Embedded Implementation of Rainflow-Counting for On-Line Predictive Maintenance

2020 ◽  
Author(s):  
Carlo Concari ◽  
Giada Bettini
Keyword(s):  
Predictive Maintenance ◽  
On Line ◽  
Rainflow Counting ◽  
Embedded Implementation

Scheduling of Condition-Based Predictive Maintenance In ANSDK Using Computer-Based On-Line Monitoring Network

2000 ◽  
Vol 97 (5) ◽  
pp. 63-79
Author(s):  
A. El Khatib ◽  
G. A. Nassef ◽  
M. N. Fors ◽  
C. C. Chen ◽  
G. A. Joshi
Keyword(s):  
Monitoring Network ◽  
Predictive Maintenance ◽  
On Line ◽  
Computer Based

On-line Surveillance for Predictive Maintenance in a Hazardous Environment

1989 ◽  
pp. 325-334
Author(s):  
Peter W. Hills
Keyword(s):  
Predictive Maintenance ◽  
On Line

Using on line insulation testing to implement generator predictive maintenance

1997 ◽  
Vol 13 (2) ◽  
pp. 17-21 ◽  
Author(s):  
V. Warren
Keyword(s):  
Predictive Maintenance ◽  
On Line

LabVIEW program design for on-line data acquisition and predictive maintenance

2002 ◽  
Author(s):  
S. Srinivasan ◽  
M. Bodruzzaman ◽  
A. Shirkhodaie ◽  
M. Malkani
Keyword(s):  
Data Acquisition ◽  
Program Design ◽  
Predictive Maintenance ◽  
On Line ◽  
Labview Program

Research on Multi Diagnosis Methods for Hydro-Generator Sets

Volume 3 ◽  
2004 ◽  
Author(s):  
Xiyang Chen ◽  
Kewei Zhang ◽  
Yucheng Peng
Keyword(s):  
Fault Diagnosis ◽  
Power System ◽  
Bayesian Network ◽  
Diagnostic System ◽  
Predictive Maintenance ◽  
System Operation ◽  
Diagnosis System ◽  
On Line ◽  
Fault Diagnostic ◽  
Generator Sets

Hydro-Generator Sets Condition Monitoring and Predictive Maintenance activity has increased dramatically over the past few years. The Fault Diagnostic System is the key technique for the Predictive Maintenance. This paper discusses the Fault Diagnostic System function structure, system design and inferential strategy of Multi-Fault Diagnostic System of the Hydro-Generator Sets. The developing of the power system to big unit requires the higher automation and reliability of the power station. Electrical power systems are constantly exposed to faults and disturbances. This may lead to damage or it may pose a threat to reliable power system operation if a faulty cannot be quickly isolated from the system operation. In accordance with diversity and complexity of Hydro-Generator Sets faults, this paper brings forward a type of fault diagnosis method based on Multi-Diagnosis methods. The Multi-Diagnosis system is constituted of two Sub-Diagnosis systems: one is On-Line Sub-Diagnosis system that based on Bayesian Network (BN) just for the modeling with Bayesian Network has been a powerful tool to solve many uncertainty problems and also with the ability of predicting the future diagnosis; the other is Off-Line Sub-Diagnosis System that based on Model of a hydraulic Turbine-Generator Rotor-Bearing. In order to apply the Bayesian Network model to the engineering fields, we have to solve the problem of constructing the Bayesian Network. Then it suggests a method of constructing Bayesian Network based on the Fault Trees that widely used by the engineers. Base on the construction method, we will construct the Bayesian Network quickly, and Bayesian Network is more suitable for Hydro-Generator Sets fault diagnosis. In accordance with the On-Line Diagnosis Sub-System, it adopts Case-Based Reasoning to make the decision of final diagnosis result or further diagnosis. However, the method mentioned above is limited because of its bottleneck of the knowledge acquisition. The model strategy of the Rotor-Bearing system of Hydro-Generator is discussed and a multi-degree-freedom nonlinear model is developed. It proposes the simulation in accordance with the three fields such as: waterpower, electric and machine. Mechanical, electrical and hydraulic forces acting on rotor externally can be taken into account during the model calculating process. The transient responses of the system are calculated by combined used the transfer matrix method. This paper brings forward a prototype of Hydro-Generator Sets Fault Diagnostic System in order to make a more efficient fault diagnostic decision.

scholarly journals Time Series RUL Estimation of Medium Voltage Connectors to Ease Predictive Maintenance Plans

2020 ◽  
Vol 10 (24) ◽  
pp. 9041
Author(s):  
Álvaro Gómez-Pau ◽  
Jordi-Roger Riba ◽  
Manuel Moreno-Eguilaz
Keyword(s):  
Time Series ◽  
Electrical Resistance ◽  
Time Series Data ◽  
Critical Role ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Series Data ◽  
Economic Losses ◽  
Medium Voltage ◽  
On Line

The ageing process of medium voltage power connectors can lead to important power system faults. An on-line prediction of the remaining useful life (RUL) is a convenient strategy to prevent such failures, thus easing the application of predictive maintenance plans. The electrical resistance of the connector is the most widely used health indicator for condition monitoring and RUL prediction, even though its measurement is a challenging task because of its low value, which typically falls in the range of a few micro-ohms. At the present time, the RUL of power connectors is not estimated, since their electrical parameters are not monitored because medium voltage connectors are considered cheap and secondary devices in power systems, despite they play a critical role, so their failure can lead to important power flow interruptions with the consequent safety risks and economic losses. Therefore, there is an imperious need to develop on-line RUL prediction strategies. This paper develops an on-line method to solve this issue, by predicting the RUL of medium voltage connectors based on the degradation trajectory of the electrical resistance, which is characterized by analyzing the electrical resistance time series data by means of the autoregressive integrated moving average (ARIMA) method. The approach proposed in this paper allows applying predictive maintenance plans, since the RUL enables determining when the power connector must be replaced by a new one. Experimental results obtained from several connectors illustrate the feasibility and accuracy of the proposed approach for an on-line RUL prediction of power connectors.

On-Line Health Condition Monitoring of Power Connectors Focused on Predictive Maintenance

2020 ◽  
pp. 1-1
Author(s):  
Jimmy Martinez ◽  
Alvaro Gomez-Pau ◽  
Jordi-Roger Riba ◽  
Manuel Moreno-Eguilaz
Keyword(s):  
Condition Monitoring ◽  
Health Condition ◽  
Predictive Maintenance ◽  
On Line

scholarly journals On-line Resistance Measurement of Substation Connectors Focused on Predictive Maintenance

2018 ◽  
Author(s):  
Akash Kadechkar ◽  
Jordi-Roger Riba ◽  
Manuel Moreno-Eguilaz ◽  
Francesca Capelli ◽  
David Gonzalez
Keyword(s):  
Predictive Maintenance ◽  
Resistance Measurement ◽  
On Line ◽  
Line Resistance
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