The Value of Enterprise Data Integration and Trending Analysis Results for an Effective Equipment Reliability Program

2005 ◽  
Author(s):  
Evan Niemkiewicz ◽  
Walter Walejeski
Keyword(s):  
Data Integration ◽  
Condition Monitoring ◽  
Performance Monitoring ◽  
Power Industry ◽  
Diagnostic Tools ◽  
Web Based ◽  
Diagnostic Parameters ◽  
Equipment Reliability ◽  
Maintenance Personnel ◽  
Diagnostic Technologies

In the power industry, many companies are trying to improve Equipment Reliability by focusing on Performance Monitoring and the application of diagnostic technologies. Software tools are focused on performing detailed technical analysis and trending of diagnostic parameters. The diagnostic data is often stored in archival databases that can track this data for many years. However, the analysis results generated by engineering and maintenance personnel are often stored outside the diagnostic tools in various databases, spreadsheets, and word documents. This valuable information is then difficult to track, trend, and then recall when a similar event occurs in the future. The paper will focus on developed and implemented web-based tools that facilitate tracking, trending, and sharing these analysis results across sites and enterprises. This paper further discusses implementation details and demonstrates the value of incorporating this information seamlessly into existing as well as developing condition monitoring programs.

The Application of Smart, Connected Power Plant Assets for Enhanced Condition Monitoring and Improving Equipment Reliability

2016 ◽  
Author(s):  
Michael Reid ◽  
Bernie Cook
Keyword(s):  
Power Plant ◽  
Data Storage ◽  
Condition Monitoring ◽  
Asset Management ◽  
Performance Monitoring ◽  
Significant Proportion ◽  
Electric Utility ◽  
Sensor Technology ◽  
Steam Turbines ◽  
Equipment Reliability

The U.S. electric utility industry continues to undergo dramatic change due to a number of key trends and also prolonged uncertainty. These trends include: • Increasing environmental regulations uncertainty • Natural gas supply uncertainty and price • Economic / decoupling of electricity demand growth from GDP • Aging coal and nuclear generation fleet / coal retirements • Aging workforce • Increasing distributed energy resources • Increasing customer expectations The transformation ultimately demands significant increases in power plant generation operating capabilities (e.g. flexibility, operating envelop, ramp rates, turn-down etc.) and higher levels of equipment reliability, while reducing O&M and capital budgets. Achieving higher levels of equipment reliability and flexibility, with such tightening budget and resource constraints, requires a very disciplined approach to maintenance and an optimized mix of the following maintenance practices: • Reactive (run-to-failure) • Preventive (time-based) • Predictive (condition-based) • Proactive (combination of 1, 2 and 3 + root cause failure analysis) Many U.S. electric utilities with fossil generation have adopted and implemented elements of an equipment reliability process consistent with Institute of Nuclear Power Operations (INPO) AP-913. The Electric Power Research Institute has created a guideline modeled from the learnings of AP-913, that consists of six key sub-processes [1]: 1. Scoping and identification of critical components (identifying system and component criticality) 2. Continuing equipment reliability improvement (establishing and continuously improving system and component maintenance bases) 3. Preventive Maintenance (PM) implementation (implementing the PM program effectively) 4. Performance monitoring (monitoring system and component performance) 5. Corrective action 6. Life cycle management (long-term asset management) A significant proportion of Duke Energy’s coal fleet is of an age where individual components have reached their design intent end-of-life thereby creating an increased need for performance monitoring. Until recent times this was largely performed by maintenance technicians with handheld devices. This approach does not allow regular data collection for trending and optimization of maintenance practices across the fleet. Significant and recent advances in sensor technology, microprocessors, data acquisition, data storage, communication technology, and software have enabled the transformation of critical power plant assets such as steam turbines, combustion turbines, generators, transformers, and large balance-of-plant equipment into smart, connected power plant assets. These enhanced assets, in conjunction with visualization software, provide a comprehensive conditioning monitoring solution that continuously acquires sensory data and performs real time analysis to provide information and insight. This advanced condition monitoring capability has been successfully applied to obtain earlier detection of equipment issues and failures and is key to improving overall equipment reliability. This paper describes an approach by Duke Energy to create and apply smart, connected power plant assets to greatly enhance its fossil generation continuous condition monitoring capabilities. It will discuss the value that is currently being realized and also look at future possibilities to apply big data and analytics to enhance information, insight, and actionable intelligence.

Improving Plant Operations With Performance and Condition Monitoring Systems

2009 ◽  
Author(s):  
Jonathan Hicks ◽  
Donald Kerber
Keyword(s):  
Condition Monitoring ◽  
First Principles ◽  
Performance Monitoring ◽  
Power Industry ◽  
Complete Analysis ◽  
Monitoring Systems ◽  
First Principles Calculations ◽  
Operation Condition ◽  
Input Quality ◽  
Condition Monitoring Systems

Business operations in the power industry, as with every other industry, require payback for the resolution of process problems. In order to achieve this payback many plant monitoring systems are used such as Performance and Condition Monitoring Systems. Performance Monitoring systems use first principles calculations for a baseline and test performance case, both of these calculations can then be reconciled to give a cost associated with off design operation. Condition Monitoring systems operate on the Advanced Pattern Recognition Algorithm (APR) and can be used to identify slight variations in the performance of a system largely independent of the quality of the inputs. The process to identify deviations is as follows: a predicted value is calculated for every modeled parameter, a difference between the predicted value and actual value is calculated, the difference is compared to an allowed threshold, and then problems are reported. The purpose of the following report is to identify the strengths and weaknesses of each monitoring system and show how they may be used together for a more thorough analysis of off design operation. Performance monitoring systems provide a reliable and actionable assessment of Heat Rate deviations when they occur in the field. The calculations provided by these systems can lead directly to the diagnosis of real performance problems as well as instrument inaccuracies and provide the financial implications of each issue. Performance monitoring systems, however, are highly dependent upon input quality as their results are found using first principles calculations. Condition Monitoring systems can be used to identify smaller deviations from normal at an earlier time. The major strength of the APR process is its ability to identify these deviations regardless of input quality. As with Performance Monitoring Systems the APR process will identify real problems as well as instrument problems. However, it will not provide a financially quantifiable result as to the effect of the deviation. Monitoring systems should be able to provide quantifiable results with minimal personnel use in order to achieve a payback for more operational problems. This paper will discuss how the use of Performance Monitoring Systems in conjunction with Condition Monitoring Systems will provide the complete analysis needed by the power industry today.

scholarly journals Definition of Set of diagnostic Parameters of System based on the Functional Spaces Theory

2019 ◽  
Vol 18 (4) ◽  
pp. 949-975 ◽  
Author(s):  
Valentin Senchenkov ◽  
Damir Absalyamov ◽  
Dmitriy Avsyukevich
Keyword(s):  
Fourier Series ◽  
Experimental Studies ◽  
Numerical Procedure ◽  
Physical Nature ◽  
Convergent Series ◽  
Technical Condition ◽  
Partial Sums ◽  
Diagnostic Tools ◽  
Diagnostic Parameters ◽  
Integrable Functions

The development of methodical and mathematical apparatus for formation of a set of diagnostic parameters of complex technical systems, the content of which consists of processing the trajectories of the output processes of the system using the theory of functional spaces, is  considered in this paper. The trajectories of the output variables are considered as Lebesgue measurable functions. It ensures a unified approach to obtaining diagnostic parameters regardless  a physical nature of these variables and a set of their jump-like changes (finite discontinuities of trajectories). It adequately takes into account a complexity of the construction, a variety of physical principles and algorithms of systems operation. A structure of factor-spaces of measurable square Lebesgue integrable functions, ( spaces) is defined on sets of trajectories. The properties of these spaces allow to decompose the trajectories by the countable set of mutually orthogonal directions and represent them in the form of a convergent series. The choice of a set of diagnostic parameters as an ordered sequence of coefficients of decomposition of trajectories into partial sums of Fourier series is substantiated. The procedure of formation of a set of diagnostic parameters of the system, improved in comparison with the initial variants, when the trajectory is decomposed into a partial sum of Fourier series by an orthonormal Legendre basis, is presented. A method for the numerical determination of the power of such a set is proposed. New aspects of obtaining diagnostic information from the vibration processes of the system are revealed. A structure of spaces of continuous square Riemann integrable functions ( spaces) is defined on the sets of vibrotrajectories. Since they are subspaces in the afore mentioned factor-spaces, the general methodological bases for the transformation of vibrotrajectories remain unchanged. However, the algorithmic component of the choice of diagnostic parameters becomes more specific and observable. It is demonstrated by implementing a numerical procedure for decomposing vibrotrajectories by an orthogonal trigonometric basis, which is contained in spaces. The processing of the results of experimental studies of the vibration process and the setting on this basis of a subset of diagnostic parameters in one of the control points of the system is provided. The materials of the article are a contribution to the theory of obtaining information about the technical condition of complex systems. The applied value of the proposed development is a possibility of their use for the synthesis of algorithmic support of automated diagnostic tools.

Power Neighboring Interval Matching Based PMC Integration

2016 ◽  
Vol 25 (08) ◽  
pp. 1650086
Author(s):  
Yuelong Li ◽  
Jigang Wu ◽  
Yawen Chen ◽  
Jason Mair ◽  
David Eyers ◽  
...  
Keyword(s):  
Data Integration ◽  
Power Consumption ◽  
Power Dissipation ◽  
System Performance ◽  
Performance Monitoring ◽  
Power Estimation ◽  
Raw Data ◽  
Integration Approach ◽  
Common Task ◽  
The Status

Performance monitoring counters (PMCs) are of great value to monitor the status of processors and their further analysis and modeling. In this paper, we explore a novel problem called PMC integration, i.e., how to combine a group of PMCs which are collected asynchronously together. It is well known that, due to hardware constraints, the number of PMCs that can be measured concurrently is strictly limited. It means we cannot directly acquire all the phenomenon features that are related with the system performance. Clearly, this source raw data shortage is extremely frustrating to PMCs based analysis and modeling tasks, such as PMCs based power estimation. To deal with this problem, we introduce a neighboring interval power values based PMC data integration approach. Based on the activity similarity of easily collected power dissipation values, the proposed approach can automatically combine distinct categories of PMC data together and hence realize the recovery of intact raw PMC data. In addition, the significance and effectiveness of the proposed approach are experimentally verified on a common task, the PMCs based power consumption modeling.

Condition Monitoring of Ship Propulsion Systems: State-of-the-Art, Development Trend and Role of Digital Twin

2021 ◽  
Author(s):  
Amir R. Nejad ◽  
Etienne Purcell ◽  
Mostafa Valavi ◽  
Roman Hudak ◽  
Benjamin Lehmann ◽  
...  
Keyword(s):  
Condition Monitoring ◽  
Performance Monitoring ◽  
State Of The Art ◽  
Development Trend ◽  
The Arctic ◽  
Special Focus ◽  
Shipping Industry ◽  
Propulsion Systems ◽  
Digital Twin ◽  
Ship Propulsion

Abstract This paper describes the current implementations and development trends of condition monitoring as it pertains to ship propulsion systems. In terms of total incidents in the shipping industry in the last five years, failures relating to the propulsion system represent the majority. Condition monitoring offers effective early detection of failure which translates to increased reliability and decreased maintenance costs. Current industrial practices are often limited to performance monitoring rather than condition monitoring. Special focus is afforded to how condition monitoring is implemented on board ships, which regulatory codes are relevant and the summary of state-of-the-art research in marine machinery. Moreover, operation and monitoring in extreme environmental conditions, such as the Arctic and Antarctic with ice impact on the propulsion has been discussed. The new developments, in particular, digital twin approaches in health and condition monitoring have been highlighted, considering its pros and cons and potential challenges.

Prognostics and Health Management of Choke Valves Subject to Erosion

2013 ◽  
pp. 313-331
Author(s):  
Giulio Gola ◽  
Bent H. Nystad
Keyword(s):  
Condition Monitoring ◽  
Performance Monitoring ◽  
Oil And Gas ◽  
Health Management ◽  
Cost Savings ◽  
Remaining Useful Life ◽  
Work Processes ◽  
Offshore Oil And Gas ◽  
And Performance ◽  
Monitoring Technologies

Oil and gas industries are constantly aiming at improving the efficiency of their operations. In this respect, focus is on the development of technology, methods, and work processes related to equipment condition and performance monitoring in order to achieve the highest standards in terms of safety and productivity. To this aim, a key issue is represented by maintenance optimization of critical structures, systems, and components. A way towards this goal is offered by Condition-Based Maintenance (CBM) strategies. CBM aims at regulating maintenance scheduling based on data analyses and system condition monitoring and bears the potential advantage of obtaining relevant cost savings and improved operational safety and availability. A critical aspect of CBM is its integration with condition monitoring technologies for handling a wide range of information sources and eventually making optimal decisions on when and what to repair. In this chapter, a CBM case study concerning choke valves utilized in Norwegian offshore oil and gas platforms is proposed and investigated. The objective is to define a procedure for optimizing maintenance of choke valves by on-line monitoring their condition and determining their Remaining Useful Life (RUL). Choke valves undergo erosion caused by sand grains transported by the oil-water-gas mixture extracted from the well. Erosion is a critical problem which can affect the correct valve functioning, resulting in revenue losses and cause environmental hazards.

scholarly journals Log4Perf: suggesting and updating logging locations for web-based systems’ performance monitoring

2019 ◽  
Vol 25 (1) ◽  
pp. 488-531
Author(s):  
Kundi Yao ◽  
Guilherme B. de Pádua ◽  
Weiyi Shang ◽  
Catalin Sporea ◽  
Andrei Toma ◽  
...  
Keyword(s):  
Performance Monitoring ◽  
Web Based
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