scholarly journals Observer Based Multi-Level Fault Reconstruction for Interconnected Systems

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1102
Author(s):  
Mei Zhang ◽  
Boutaïeb Dahhou ◽  
Qinmu Wu ◽  
Zetao Li
Keyword(s):  
Sliding Mode ◽  
Field Extension ◽  
Interconnected Systems ◽  
Virtual Sensors ◽  
Fault Reconstruction ◽  
Reduced Order Observer ◽  
Multi Level ◽  
Algebraic Concept ◽  
High Level ◽  
Input Reconstruction

The problem of local fault (unknown input) reconstruction for interconnected systems is addressed in this paper. This contribution consists of a geometric method which solves the fault reconstruction (FR) problem via observer based and a differential algebraic concept. The fault diagnosis (FD) problem is tackled using the concept of the differential transcendence degree of a differential field extension and the algebraic observability. The goal is to examine whether the fault occurring in the low-level subsystem can be reconstructed correctly by the output at the high-level subsystem under given initial states. By introducing the fault as an additional state of the low subsystem, an observer based approached is proposed to estimate this new state. Particularly, the output of the lower subsystem is assumed unknown, and is considered as auxiliary outputs. Then, the auxiliary outputs are estimated by a sliding mode observer which is generated by using global outputs and inverse techniques. After this, the estimated auxiliary outputs are employed as virtual sensors of the system to generate a reduced-order observer, which is caplable of estimating the fault variable asymptotically. Thus, the purpose of multi-level fault reconstruction is achieved. Numerical simulations on an intensified heat exchanger are presented to illustrate the effectiveness of the proposed approach.

scholarly journals Natural Language Understanding for Multi-Level Distributed Intelligent Virtual Sensors

IoT ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 494-505
Author(s):  
Radu-Casian Mihailescu ◽  
Georgios Kyriakou ◽  
Angelos Papangelis
Keyword(s):  
Deep Learning ◽  
User Interface ◽  
Natural Language ◽  
Learning Approach ◽  
Language Understanding ◽  
Virtual Sensor ◽  
Virtual Sensors ◽  
Multi Level ◽  
High Level ◽  
User Friendly

In this paper we address the problem of automatic sensor composition for servicing human-interpretable high-level tasks. To this end, we introduce multi-level distributed intelligent virtual sensors (multi-level DIVS) as an overlay framework for a given mesh of physical and/or virtual sensors already deployed in the environment. The goal for multi-level DIVS is two-fold: (i) to provide a convenient way for the user to specify high-level sensing tasks; (ii) to construct the computational graph that provides the correct output given a specific sensing task. For (i) we resort to a conversational user interface, which is an intuitive and user-friendly manner in which the user can express the sensing problem, i.e., natural language queries, while for (ii) we propose a deep learning approach that establishes the correspondence between the natural language queries and their virtual sensor representation. Finally, we evaluate and demonstrate the feasibility of our approach in the context of a smart city setup.

scholarly journals Actuator fault-based integrated control for vehicle chassis system

2020 ◽  
pp. 002029402097757
Author(s):  
Jinwei Sun ◽  
Jingyu Cong ◽  
Weihua Zhao ◽  
Yonghui Zhang
Keyword(s):  
Performance Enhancement ◽  
Fault Tolerant ◽  
Sliding Mode ◽  
Integrated Control ◽  
Stability Control ◽  
Actuator Fault ◽  
Roll Moment ◽  
Fault Reconstruction ◽  
Driving Conditions ◽  
Vehicle Chassis

An integrated fault tolerant controller is proposed for vehicle chassis system. Based on the coupled characteristics of vertical and lateral system, the fault tolerant controller mainly concentrates on the cooperative control of controllable suspension and lateral system with external disturbances and actuator faults. A nine-DOF coupled model is developed for fault reconstruction and accurate control. Firstly, a fault reconstruction mechanism based on sliding mode is introduced; when the sliding mode achieves, actuator fault signals can be observed exactly through selecting appropriate gain matrix and equivalent output injection term. Secondly, an active suspension controller, a roll moment controller and a stability controller is developed respectively; the integrated control strategy is applied to the system under different driving conditions: when the car is traveling straightly, the main purpose of the integrated strategy is to improve the vertical performance; the lateral controller including roll moment control and stability control will be triggered when there is a steering angle input. Simulations experiments verify the performance enhancement and stability of the proposed controller under three different driving conditions.

Sliding mode based learning control for interconnected systems

2013 ◽  
Author(s):  
Fei Siang Tay ◽  
Zhihong Man ◽  
Jiong Jin ◽  
Sui Yang Khoo ◽  
Jinchuan Zheng ◽  
...  
Keyword(s):  
Sliding Mode ◽  
Learning Control ◽  
Interconnected Systems

scholarly journals An Efficient Module for Instance Segmentation Based on Multi-Level Features and Attention Mechanisms

2021 ◽  
Vol 11 (3) ◽  
pp. 968
Author(s):  
Yingchun Sun ◽  
Wang Gao ◽  
Shuguo Pan ◽  
Tao Zhao ◽  
Yahui Peng
Keyword(s):  
Feature Extraction ◽  
Spatial Structure ◽  
Semantic Feature ◽  
Semantic Features ◽  
Segmentation Method ◽  
Spatial Dimensions ◽  
Feature Pyramid ◽  
Multi Level ◽  
High Level ◽  
Instance Segmentation

Recently, multi-level feature networks have been extensively used in instance segmentation. However, because not all features are beneficial to instance segmentation tasks, the performance of networks cannot be adequately improved by synthesizing multi-level convolutional features indiscriminately. In order to solve the problem, an attention-based feature pyramid module (AFPM) is proposed, which integrates the attention mechanism on the basis of a multi-level feature pyramid network to efficiently and pertinently extract the high-level semantic features and low-level spatial structure features; for instance, segmentation. Firstly, we adopt a convolutional block attention module (CBAM) into feature extraction, and sequentially generate attention maps which focus on instance-related features along the channel and spatial dimensions. Secondly, we build inter-dimensional dependencies through a convolutional triplet attention module (CTAM) in lateral attention connections, which is used to propagate a helpful semantic feature map and filter redundant informative features irrelevant to instance objects. Finally, we construct branches for feature enhancement to strengthen detailed information to boost the entire feature hierarchy of the network. The experimental results on the Cityscapes dataset manifest that the proposed module outperforms other excellent methods under different evaluation metrics and effectively upgrades the performance of the instance segmentation method.

Sign in / Sign up

Export Citation Format

Share Document