Fault Detection in Coal Mills Used in Power Plants

Odgaard, Peter Fogh; Mataji, Babak

doi:10.3182/20060625-4-ca-2906.00036

Cited by 15 publications

(5 citation statements)

References 4 publications

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“…P. F. Odgaard and B. Mataji [119][120][121][122] presented an observer-based method for detecting faults and estimating moisture content in the coal in coal mills using simplified energy balance model of the coal mill. A kalman estimator is designed and sign of the energy imbalance is used to isolate the faults.…”

Section: Mill Fault Detection Using Quantitative Model-based Methodsmentioning

confidence: 99%

“…The researches done to identify problems in the milling system using model based fault diagnosis are provided in [3,[119][120][121][122][123][124][125][126][127][128]. P. F. Odgaard and B. Mataji [119][120][121][122] presented an observer-based method for detecting faults and estimating moisture content in the coal in coal mills using simplified energy balance model of the coal mill.…”

Section: Mill Fault Detection Using Quantitative Model-based Methodsmentioning

confidence: 99%

“…The quantitative model based techniques for the mill FDD are presented in [3,[119][120][121][122][123][124][125][126][127][128]. These methods are sensitive to the mathematical model used to describe the process behaviour.…”

Section: Comparison Of Various Fault Diagnostic Approachesmentioning

confidence: 99%

See 2 more Smart Citations

Review of control and fault diagnosis methods applied to coal mills

Agrawal

Panigrahi

Subbarao

2015

Journal of Process Control

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Section: Mill Fault Detection Using Quantitative Model-based Methodsmentioning

confidence: 99%

Section: Mill Fault Detection Using Quantitative Model-based Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Review of control and fault diagnosis methods applied to coal mills

Agrawal

Panigrahi

Subbarao

2015

Journal of Process Control

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“…As early as in 1998, an improved dynamic model of vertical spindle roll mill was established in reference [9] in order to realize precise control of pulverized fuel flow, in which the abnormal operation conditions were also considered and simulated, but it cannot support the simulation in failure state. In reference [10], an optimal observer was designed based on the simple dynamic model, in which the estimated residual can be used for faults detection. In this model, the input coal flow was assumed to be equal of the output coal flow, and only the energy transfer process was considered.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Coal Mill System Used for Fault Simulation

Boyu

Zeng

et al. 2020

Energies

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Monitoring and diagnosis of coal mill systems are critical to the security operation of power plants. The traditional data-driven fault diagnosis methods often result in low fault recognition rate or even misjudgment due to the imbalance between fault data samples and normal data samples. In order to obtain massive fault sample data effectively, based on the analysis of primary air system, grinding mechanism and energy conversion process, a dynamic model of the coal mill system which can be used for fault simulation is established. Then, according to the mechanism of various faults, three types of faults (i.e., coal interruption, coal blockage and coal self-ignition) are simulated through the modification of model parameters. The simulation shows that the dynamic characteristic of the model is consistent with the actual object, the relative error of each output variable is less than 2.53%, and the total average relative error of all outputs is about 1.2%. The model has enough accuracy and adaptability for fault simulation, and the problem of massive fault samples acquisition can be effectively solved by the proposed method.

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“…Experience shows that this type of detection can often be misleading or late. Besides this, mill's differential pressure and measurements of mill-motor amperage, vibrations [6], [7] and energy balance of mill [8] are used for monitoring of grinding and conveying system. In fact, controlled variables mentioned above are only indirectly related to coal flow.…”

Section: Introductionmentioning

confidence: 99%

A Surveillance of Direct-Firing System for Pulverized-Coal Using Statistically Treated Signals from Intrusive Electrostatic Sensors

Jurjevčič

Senegačnik

Kuštrin

2017

SV-JME

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Operational surveillance of all vital parts of thermal power plants is nowadays more important than any time before due to requirements for their extremely flexible operation resulting from intermittent behaviour of renewable energy sources. New methods for online measuring of pneumatic transport provide new possibilities for control and early fault detection of coal grinding and conveying system in direct-fired power plant boilers. Arrays of intrusive electrostatic sensors are an attractive option due to their inexpensive application and good spatial sensitivity required in large rectangular ducts of pulverized-coal systems. In this study, statistically treated electrostatic signals are used for detection of unexpected change in operating regime of coal grinding and conveying. Model-based and model-free autocorrelation reduction techniques are used to reduce the inherent autocorrelation of data. Forming batch-means of data, a model-free autocorrelation reduction technique is proposed in combination with an autoregressive-integrated-moving-average (ARIMA) method. Residuals between real and ARIMA-model fitted data are entered into exponentially-weighted-moving-average (EWMA) control chart for statistical surveillance of the process. The robust and cost-effective measuring method accompanied with a simple and intuitive control scheme proves to be effective for early fault detection of the pulverized-coal preparation system.

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Fault Detection in Coal Mills Used in Power Plants

Cited by 15 publications

References 4 publications

Review of control and fault diagnosis methods applied to coal mills

Review of control and fault diagnosis methods applied to coal mills

Modeling of Coal Mill System Used for Fault Simulation

A Surveillance of Direct-Firing System for Pulverized-Coal Using Statistically Treated Signals from Intrusive Electrostatic Sensors

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