A Prognostic and Health Management Framework for Aero-Engines Based on a Dynamic Probability Model and LSTM Network

Huang, Yufeng; Tao, Jun; Sun, Gang; Zhang, Hao; Hu, Yan

doi:10.3390/aerospace9060316

Cited by 12 publications

(5 citation statements)

References 43 publications

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“…Table 3 shows the performance of the proposed and existing Ma et al 2022 [15], Yang et al 2021 [16], Xu et al 2022 [17], Alijemely et al 2022 [18], and Huang et al 2022 [19] models. Here, the performance attained by the proposed model for Accuracy, MSE, and PT is improved by 0.64%, 0.002s, and 44s than the existing methods.…”

Section: Comparative Analysis With Existing Papersmentioning

confidence: 99%

Gas turbine aero engine fault detection using Geo-TLSVM and digital twin with multimodal data analysis

Tadepalli,

Koona

2024

Eng. Res. Express

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DT is the digital representation of physical systems to simulate real situations in a digital version. Here, the DT of the GaT-AE is created for the Fault Detection (FD) process. The GaT is a type of internal combustion engine that uses the air as the working fluid to propel the flight. However, the incorporation of big data analysis in the DT model is challenging in previous works. So, a multimodal GaT-AE fault identification system is proposed. First, the 3D print of the GaT-AE is created. Then, the data is collected from both built-in sensors and DT. Afterward, the multimodal data is pre-processed and balanced by using HT-QNN and QADASYN, respectively. Next, the features are processed by GT-PFS, and finally, the faults are detected by Geo-TLSVM. The analysis proved that the developed model outperformed the other state-of-the-art model with its effective FD rate of 97.24%.

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Section: Comparative Analysis With Existing Papersmentioning

confidence: 99%

Gas turbine aero engine fault detection using Geo-TLSVM and digital twin with multimodal data analysis

Tadepalli,

Koona

2024

Eng. Res. Express

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“…RMSE is widely used to measure how accurate a predictive model's result is. It is defined as Equation (5).…”

Section: Performance Measuresmentioning

confidence: 99%

“…The remaining life indicates how long the turbofan engine can function before it fails. The remaining life prediction is an important link in the field of engine failure prediction and health management [3][4][5][6][7][8]. Accurate prediction on the remaining life of a turbofan engine can provide a reference for equipment overhaul and maintenance, thereby avoiding cost increases caused by excessive maintenance or potential safety hazards caused by neglected maintenance [9].…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction for Turbofan Engine Using SAE-TCN Model

et al. 2023

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Turbofan engines are known as the heart of the aircraft. The turbofan’s health state determines the aircraft’s operational status. Therefore, the equipment monitoring and maintenance of the engine is an important part of ensuring the healthy and stable operation of the aircraft, and it is vital to monitor the remaining useful life (RUL) of the engine. The monitored data of turbofan engines have high dimensions and a long time span, which cause difficulties in predicting the remaining useful life of the engine. This paper proposes a residual life prediction model based on Autoencoder and a Temporal Convolutional Network (TCN). Among them, Autoencoder is used to reduce the dimension of the data and extract features from the engine monitoring data. The TCN network is trained on the obtained low-dimensional data to predict the remaining useful life. The model mentioned in this article is verified on the NASA public data set (C-MAPSS) and compared with common machine learning methods and other deep neural networks. The SAE-TCN model achieved better scores on the FD001 independent testing data set with an RMSE of 18.01 and a score of 161. The average relative error of the model relative to other common learning models is 0.9499 in RMSE and 0.2656 in Scoring Function. The experimental results show that the model proposed in this paper performs the best in the evaluation, and this conclusion has important implications for engine health.

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“…These systems can help identify potential problems before they occur, allowing for early intervention and preventative measures. Integrating cyber-physical systems, AI, and machine learning in industry 5.0 is expected to bring about significant changes in manufacturing and production processes, leading to more efficient, flexible, and adaptable factories [ 6 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

LSTM-Autoencoder for Vibration Anomaly Detection in Vertical Carousel Storage and Retrieval System (VCSRS)

Seok

Kareem

Hur

2023

Sensors

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Industry 5.0, also known as the “smart factory,” is an evolution of manufacturing technology that utilizes advanced data analytics and machine learning techniques to optimize production processes. One key aspect of Industry 5.0 is using vibration data to monitor and detect anomalies in machinery and equipment. In the case of a vertical carousel storage and retrieval system (VCSRS), vibration data can be collected and analyzed to identify potential issues with the system’s operation. A correlation coefficient model was used to detect anomalies accurately in the vertical carousel system to ascertain the optimal sensor placement position. This model utilized the Fisher information matrix (FIM) and effective independence (EFI) methods to optimize the sensor placement for maximum accuracy and reliability. An LSTM-autoencoder (long short-term memory) model was used for training and testing further to enhance the accuracy of the anomaly detection process. This machine-learning technique allowed for detecting patterns and trends in the vibration data that may not have been evident using traditional methods. The combination of the correlation coefficient model and the LSTM-autoencoder resulted in an accuracy rate of 97.70% for detecting anomalies in the vertical carousel system.

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A Prognostic and Health Management Framework for Aero-Engines Based on a Dynamic Probability Model and LSTM Network

Cited by 12 publications

References 43 publications

Gas turbine aero engine fault detection using Geo-TLSVM and digital twin with multimodal data analysis

Gas turbine aero engine fault detection using Geo-TLSVM and digital twin with multimodal data analysis

Remaining Useful Life Prediction for Turbofan Engine Using SAE-TCN Model

LSTM-Autoencoder for Vibration Anomaly Detection in Vertical Carousel Storage and Retrieval System (VCSRS)

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