scholarly journals LUNA Condition Based Monitoring Update: Reducing the number of Sensors for Excess Load and External Leak

2021 ◽  
Author(s):  
Andre Green
Keyword(s):  
Condition Based Monitoring ◽  
Excess Load

Low-Cost Vibration Sensor for Condition-Based Monitoring Manufactured From Polyurethane Foam

2017 ◽  
Vol 1 (6) ◽  
pp. 1-4 ◽  
Author(s):  
Reshma Ajith ◽  
Amit Tewari ◽  
Dipti Gupta ◽  
Siddharth Tallur
Keyword(s):  
Polyurethane Foam ◽  
Low Cost ◽  
Vibration Sensor ◽  
Condition Based Monitoring

A Deep Learning Approach for Condition-Based Monitoring and FaultDiagnosis of Rod Pump System

2017 ◽  
Vol 1 (1) ◽  
pp. 32-42
Author(s):  
Hangqi Zhao ◽  
◽  
Jian Wang ◽  
Peng Gao ◽  
◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Pump System ◽  
Condition Based Monitoring

Intelligent Fault Recognition and Diagnosis for Rotating Machines using Neural Networks

2013 ◽  
pp. 158-172 ◽  
Author(s):  
Cyprian F. Ngolah ◽  
Ed Morden ◽  
Yingxu Wang
Keyword(s):  
Machine Condition ◽  
Vibration Signals ◽  
Rotating Machine ◽  
Fault Recognition ◽  
Machine Operators ◽  
System Vibration ◽  
Machine Health ◽  
Condition Based Monitoring ◽  
Industrial Machine ◽  
Previous Training

Monitoring industrial machine health in real-time is not only in high demand, it is also complicated and difficult. Possible reasons for this include: (a) access to the machines on site is sometimes impracticable, and (b) the environment in which they operate is usually not human-friendly due to pollution, noise, hazardous wastes, etc. Despite theoretically sound findings on developing intelligent solutions for machine condition-based monitoring, few commercial tools exist in the market that can be readily used. This paper examines the development of an intelligent fault recognition and monitoring system (Melvin I), which detects and diagnoses rotating machine conditions according to changes in fault frequency indicators. The signals and data are remotely collected from designated sections of machines via data acquisition cards. They are processed by a signal processor to extract characteristic vibration signals of ten key performance indicators (KPIs). A 3-layer neural network is designed to recognize and classify faults based on a pre-determined set of KPIs. The system implemented in the laboratory and applied in the field can also incorporate new experiences into the knowledge base without overwriting previous training. Results show that Melvin I is a smart tool for both system vibration analysts and industrial machine operators.

Performance of Francis Turbine Operating at Excess Load Condition

2020 ◽  
Author(s):  
Muhannad Altimemy ◽  
Justin Caspar ◽  
Alparslan Oztekin
Keyword(s):  
Flow Field ◽  
Draft Tube ◽  
Turbine Blades ◽  
Pressure Fluctuations ◽  
Load Condition ◽  
Operating Conditions ◽  
Francis Turbine ◽  
Partial Load ◽  
Dynamics Simulations ◽  
Excess Load

Abstract Computational fluid dynamics simulations are conducted to characterize the spatial and temporal characteristics of the flow field inside a Francis turbine operating in the excess load regime. A high-fidelity Large Eddy Simulation (LES) turbulence model is applied to investigate the flow-induced pressure fluctuations in the draft tube of a Francis Turbine. Probes placed alongside the wall and in the center of the draft tube measure the pressure signal in the draft tube, the pressure over the turbine blades, and the power generated to compare against previous studies featuring design point and partial load operating conditions. The excess load is seen during Francis turbines in order to satisfy a spike in the electrical demand. By characterizing the flow field during these conditions, we can find potential problems with running the turbine at excess load and inspire future studies regarding mitigation methods. Our studies found a robust low-pressure region on the edges of turbine blades, which could cause cavitation in the runner region, which would extend through the draft tube, and high magnitude of pressure fluctuations were observed in the center of the draft tube.

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