scholarly journals LUNA Condition-Based Monitoring Update: Dimensions and Sensors for Separating Act-Act from Act-Val and Differentiating Damage Types

2021 ◽  
Author(s):  
Andre Green
Keyword(s):  
Condition Based Monitoring ◽  
Damage Types

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

Ageing Effects on Burst Pressure Test of Impacted Glass Fibre Reinforcement Epoxy (GRE) Pipes

2014 ◽  
Vol 695 ◽  
pp. 717-720 ◽  
Author(s):  
A. Hawa ◽  
Mohd Shukry Abdul Majid ◽  
Mohd Afendi ◽  
M. Haslan ◽  
Krishnan Pranesh ◽  
...  
Keyword(s):  
Energy Levels ◽  
Testing Machine ◽  
Filament Winding ◽  
Impact Testing ◽  
Burst Pressure ◽  
Fibre Reinforcement ◽  
Pressure Test ◽  
Leakage Failure ◽  
Composite Pipes ◽  
Damage Types

The main objective of this experimental study is to investigate the effects of hydrothermal ageing on the pressure bearing capacity of the E-glass/epoxy composite pipes subjected to impact loadings. The pipes were produced by the conventional filament winding technique comprises of six antisymmetric layers with (±55°)3 winding angles. The pipes were immersed in tab water for period of 500, 1000, and 1500 hours. The specimens were impacted at three different energy levels; 5 J, 7.5 J, and 10 J using an instrumented drop weight impact testing machine (IMATEK IM10). The samples were then subjected to pressure test until distinct leakage failure is observed. The results indicates that peak force and contact time increase with increasing impact energy. The tests results show that the burst pressure decreases with increase in energy levels during impact loading. During the burst tests, several damage types named leakage and eruption were observed.

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.

scholarly journals A Fault-Signal-Based Generalizing Remaining Useful Life Prognostics Method for Wheel Hub Bearings

2019 ◽  
Vol 9 (6) ◽  
pp. 1080 ◽  
Author(s):  
Shixi Tang ◽  
Jinan Gu ◽  
Keming Tang ◽  
Rong Zou ◽  
Xiaohong Sun ◽  
...  
Keyword(s):  
Fatigue Damage ◽  
Remaining Useful Life ◽  
Working Environment ◽  
Short Term ◽  
Signal Model ◽  
Useful Life ◽  
Simplified Solution ◽  
Damage Type ◽  
Damage Types

The goal of this work is to improve the generalization of remaining useful life (RUL) prognostics for wheel hub bearings. The traditional life prognostics methods assume that the data used in RUL prognostics is composed of one specific fatigue damage type, the data used in RUL prognostics is accurate, and the RUL prognostics are conducted in the short term. Due to which, a generalizing RUL prognostics method is designed based on fault signal data. Firstly, the fault signal model is designed with the signal in a complex and mutative environment. Then, the generalizing RUL prognostics method is designed based on the fault signal model. Lastly, the simplified solution of the generalizing RUL prognostics method is deduced. The experimental results show that the proposed method gained good accuracies for RUL prognostics for all the amplitude, energy, and kurtosis features with fatigue damage types. The proposed method can process inaccurate fault signals with different kinds of noise in the actual working environment, and it can be conducted in the long term. Therefore, the RUL prognostics method has a good generalization.

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