Detection of Tube Leaks and Their Location Using Input/Loss Methods

Lang, Fred D.; Rodgers, David A. T.; Mayer, Loren E.

doi:10.1115/power2004-52027

Cited by 10 publications

(7 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the reliability of the main oil A TPP shutdowns, whether planned or unplanned, can cause significant financial losses among which boiler tube leakage is the most dominant failure to cause power plant shutdowns. An extensive repairing cost ranging from 2 to 10 million dollars is typically required to repair these leaks [29]. Yong et al [35] utilized the decision-tree-based method to carry out the cost analysis of the economizer tube leaks in TPP.…”

Section: Turbine Motor Failure Analysismentioning

confidence: 99%

Real-World Data-Driven Machine-Learning-Based Optimal Sensor Selection Approach for Equipment Fault Detection in a Thermal Power Plant

2021

View full text Add to dashboard Cite

Due to growing electricity demand, developing an efficient fault-detection system in thermal power plants (TPPs) has become a demanding issue. The most probable reason for failure in TPPs is equipment (boiler and turbine) fault. Advance detection of equipment fault can help secure maintenance shutdowns and enhance the capacity utilization rates of the equipment. Recently, an intelligent fault diagnosis based on multivariate algorithms has been introduced in TPPs. In TPPs, a huge number of sensors are used for process maintenance. However, not all of these sensors are sensitive to fault detection. The previous studies just relied on the experts’ provided data for equipment fault detection in TPPs. However, the performance of multivariate algorithms for fault detection is heavily dependent on the number of input sensors. The redundant and irrelevant sensors may reduce the performance of these algorithms, thus creating a need to determine the optimal sensor arrangement for efficient fault detection in TPPs. Therefore, this study proposes a novel machine-learning-based optimal sensor selection approach to analyze the boiler and turbine faults. Finally, real-world power plant equipment fault scenarios (boiler water wall tube leakage and turbine electric motor failure) are employed to verify the performance of the proposed model. The computational results indicate that the proposed approach enhanced the computational efficiency of machine-learning models by reducing the number of sensors up to 44% in the water wall tube leakage case scenario and 55% in the turbine motor fault case scenario. Further, the machine-learning performance is improved up to 97.6% and 92.6% in the water wall tube leakage and turbine motor fault case scenarios, respectively.

show abstract

Section: Turbine Motor Failure Analysismentioning

confidence: 99%

Real-World Data-Driven Machine-Learning-Based Optimal Sensor Selection Approach for Equipment Fault Detection in a Thermal Power Plant

2021

View full text Add to dashboard Cite

show abstract

“…Additionally, the methods of mass balance testing are time consuming and are performed with a frequency which is not sufficient to protect the plant from serious damage. More advanced approaches to leak detection by the use of mass balance testing are based on the Input/Loss Method, a patented method (based on integration of system stoichiometrics with thermodynamics) which computes fuel chemistry, heating value and fuel flow by integrating effluent measurements with thermodynamics [11].…”

Section: Current Approaches To Fault Detection In a Pipeline System Omentioning

confidence: 99%

Multiway PCA for Early Leak Detection in a Pipeline System of a Steam Boiler—Selected Case Studies

Swiercz

Mroczkowska²

2020

Sensors

View full text Add to dashboard Cite

In the paper the usability of the Multiway PCA (MPCA) method for early detection of leakages in the pipeline system of a steam boiler in a thermal-electrical power plant is presented. A long segment of measurements of selected process variables was divided into a series of “batches” (representing daily recordings of normal behavior of the plant) and used to create the MPCA model of a “healthy” system in a reduced space of three principal components (PC). The periodically updated MPCA model was used to establish the confidence ellipsoid for the “healthy” system in the PC coordinates. The staff’s decision of the probable leak detection is supported by comparison of the current location of the operating point (on the “fault trajectory”) with the boundaries of the confidence ellipsoid. It must be emphasized that due to daily and seasonal changes of heat/electricity demands, the process variables have substantially greater variability than in the examples of batch processes studied in literature. Despite those real challenges for the MPCA method, numerical examples confirmed that the presented approach was able to foresee the leaks earlier than the operator, typically 3–5 days before the boiler shutdown. The presented methodology may be useful in implementation of an on-line system, developed to improve safety and maintenance of boilers in a thermal-electrical power plant.

show abstract

“…The estimated cost of repairing massive damage to boilers in 2003 varied from $2 to $10 million per leak [5].…”

Section: Introductionmentioning

confidence: 99%

“…The high cost of emergency shutdowns and repairs of fluidizedbed boilers [5,7]despecially the high total number of days needed for repair (Figs. 1 and 2)dand the lack of successful systems for early leak detection and prediction are the main driving forces for research work in this area.…”

Section: Introductionmentioning

confidence: 99%

Early detection and prediction of leaks in fluidized-bed boilers using artificial neural networks

Rostek

Morytko

Jankowska

2015

Energy

View full text Add to dashboard Cite

Detection of Tube Leaks and Their Location Using Input/Loss Methods

Cited by 10 publications

References 1 publication

Real-World Data-Driven Machine-Learning-Based Optimal Sensor Selection Approach for Equipment Fault Detection in a Thermal Power Plant

Real-World Data-Driven Machine-Learning-Based Optimal Sensor Selection Approach for Equipment Fault Detection in a Thermal Power Plant

Multiway PCA for Early Leak Detection in a Pipeline System of a Steam Boiler—Selected Case Studies

Early detection and prediction of leaks in fluidized-bed boilers using artificial neural networks

Contact Info

Product

Resources

About