scholarly journals A Model-Based Method for Leakage Detection of Piston Pump Under Variable Load Condition

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 99771-99781 ◽  
Author(s):  
Hongbin Tang ◽  
Wenxian Yang ◽  
Zichao Wang
Keyword(s):  
Load Condition ◽  
Piston Pump ◽  
Variable Load ◽  
Leakage Detection ◽  
Model Based

Motor Torque Stability and Controlling Research in Variable Load Condition

2014 ◽  
Vol 940 ◽  
pp. 380-385 ◽  
Author(s):  
Yan Zhi Cheng ◽  
You Liang Ma ◽  
Xi Chen
Keyword(s):  
Fuzzy Control ◽  
Control Method ◽  
Load Condition ◽  
Control Model ◽  
Control Law ◽  
Variable Load ◽  
Real Condition ◽  
Handling Characteristics ◽  
Motor Load ◽  
Special Control

The torque stability and shutdown control of electric learner-driven vehicle (ELV) in the condition of motor load suddenly changing make the ELV has the same clutch handling characteristics with the traditional vehicle, and this makes the ELV popularization possible. A special control method is put forward in this article to achieve the consistency with the mechanical properties of engine. A multiparameter control model to identify the real condition of clutch handling by driver is builded with fuzzy control law. The torque stability and shutdown control of the motor with the load raising rapidly condition are approached by the adjusting of armature voltage with PWM control law. Keywords: Electric Learner-driven Vehicle;Torque Stability;Fuzzy Control

Psychological Effects of a Self-Sufficiency Model Based on Urge System

2010 ◽  
Vol 14 (7) ◽  
pp. 877-884 ◽  
Author(s):  
Teruaki Ando ◽  
◽  
Masayoshi Kanoh
Keyword(s):  
Interaction Effect ◽  
Autonomous System ◽  
Load Condition ◽  
Psychological Effects ◽  
Simulation Experiments ◽  
Physiological Conditions ◽  
Model Based ◽  
Self Sufficiency ◽  
Physiological Load ◽  
To Receive

In recent years, robots to coexist with humans have been developed. Their ability to communicate is indispensable for their coexistence with humans, so studies on the interaction between humans and robots are important. This paper proposes a model of the selfsufficiency system of a robot, in which we apply the urge system to the autonomous system of emotion. In this model, a robot expresses its changing psychological and physiological conditions (physiological load condition) and conveys them sensitively to the user. This is expected to result in a mental interaction effect between the user and the agent. We carry out simulation experiments on this model and verify the psychological interaction between the software robot (agent) and the user. As a result of these experiments, it is recognized that the agents with the ability to properly express physiological load among those with this model implemented have a tendency to receive higher evaluations from their users.

scholarly journals A Proof-of-Concept Study for Hydraulic Model-Based Leakage Detection in Water Pipelines Using Pressure Monitoring Data

2021 ◽  
Vol 3 ◽  
Author(s):  
Ahmad Momeni ◽  
Kalyan R. Piratla
Keyword(s):  
Hydraulic Model ◽  
Leakage Detection ◽  
Pressure Data ◽  
Pressure Monitoring ◽  
Water Utility ◽  
Detection Techniques ◽  
Model Based ◽  
Smart Water ◽  
Water Pipelines ◽  
Monitoring Stations

It is estimated that about 20% of treated drinking water is lost through distribution pipeline leakages in the United States. Pipeline leakage detection is a top priority for water utilities across the globe as leaks increase operational energy consumption and could also develop into potentially catastrophic water main breaks, if left unaddressed. Leakage detection is a laborious task often limited by the financial and human resources that utilities can afford. Many conventional leak detection techniques also only offer a snapshot indication of leakage presence. Furthermore, the reliability of many leakage detection techniques on plastic pipelines that are increasingly preferred for drinking water applications is questionable. As part of a smart water utility framework, this paper proposes and validates a hydraulic model-based technique for detecting and assessing the severity of leakages in buried water pipelines through monitoring of pressure from across the water distribution system (WDS). The envisioned smart water utility framework entails the capabilities to collect water consumption data from a limited number of WDS nodes and pressure data from a limited number of pressure monitoring stations placed across the WDS. A popular benchmark WDS is initially modified by inducing leakages through addition of orifice nodes. The leakage severity is controlled using emitter coefficients of the orifice nodes. WDS pressure data for various sets of demands is subsequently gathered from locations where pressure monitoring stations are to be placed in that modified distribution network. An evolutionary optimization algorithm is subsequently used to predict the emitter coefficients so as to determine the leakage severities based on the hydraulic dependency of the monitored pressure data on various sets of nodal demands. Artificial neural networks (ANNs) are employed to mimic the popular hydraulic solver EPANET 2.2 for high computational efficiency. The goals of this study are to: (1) validate the proof of concept of the proposed modeling approach for detecting and assessing the severity of leakages and (2) evaluate the sensitivity of the prediction accuracy to number of pressure monitoring stations and number of demand nodes at which consumption data is gathered and used. This study offers new value to prioritize pipes for rehabilitation by predicting leakages through a hydraulic model-based approach.

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