An Electro-Mechanical Actuator Motor Voltage Estimation Method with a Feature-Aided Kalman Filter

Zhang, Yujie; Liu, Liansheng; Yu, Peng; Liu, Datong

doi:10.3390/s18124190

Cited by 24 publications

(8 citation statements)

References 45 publications

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“…The AJEKF was introduced and combined with an adaptive dual-based Kalman filter to capture the degradation of DC-DC converters [18]. Y Zhang et al also used the feature-aided Kalman filter (FAKF) to evaluate the degradation of electromechanical actuators (EMA) [19]. The industries that use the KF for sensor data fusion are diverse, such as aviation, robots, drones, and autonomous vehicles, so research is active.…”

Section: Motivation and Literature Reviewmentioning

confidence: 99%

RUL Prediction of Switched Mode Power Supply Using a Kalman Filter Assisted Deep Neural Network

et al. 2021

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Switched-mode power supply (SMPS) has been of vital importance majorly in power management of industrial equipment with much-improved efficiency and reliability. Given the diverse range on loading and operating conditions of SMPS, several anomalies can occur in the device resulting to over-voltage, overloading, erratic atmospheric conditions, etc. Electrical over-stress (EOS) is one of the commonly used causes of failure among power electronic devices. Since there is a limitation for the SMPS in terms of input voltage and current (two methods of controlling an SMPS), the device has been subjected to an accelerated aging test using EOS. This study presents a two-fold approach to evaluate the overall state of health of SMPS using an integration of extended Kalman filter (EKF) and deep neural network. Firstly, the EKF algorithm would assist in fusing fault features to acquire an comprehensive degradation trend. Secondly, the degradation pattern of the SMPS has been monitored for four different electrical loadings, and a bi-directional long short-term memory (BiLSTM) deep neural network is trained for future predictions. The proposed model provides a unique approach and accuracy in SMPS fault indication with the aid of electrical parameters.

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Section: Motivation and Literature Reviewmentioning

confidence: 99%

RUL Prediction of Switched Mode Power Supply Using a Kalman Filter Assisted Deep Neural Network

et al. 2021

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“…4,5 Although the model-based method can achieve precise and stable prediction result, it is hard to formulate the physical model of complex object. 6 Hence, it is not applicable in many modern industry systems. On the contrary, data-driven model can be carried out with the available sensing data of the monitored system.…”

Section: Introductionmentioning

confidence: 99%

In-situ remaining useful life prediction of aircraft auxiliary power unit based on quantitative analysis of on-wing sensing data

Liu

Guo

Wang³

et al. 2020

Advances in Mechanical Engineering

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The in-situ prognostics and health management of aircraft auxiliary power unit faces difficulty using the sparse on-wing sensing data. As the key technology of prognostics and health management, remaining useful life prediction of in-situ aircraft auxiliary power unit is hard to achieve accurate results. To solve this problem, we propose one kind of quantitative analysis of its on-wing sensing data to implement remaining useful life prediction of auxiliary power unit. Except the most important performance parameter exhaust gas temperature, the other potential parameters are utilized based on mutual information, which can be used as the quantitative metric. In this way, the quantitative threshold of mutual information for enhancing remaining useful life prediction result can be determined. The implemented cross-validation experiments verify the effectiveness of the proposed method. The real on-wing sensing data of auxiliary power unit for experiment are from China Southern Airlines Company Limited Shenyang Maintenance Base, which spends over $6.5 million on auxiliary power unit maintenance and repair each year for the fleet of over 500 aircrafts. Although the relative improvement is not too large, it is helpful to reduce the maintenance and repair cost.

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“…Therefore, the modelbased method cannot be realized in many modern industry systems. In contrast, the data-driven method which depends on the condition monitoring data of the system is easier to be implemented [9]. With the progress of sensor technology, industry internet, internet of things, etc., more and more sensing data of the monitored system are available.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Remaining Useful Life Prediction Considering Sensor Anomaly Detection and Data Recovery

et al. 2019

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Prognostics and health management (PHM) is being adopted more and more in the modern engineering systems. As one of the most important technologies in the PHM domain, remaining useful life (RUL) prediction has attracted much attention from the researchers in the scholar and industrial field. Although many methods have been proposed to improve the prediction result, the problem of sensor anomaly detection and data recovery has not been considered together. To achieve this object, the data-driven RUL prediction framework considering sensor anomaly detection and data recovery is proposed, which is expected to improve the performance of RUL prediction caused by sensor anomaly. The selected sensor data are first detected to decide whether they are anomalous. If the data of this selected sensor are normal, they are continuously adopted as the input of the RUL prediction algorithm. But, if the data are anomalous, they will be recovered by the related algorithm. The recovered data will be utilized as the input of the RUL prediction algorithm. In the proposed framework, mutual information, Kernel principal component analysis (KPCA), least square-support vector machine (LS-SVM), and Gaussian process regression (GPR) are utilized. Both simulation data and practical data are used to evaluate the performance of the proposed method. Compared with abandoning the anomalous sensor data, the recovered data can indeed help to enhance the RUL prediction result. INDEX TERMSRemaining useful life, mutual information, sensor anomaly detection, data recovery for prognostics.

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An Electro-Mechanical Actuator Motor Voltage Estimation Method with a Feature-Aided Kalman Filter

Cited by 24 publications

References 45 publications

RUL Prediction of Switched Mode Power Supply Using a Kalman Filter Assisted Deep Neural Network

RUL Prediction of Switched Mode Power Supply Using a Kalman Filter Assisted Deep Neural Network

In-situ remaining useful life prediction of aircraft auxiliary power unit based on quantitative analysis of on-wing sensing data

Data-Driven Remaining Useful Life Prediction Considering Sensor Anomaly Detection and Data Recovery

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