The turbofan engine is a crucial component of the aircraft. In order to provide an appropriate maintenance for improve reliability system, it necessary estimate remaining useful life (RUL) engine. this paper, data-based RUL prediction method proposed using Light Gradient Boosting Machine (LightGBM). To capture more degradation information, time window row data and runtime are used as inputs after normalization. LightGBM works very well with these high-dimensional model easy interpret....

Cooperative control for virtual coupling systems of multiple heavy-haul trains can improve the safety and efficiency railway transportation. However, false data injection attack system is a serious obstacle, which will lead to imprecise train operation control. To address this issue, deep learning-based (FDIA) detection proposed. First, cyber-physical model established. Second, cooperative law designed system, effects FDIA on analyzed. Then, unsupervised autoencoder method introduced achieve...

Cooperative control of multiple heavy-haul trains can improve the safety and efficiency railway transportation. However, influence internal external unknown disturbances for is a serious obstacle, which will lead to imprecise train operation control. To address this issue, cooperative active disturbance rejection proposed. First, multi-mass point longitudinal dynamic model established meet actual operation. Second, controller designed estimate compensate caused by interaction between...

It is crucial to predict the remaining useful life (RUL) of aircraft engines accurately and timely for operation safety appropriate maintenance decision. The key issue how efficiently mine internal relation hiding in historical time series monitoring data with high dimension features. In this paper, a data-driven prediction method proposed by combining window (TW) extreme learning machine (ELM). First, based on specific properties data, sliding introduced sample obtain input vector. Then,...

The remaining useful life estimation has been widely studied for engineering systems. A system commonly works under varying operating conditions, which may affect the degradation trajectory differently and consequently reduce accuracy of estimation. In this paper, we propose CNN-XGB with extended time window to tackle issue. Firstly, is created by feature extension processing in data preprocessing. extension, multiple features are extracted an improved differential method, these appended raw...

The cruise control of high-speed trains is challenging due to the presence time-varying air resistance coefficients and constrains. Because for are not accurately known will change with actual operating environment, precision high speed train model lower. In order ensure safe effective operation train, conditions must meet safety constraints. most traditional methods PID control, predictive so on, in which identified offline. However, typically suffer from performance degradations...

Data-driven methods are widely applied to predict the remaining useful life (RUL) of lithium-ion batteries, but they generally suffer from two limitations: (i) potentials features not fully exploited, and (ii) parameters prediction model difficult determine. To address this challenge, paper proposes a new data-driven method using feature enhancement adaptive optimization. First, battery aging extracted online. Then, technologies, including box-cox transformation time window processing, used...

Self-flooding behaviors are observed at the reactant gas bubble/transparent Pt/ITO electrode interface as a mimic catalyst layer of fuel cell, revealed by water vapor condensation induced ion-generation reactions.

Fatigue-related traffic accidents have a higher mortality rate and cause more significant damage to the environment. To ensure driving safety, real-time driver fatigue detection method based on convolutional neural network (CNN) is proposed in this paper. The cascaded by two CNN-based stages, including detecting phase classifying phase. Location Detection Network designed extract facial features localize driver's eyes mouth regions. Then State Recognition training recognize status....

Remaining useful life (RUL) estimation is expected to provide appropriate maintenance for components or systems in industry improve the reliability of systems. Most data-based methods are limited a single model, which susceptible various factors like environmental variability and diversity operating conditions. In this paper, we propose an optimal stacking ensemble method combining different learning algorithms as meta-learners mitigate impact multi-operating The selection follows...

The health stage division problem has been attracting interest for its potential role in Prognostics and Health Management (PHM). In traditional division, multiple indicators (HIs) are extracted from original data, then one suitable HI is constructed. According to the trend of HI, whole degradation process divided into different stages based on change points. points transition between stages. But difficult construct just special applications. Therefore how consider HIs simultaneously a big...

Supercapacitor energy storage systems (ESS) play a significant role in light rail vehicles LRV with no need for overhead lines and the pantograph. As ESS of demand short charging time high-power supply, it is crucial to achieve rapid balancing series-connected supercapacitor modules ESS. To address this issue, paper proposes distributed control scheme state-of-energy (SoE) modules. In order provide sufficient current, push-pull converter introduced transfer among low working voltage. Then,...

Lithium-ion battery remaining useful life (RUL)is a key parameter on management system. Many machine learning methods are applied to RUL predictions, but they generally suffer from two limitations: (i)the extracted features fail reflect the information hidden in historical degradation status, and (ii)the accuracy cannot be guaranteed evaluation of due non-linearity. In this paper, new prediction method is proposed combining time window (TW)and Gradient Boosting Decision Trees (GBDT). First,...

The high-speed train operation process is highly nonlinear and has multiple constraints objectives, which lead to a requirement for the automatic (ATO) system. In this paper, hybrid model predictive control (MPC) framework proposed controller design of ATO Firstly, piecewise linear system with state input constructed through linearization train’s dynamics. Secondly, transformed into mixed logical dynamical (MLD) by introducing auxiliary binary variables. For MLD system, MPC designed realize...

An accurate state-of-charge (SOC) estimation for a lithium-ion battery is highly dependent on the knowledge of aging, which usually costly or not available through online measurements. In this paper, novel aging-aware features can simultaneously characterize aging and SOC are extracted from discharging process. Then, extreme gradient boosting (XGBoost) algorithm combined stage division applied to acquire nonlinear relationship model between proposed offline training. The method does require...

In transportation fields, safety, efficiency, and reliability of engines are primary concerns. Remaining useful life (RUL) estimation technology is used to assess the current health status make effective maintenance plans for engines. How estimate RUL accurately increasing safety systems a challenge issue. However, existing methods focus on single size sequence. To address it, this paper proposes remaining prediction model based hybrid long-short sequences For long sequence, short-term...

A high-speed solenoid valve is a key component of the braking system. Accurately predicting failure type an important guarantee for safe operation However, electrical, magnetic, and mechanical coupling aging mechanism; individual differences; uncertainty processes have remained major challenges. To address this problem, method combining physical indices data features proposed to predict valve. Firstly, mechanism model established five are extracted from driven current curve. Then, frequency...

Imbalanced data is ubiquitous and brings much difficulty for classification. In this paper, we propose an ensemble strategy to address binary classification imbalanced problem by treatment of difficult samples or hard classifying samples. The within a two layers framework, which combines the advantages resampling method classifier. first layer, novel proposed increase weight in dataset. Then, efficient utilized second applies extreme gradient boosting classifier combined with train model....