Remaining Useful Life Estimation of Aircraft Engines Using Differentiable Architecture Search

Mao, Pengli; Lin, Yan; Xue, Sichuang; Zhang, Baochang

doi:10.3390/math10030352

Cited by 5 publications

(4 citation statements)

References 33 publications

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Section: Prediction Of Time Remaining Until Start Of the Descent Phasementioning

confidence: 99%

“…While the concept of predicting the time until an event has been reported in [28] [29] [30] [31] [32] [33], predicting the time remaining until a fall appears to be unique. Most previous applications are for a much longer time horizon, predicting the remaining usable life of machinery or in survival analysis, the time until death of a living organism [30] [29] [28]. In those prior works, the objective is to estimate some type of remaining time, while in our case the time estimate is used for confirming a decision or making more cautious decisions in order to reduce the false positive rate.…”

Section: Prediction Of Time Remaining Until Start Of the Descent Phasementioning

confidence: 99%

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Time-Critical Fall Prediction Based on Lipschitz Data Analysis and Design of a Reconfigurable Walker for Preventing Fall Injuries

Kamienski,

Bonato,

Asada

2024

IEEE Access

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Falls may cause serious injuries to older adults, leading to a deteriorated quality of life. Currently, fall injury prevention devices are lacking, especially for the balance impaired population who rely on mobility aids. Here, the functionality of a walker is augmented, so that it can predict a fall in real-time and prevent fall injuries with a rapidly reconfigurable mechanism. A key challenge is real-time fall prediction, which is a time-critical decision making process. A fall must be predicted preemptively so that the system has sufficient time to deploy the injury prevention mechanism. Data are collected from human subjects undergoing diverse loss-of-balance situations while using a walker. A predictor based on multiple Long-Short Term Memory (LSTM) networks is constructed based on two novel techniques. One is to construct a "Timer LSTM" that estimates the time remaining before the fall prevention mechanism must be activated, so that if time allows, additional data are collected and the possibility of a fall is further examined. This lowered the fall prediction false positive rate. Second, confounding cases are further analyzed using a metric of data deficiency, called the Lipschitz quotient. Additional data features that lower the Lipschitz quotients and, thereby, increase data predictability, are sought and incorporated into the original input signals. Augmenting the data further improved performance, and the best model had a 97% identified falls rate at a 0.17% false positive rate. The prediction method is implemented on a novel walker-type fall prediction and prevention prototype. The walker has a small footprint for improved maneuverability, and becomes untippable when its expandable legs are deployed in the event of a predicted fall. Thus, the older adult tethered to the untippable walker is protected from a fall. This promises immense benefits for future research on improving older adult wellbeing through real-time fall protection.

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Section: Prediction Of Time Remaining Until Start Of the Descent Phasementioning

confidence: 99%

Section: Prediction Of Time Remaining Until Start Of the Descent Phasementioning

confidence: 99%

Time-Critical Fall Prediction Based on Lipschitz Data Analysis and Design of a Reconfigurable Walker for Preventing Fall Injuries

Kamienski,

Bonato,

Asada

2024

IEEE Access

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“…The capability of detecting anomalies in the aero-engine in time is essential for the health monitoring of the engine gas-path system. If anomalies in the aero-engine can be detected early, the maintenance team will have enough time to make a sound maintenance plan [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Health Monitoring of Aero-Engine Gas-Path System Based on SFA-GMM-BID

Yang

Zhang

et al. 2023

Electronics

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This paper proposes a dynamic health monitoring method for aero-engines by extracting more hidden information from the raw values of gas-path parameters based on slow feature analysis (SFA) and the Gaussian mixture model (GMM) to improve the capability of detecting gas-path faults of aero-engines. First, an SFA algorithm is used to process the raw values of gas-path parameters, extracting the effective features reflecting the slow variation of the gas-path state. Then, a GMM is established based on the slow features of the target aero-engine in a normal state to measure its health status. Moreover, an indicator based on the Bayesian inference distance (BID) is constructed to quantitatively characterize the performance degradation degree of the target aero-engine. Considering that the fixed threshold does not suit the time-varying characteristics of the gas-path state, a dynamic threshold based on the maximum information coefficient is designed for aero-engine health monitoring. The proposed method is verified using a set of actual operation data of a certain aero-engine. The results show that the proposed method can better reflect the degradation process of the aero-engine and identify aero-engine anomalies earlier than other aero-engine fault detection methods. In addition, the dynamic threshold can reduce the occurrence of false alarms. All these advantages give the proposed method high value in real-world applications.

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“…Aircraft engine monitoring data has the features of large amount of data and high dimensionality, and deep learning has better feature extraction ability in processing such data ( Mao et al, 2022 ; Zhao & Wang, 2021 ), so the prediction method of deep learning is more suitable for aircraft engine RUL prediction. At present, some scholars have also conducted relevant research.…”

Section: Introductionmentioning

confidence: 99%

An enhanced CNN-LSTM remaining useful life prediction model for aircraft engine with attention mechanism

Wang

2022

PeerJ Computer Science

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Remaining useful life (RUL) prediction is one of the key technologies of aircraft prognosis and health management (PHM) which could provide better maintenance decisions. In order to improve the accuracy of aircraft engine RUL prediction under real flight conditions and better meet the needs of PHM system, we put forward an improved CNN-LSTM model based on the convolutional block attention module (CBAM). First, the features of aircraft engine operation data are extracted by multi-layer CNN network, and then the attention mechanism is processed by CBAM in channel and spatial dimensions to find key variables related to RUL. Finally, the hidden relationship between features and service time is learned by LSTM and the predicted RUL is output. Experiments were conducted using C-MPASS dataset. Experimental results indicate that our prediction model has feasibility. Compared with other state-of-the-art methods, the RMSE of our method decreased by 17.4%, and the score of the prediction model was improved by 25.9%.

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Remaining Useful Life Estimation of Aircraft Engines Using Differentiable Architecture Search

Cited by 5 publications

References 33 publications

Time-Critical Fall Prediction Based on Lipschitz Data Analysis and Design of a Reconfigurable Walker for Preventing Fall Injuries

Time-Critical Fall Prediction Based on Lipschitz Data Analysis and Design of a Reconfigurable Walker for Preventing Fall Injuries

Dynamic Health Monitoring of Aero-Engine Gas-Path System Based on SFA-GMM-BID

An enhanced CNN-LSTM remaining useful life prediction model for aircraft engine with attention mechanism

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