3篇
您的检索式：作者名="Haohan GAO"
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| 1 | Adaptive decision-level fusion strategy for the fault diagnosis of axial piston pumps using multiple channels of vibration signals显示文摘An axial piston pump is a key component that plays the role of the 'heart' in hydraulic systems. The pump failure will lead to an unexpected breakdown of the entire hydraulic system or even economic loss and catastrophic safety consequences. Several vibration-based machine learning methods have been developed to detect and diagnose faults of axial piston pumps. However,most of these intelligent diagnosis methods use single-sensor vibration data to monitor the pump health states. Additionally, the diagnostic accuracy is unacceptable in most situations due to the complex pump structure and limited sensor information.Therefore, this study proposes a multi-sensor fusion method to improve the fault diagnosis performance of axial piston pumps.The convolutional neural network receives three channels of vibration data and makes the final diagnosis through information fusion at the decision level. The proposed decision fusion method is evaluated on the classification task of leakage levels of an actual axial piston pump. The experimental results show that the proposed method improves the classification accuracy by adjusting the probability distribution of classification according to the learned weight matrix. | CHAO Qun GAO HaoHan TAO JianFeng WANG YuanHang ZHOU Jian LIU ChengLiang | 2022 | Science China(Technological Sciences)2022，65，2： | 2 |
| 2 | Fault diagnosis of axial piston pumps with multi-sensor data and convolutional neural network显示文摘Axial piston pumps have wide applications in hydraulic systems for power transmission.Their condition monitoring and fault diagnosis are essential in ensuring the safety and reliability of the entire hydraulic system.Vibration and discharge pressure signals are two common signals used for the fault diagnosis of axial piston pumps because of their sensitivity to pump health conditions.However,most of the previous fault diagnosis methods only used vibration or pressure signal,and literatures related to multi-sensor data fusion for the pump fault diagnosis are limited.This paper presents an end-to-end multi-sensor data fusion method for the fault diagnosis of axial piston pumps.The vibration and pressure signals under different pump health conditions are fused into RGB images and then recognized by a convolutional neural network.Experiments were performed on an axial piston pump to confirm the effectiveness of the proposed method.Results show that the proposed multi-sensor data fusion method greatly improves the fault diagnosis of axial piston pumps in terms of accuracy and robustness and has better diagnostic performance than other existing diagnosis methods. | Qun CHAO Haohan GAO Jianfeng TAO Chengliang LIU Yuanhang WANG Jian ZHOU | 2022 | Frontiers of Mechanical Engineering2022，17，3： | 2 |
| 3 | Measuring spatio-temporal autocorrelation in time series data of collective human mobility显示文摘Massive spatio-temporal big data about human mobility have become increasingly available.Revealing underlying dynamic patterns from these data is essential for understanding people’s behavior and urban deployment.Spatio-temporal autocorrelation analysis is an exploratory approach to recognizing data distribution in space and time.The most widely used spatial autocorrelation measurements,such as Moran’s I and local indicators of spatial association(LISA),only apply to static data,so are powerless to spatio-temporal big data about human mobility.Thus,we proposed a new method by extending Moran’s I to measure the spatial autocorrelation of time series data.Then the method was applied to taxi ride data in Beijing,China to reveal the spatial pattern of collective human mobility.The result shows that there is strong positive spatio-temporal autocorrelation within the 5th Ring Road,weak negative spatio-temporal autocorrelation nearby the Sixth Ring Road,and almost no spatiotemporal autocorrelation between the roads.Local spatial patterns of taxi travel were also recognized.This method is useful for discovering underlying patterns from spatio-temporal big data to understand human mobility. | Yong Gao Jing Cheng Haohan Meng Yu Liu | 2019 | Geo-Spatial Information Science2019，22，3： | 0 |
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