scholarly journals Condition Monitoring for Early Failure Detection. Frognerparken Pumping Station as Case Study

2014 ◽  
Vol 70 ◽  
pp. 162-171 ◽  
Author(s):  
S.P. Berge ◽  
B.F. Lund ◽  
R. Ugarelli
Keyword(s):  
Condition Monitoring ◽  
Failure Detection ◽  
Early Failure ◽  
Pumping Station

Cointegration Modelling for Health and Condition Monitoring of Wind Turbines - An Overview

2022 ◽  
Author(s):  
P.B. Dao
Keyword(s):  
Fault Detection ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Control Charts ◽  
Failure Detection ◽  
Short Review ◽  
Early Failure ◽  
Promising Tool ◽  
Condition Monitoring Systems ◽  
Scientists And Engineers

Abstract. The cointegration method has recently attracted a growing interest from scientists and engineers as a promising tool for the development of wind turbine condition monitoring systems. This paper presents a short review of cointegration-based techniques developed for condition monitoring and fault detection of wind turbines. In all reported applications, cointegration residuals are used in control charts for condition monitoring and early failure detection. This is known as the residual-based control chart approach. Vibration signals and SCADA data are typically used with cointegration in these applications. This is due to the fact that vibration-based condition monitoring is one of the most common and effective techniques (used for wind turbines); and the use of SCADA data for condition monitoring and fault detection of wind turbines has become more and more popular in recent years.

scholarly journals Developing a 3D decision-making grid based on failure modes and effects analysis with a case study in the steel industry

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Arash Shahin ◽  
Ashraf Labib ◽  
Ali Haj Shirmohammadi ◽  
Hadi Balouei Jamkhaneh
Keyword(s):  
Decision Making ◽  
Condition Monitoring ◽  
Detection Rate ◽  
Failure Modes ◽  
Failure Detection ◽  
Operating Conditions ◽  
Content Type ◽  
Casting Unit ◽  
Third Dimension

PurposeThe aim of this study is to develop a 3D model of decision- making grid (DMG) considering failure detection rate.Design/methodology/approachIn a comparison between DMG and failure modes and effects analysis (FMEA), severity has been assumed as time to repair and occurrence as the frequency of failure. Detection rate has been added as the third dimension of DMG. Nine months data of 21 equipment of casting unit of Mobarakeh Steel Company (MSC) has been analyzed. Then, appropriate condition monitoring (CM) techniques and maintenance tactics have been suggested. While in 2D DMG, CM is used when downtime is high and frequency is low; its application has been developed for other maintenance tactics in a 3D DMG.FindingsFindings indicate that the results obtained from the developed DMG are different from conventional grid results, and it is more capable in suggesting maintenance tactics according to the operating conditions of equipment.Research limitations/implicationsIn failure detection, the influence of CM techniques is different. In this paper, CM techniques have been suggested based on their maximum influence on failure detection.Originality/valueIn conventional DMG, failure detection rate is not included. The developed 3D DMG provides this advantage by considering a new axis of detection rate in addition to mean time to repair (MTTR) and failure frequency, and it enhances maintenance decision-making by simultaneous selection of suitable maintenance tactics and condition-monitoring techniques.

scholarly journals Case study on the effectiveness of condition monitoring techniques for fault diagnosis of pumps in thermal power plant

2019 ◽  
Vol 23 (1) ◽  
pp. 70-75 ◽  
Author(s):  
Caneon Kurien ◽  
Ajay Kumar Srivastava
Keyword(s):  
Power Plant ◽  
Condition Monitoring ◽  
Thermal Power Plant ◽  
Thermal Power ◽  
Cooling Water ◽  
Failure Detection ◽  
Feed Water ◽  
Water Pump ◽  
Monitoring Techniques

Abstract A case study was carried out to investigate the effectiveness of condition monitoring techniques in the early failure detection of pumps in a thermal power plant. Various condition monitoring techniques used in this case study involved vibration analysis, motor current signature analysis, noise monitoring and wear debris analysis. These techniques were applied on the three pumps, namely boiler feed water pump, auxiliary cooling water pump and condensate extraction pump, which have to work continuously for the operation of the thermal power plant. Vibration analysis of the auxiliary cooling water pump showed that there is a rising trend in the acceleration values at its driving and non-driving end indicating the deterioration of bearings. Motor current index range of all the pumps was found to be within acceptable limits. Wear debris analysis of lubricant in the hydraulic coupling of boiler feed water pump indicated the presence of sand, dirt and low alloy steel sliding wear particles in it. Condition monitoring techniques have been proved to be an effective technique in early failure detection of pumps.

scholarly journals A Combined Approach for Model-Based PV Power Plant Failure Detection and Diagnostic

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1261
Author(s):  
Christopher Gradwohl ◽  
Vesna Dimitrievska ◽  
Federico Pittino ◽  
Wolfgang Muehleisen ◽  
András Montvay ◽  
...  
Keyword(s):  
Power Plants ◽  
Large Scale ◽  
Failure Detection ◽  
Energy Yield ◽  
Combined Approach ◽  
Levelized Cost Of Electricity ◽  
Term Operation ◽  
Model Based

Photovoltaic (PV) technology allows large-scale investments in a renewable power-generating system at a competitive levelized cost of electricity (LCOE) and with a low environmental impact. Large-scale PV installations operate in a highly competitive market environment where even small performance losses have a high impact on profit margins. Therefore, operation at maximum performance is the key for long-term profitability. This can be achieved by advanced performance monitoring and instant or gradual failure detection methodologies. We present in this paper a combined approach on model-based fault detection by means of physical and statistical models and failure diagnosis based on physics of failure. Both approaches contribute to optimized PV plant operation and maintenance based on typically available supervisory control and data acquisition (SCADA) data. The failure detection and diagnosis capabilities were demonstrated in a case study based on six years of SCADA data from a PV plant in Slovenia. In this case study, underperforming values of the inverters of the PV plant were reliably detected and possible root causes were identified. Our work has led us to conclude that the combined approach can contribute to an efficient and long-term operation of photovoltaic power plants with a maximum energy yield and can be applied to the monitoring of photovoltaic plants.

Comparison of postal and non-postal post-vasectomy semen sample submission strategies on compliance and failures: an 11-year analysis of the audit database of the Association of Surgeons in Primary Care of the UK

2021 ◽  
pp. bmjsrh-2021-201064
Author(s):  
Melanie Atkinson ◽  
Gareth James ◽  
Katie Bond ◽  
Zoe Harcombe ◽  
Michel Labrecque
Keyword(s):  
Primary Care ◽  
Semen Analysis ◽  
Failure Detection ◽  
Semen Sample ◽  
Failure Rates ◽  
Early Failure ◽  
Detection Rates ◽  
Late Failure ◽  
The Difference ◽  
The Uk

BackgroundVasectomy occlusive success is defined by the recommendation of ‘clearance’ to stop other contraception, and is elicited by post-vasectomy semen analysis (PVSA). We evaluated how the choice of either a postal or non-postal PVSA submission strategy was associated with compliance to PVSA and effectiveness of vasectomy.MethodsWe studied vasectomies performed in the UK from 2008 to 2019, reported in annual audits by Association of Surgeons in Primary Care members. We calculated the difference between the two strategies for compliance with PVSA, and early and late vasectomy failure. We determined compliance by adding the numbers of men with early failure and those given clearance. We performed stratified analyses by the number of test guidance for clearance (one-test/two-test) and the study period (2008–2013/2014–2019).ResultsAmong 58 900 vasectomised men, 32 708 (56%) and 26 192 (44%) were advised submission by postal and non-postal strategies, respectively. Compliance with postal (79.5%) was significantly greater than with non-postal strategy (59.1%), the difference being 20.4% (95% CI 19.7% to 21.2%). In compliant patients, overall early failure detection was lower with postal (0.73%) than with non-postal (0.94%) strategy (−0.22%, 95% CI −0.41% to −0.04%), but this difference was neither clinically nor statistically significant with one-test guidance in 2014–2019. There was no difference in late failure rates.ConclusionsPostal strategy significantly increased compliance to PVSA with similar failure detection rates. This resulted in more individuals receiving clearance or early failure because of the greater percentage of postal samples submitted. Postal strategy warrants inclusion in any future guidelines as a reliable and convenient option.

The SMART SRP Well – Application of Edge Analytics for Automated Well Performance Control and Condition Monitoring in a Mature Brownfield Environment – A Case Study from Austria

2021 ◽  
Author(s):  
Christian Windisch
Keyword(s):  
Condition Monitoring ◽  
Data Communication ◽  
Holistic Approach ◽  
Well Performance ◽  
Measuring Device ◽  
Technology Transfers ◽  
Failure Conditions ◽  
Diverse Data ◽  
Bottomhole Pressure

Abstract This paper presents a holistic approach to modern oilfield and well surveillance through the inclusion of state-of-the-art edge computing applications in combination with a novel type of data transmission technology and algorithms developed in-house for automatic condition monitoring of SRP systems. The objective is to enable the responsible specialist staff to focus on the most important decisions regarding oilfield management, rather than wasting time with data collection and preparation. An own operated data communication system, based on LPWAN-technology transfers the dyno-cards, generated by an electric load cell, into the in-house developed production assistance software platform. Suitable programmed AI-algorithms enable automatic condition detection of the incoming dyno cards, including conversion and analysis of the corresponding subsurface dynamograms. A smart alarming system informs about occurring failure conditions and specifies whether an incident of rod rupture, pump-off condition, gas lock or paraffin precipitation occurred in the well. A surface mounted measuring device delivers liquid level and bottomhole pressure information automatically into the software. Based on these diverse data, the operations team plans the subsequent activities. The holistic application approach is illustrated using the case study of an SPR-operated well in an Austrian brownfield.

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