and health management. However, compared with one-step time-series prediction, continuous online prediction faces many uncertainties, including error accumulation and lack of information. To realize continuous online prediction of time-series data in complex systems, this paper proposes a continuous online prediction strategy based on multihorizons transfer (OnMultiHorTS), which is used for continuous online prediction tasks of timeseries data. The algorithm aims to use source domain data to provide more effective information for target prediction tasks. However, the time-varying characteristics of time-series data often lead to large differences in data distribution over a long time span, which is difficult to guarantee the assumption that the data are the same distribution. How to construct more effective source domain information based on historical data and existing data, and apply it to the target domain prediction tasks, is one of the focuses of our OnMultiHorTS algorithm. In addition, different from the typical iterative and multistep advance prediction methods, the proposed algorithm regards different prediction tasks as different horizons, which are

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Section: Data Preprocessingmentioning

confidence: 99%

Section: Data Preprocessingmentioning

confidence: 99%

Multihorizons transfer strategy for continuous online prediction of time‐series data in complex systems

Zhou

Wang

2022

Int J of Intelligent Sys

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“…The C-MAPSS dataset is a popular dataset simulating various scenarios of aircraft engine degradation and has been widely used to test the performance of various datadriven failure prognosis methods [28][29][30][31][32]. Figure 5 shows the schematic diagram of the simulated engine.…”

Section: Description Of the C-mapss Datasetmentioning

confidence: 99%

Deep Auto-Encoder and Deep Forest-Assisted Failure Prognosis for Dynamic Predictive Maintenance Scheduling

Yu¹,

Chen

et al. 2021

Sensors

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Prognostics and health management (PHM) with failure prognosis and maintenance decision-making as the core is an advanced technology to improve the safety, reliability, and operational economy of engineering systems. However, studies of failure prognosis and maintenance decision-making have been conducted separately over the past years. Key challenges remain open when the joint problem is considered. The aim of this paper is to develop an integrated strategy for dynamic predictive maintenance scheduling (DPMS) based on a deep auto-encoder and deep forest-assisted failure prognosis method. The proposed DPMS method involves a complete process from performing failure prognosis to making maintenance decisions. The first step is to extract representative features reflecting system degradation from raw sensor data by using a deep auto-encoder. Then, the features are fed into the deep forest to compute the failure probabilities in moving time horizons. Finally, an optimal maintenance-related decision is made through quickly evaluating the costs of different decisions with the failure probabilities. Verification was accomplished using NASA’s open datasets of aircraft engines, and the experimental results show that the proposed DPMS method outperforms several state-of-the-art methods, which can benefit precise maintenance decisions and reduce maintenance costs.

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“…Training a large number of "measured parameters-health parameters" mapping samples can calculate the engine performance degradation trend from the measured parameters. The longshort-term memory network [13][14][15] can deeply mine the characteristics of time series and alleviate the gradient disappearance and gradient explosion problems of traditional recurrent neural networks. It also showed excellent predictive ability in the NASA C-MPASS aero-engine simulation dataset [16].…”

Section: Introductionmentioning

confidence: 99%

Performance Degradation Evaluation of Low Bypass Ratio Turbofan Engine Based on Flight Data

Qin¹,

Zhao²,

Ren³

et al. 2022

Sustainability

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A low bypass ratio turbofan engine operates in a hostile environment, resulting in performance degradation. This seriously affects the security and reliability of the engine. Therefore, a performance degradation evaluation method for engines based on flight data is proposed. The method expands the equation system to solve the underdetermined problem caused by the lack of engine sensors based on multiple operating point analysis. The improved evolution algorithm is employed to solve the equation system, which relieves the problem of insufficient precision. The engine performance degradation dataset is established based on the engine performance calculation model to verify the reliability of the degradation evaluation method. The results show that the method is applicable to the dataset. Finally, the method is applied to the actual flight data to study the law of the performance degradation of the researched engine, which indicates that the engine’s fan efficiency and high-pressure compressor flow capacity have an apparent downward trend over time.

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A data-driven degradation prognostic strategy for aero-engine under various operational conditions

Cited by 35 publications

References 31 publications

Multihorizons transfer strategy for continuous online prediction of time‐series data in complex systems

Multihorizons transfer strategy for continuous online prediction of time‐series data in complex systems

Deep Auto-Encoder and Deep Forest-Assisted Failure Prognosis for Dynamic Predictive Maintenance Scheduling

Performance Degradation Evaluation of Low Bypass Ratio Turbofan Engine Based on Flight Data

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