Fault detection of FWTPs in coal‐fired power plants using K‐WD‐KPCA in consideration of multiple operation conditions

2020 ◽  
Author(s):  
Yuling Tang ◽  
Shirong Zhang
Keyword(s):  
Fault Detection ◽  
Power Plants ◽  
Operation Conditions ◽  
Multiple Operation

scholarly journals Online Fault Detection of Fixed-Wing UAV Based on DKPCA Algorithm with Multiple Operation Conditions Considered

2020 ◽  
Vol 38 (3) ◽  
pp. 619-626
Author(s):  
Shaojun Liang ◽  
Shirong Zhang ◽  
Xing Zheng ◽  
Dongsheng Lin
Keyword(s):  
Fault Detection ◽  
Measurement Errors ◽  
Wavelet Denoising ◽  
Operation Condition ◽  
Data Set ◽  
Control Loops ◽  
Operation Conditions ◽  
Fault Monitoring ◽  
Online Matching ◽  
Multiple Operation

The mission execution process of a fixed-wing UAV has multiple phases and multiple operation conditions. Its parameters are nonlinear and dynamic. These characteristics make its online fault detection rather complicated. To carry out the fault detection, this paper selects nine key parameters of the transverse, longitudinal and velocity control loops of the UAV to characterize its real-time conditions. The core parameters are dynamically preprocessed to construct an augmented matrix so as to describe the dynamic characteristics of the UAV. Then, the improved k-mediods* algorithm is used to cluster the operation conditions of the UAVs by means of augmented dimensions. Neural networks are used to achieve the online matching of operation conditions. To overcome the nonlinearity of the UAV, the fault detection is performed by using the DKPCA algorithm; the fault monitoring is conducted through constructing the compound indexes of SPE and T2, notated as FAI. Furthermore, the fault separation algorithm is proposed to specify the variables of fault from the augmented high-dimensional data set. In order to deal with the erroneous reporting of faults due to measurement errors, the paper conducts the wavelet denoising of FAI, the compound indexes of the DKPCA algorithm. Finally, the data set collected from a real UAV flight is used to verify the effectiveness of the DKPCA algorithm for operation condition clustering and matching, fault detection and wavelet denoising.

Data-driven fault diagnosis of FW-UAVs with consideration of multiple operation conditions

2021 ◽  
Author(s):  
Shaojun Liang ◽  
Shirong Zhang ◽  
Yuping Huang ◽  
Xing Zheng ◽  
Jian Cheng ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Data Driven ◽  
Operation Conditions ◽  
Multiple Operation

Characterization of Coal Fly Ash, Bottom Ash and their Possibilities as Catalysts for Biodiesel Production

2021 ◽  
Vol 904 ◽  
pp. 413-418
Author(s):  
Wilasinee Kingkam ◽  
Sasikarn Nuchdang ◽  
Dussadee Rattanaphra
Keyword(s):  
Fly Ash ◽  
Palm Oil ◽  
Power Plants ◽  
Coal Fly Ash ◽  
Bottom Ash ◽  
Biodiesel Production ◽  
Catalyst Loading ◽  
Free Lime ◽  
Operation Conditions ◽  
Class C

Coal fly ash (CFA) and bottom ash (BA) obtained from coal fired power plants in Thailand and local supplier were characterized using XRF, XRD and N2 adsorption-desorption techniques. Their possibilities for conversion of palm oil into biodiesel were investigated. Selected CFA was also modified with lanthanum (La) at different La loading and the influence of La loading on biodiesel conversion was evaluated. The resulted showed that the Class C CFA as contained large amount of CaO (free lime) could catalyze the transesterification to achieve the highest FAME content of 89% under the operation conditions; the reaction temperature of 200 °C, the reaction pressure of 39 bars, the catalyst loading of 5 wt% of oil, the molar of oil to methanol of 1:30 and the stirring speed of 600 rpm for 5 h. The addition of La on the Class C CFA had a negative effect on conversion of palm oil. The FAME content decreased gradually from 89 to 62% with increasing La loading from 0 to 1 wt%.

A New Model-Based Tool for Fault Detection and Isolation in Machine- and Rotordynamics

1995 ◽  
Author(s):  
Dirk Söffker
Keyword(s):  
Decision Making ◽  
Fault Detection ◽  
Signal Analysis ◽  
Power Plants ◽  
Crack Detection ◽  
Fault Detection And Isolation ◽  
Vibration Monitoring ◽  
Monitoring Systems ◽  
Implicit Assumption ◽  
Signal Parameters

Abstract Reliability and safety aspects are becoming much more important due to higher quality requirements, complicated and/or connected processes. The fault monitoring systems to be commonly used in machine- and rotordynamics are based on signal analysis methods. Furthermore, various kinds of fault detection and isolation (FDI)-schemes are already applied to a lot of technical applications of detecting and isolating sensor and actuator failures (Isermann, 1994; van Schrick, 1994) and also to fault detection in power plants (in general) or in manufacturing machines. An implicit assumption is that process or machine changes due to faults lead to changes in calculated parameters, which are unique and unambiguous. In the case of applying methods of signal analysis this means spectrums etc. the vibration behaviour will be monitored very well but have to be interpreted. On the other hand signal parameters usually only describe the system by analyzing output signals without use of known and unknown inner parameters and/or inputs. These parameters are available, and normally this knowledge is used by the operating staff interpreting the resulting signal parameters. In this way a decision-making problem appears so that questions about the physical character of faults, about the existence of special faults and also about the location of failures/faults has to be answered. In this way the experience and knowledge of the interpreting persons are very important. In this contribution the problems of the decision-making process are tried to defuse: • The available knowledge about the unfaulty system parameters is used to built up beside a nominal system model an unambiguous fault-specific ratio. Inner states of the structure are estimated by an PI-observer. • The developed robust PI-observer (Söffker et al., 1993a; Söffker et al., 1995a) estimates inner states and unknown inputs. In (Söffker et al., 1993b) this new method is applied to the crack detection of a rotor, but not proved. In this paper the proof is given and a generalization is described. The advantages in contrast to usual signal based vibration monitoring systems and also modern FDI-schemes are shown.

scholarly journals Power efficiency estimation based health monitoring and fault detection of modular and reconfigurable robot

2021 ◽  
Author(s):  
Jing Yaun
Keyword(s):  
Fault Detection ◽  
Health Monitoring ◽  
Power Efficiency ◽  
Joint Torque ◽  
Robot System ◽  
Operation Conditions ◽  
Efficiency Estimation ◽  
Joint Torque Sensor ◽  
Reconfigurable Robot ◽  
Critical Components

Power efficiency degradation of machines often provides intrinsic indication of problems associated with their operation conditions. Inspired by this observation, in this thesis work, a simple yet effective power efficiency estimation base health monitoring and fault detection technique is proposed for modular and reconfigurable robot with joint torque sensor. The design of the Ryerson modular and reconfigurable robot system is first introduced, which aims to achieve modularity and compactness of the robot modules. Critical components, such as the joint motor, motor driver, harmonic drive, sensors, and joint brake, have been selected according to the requirement. Power efficiency coefficients of each joint module are obtained using sensor measurements and used directly for health monitoring and fault detection. The proposed method has been experimentally tested on the developed modular and reconfigurable robot with joint torque sensing and a distributed control system. Experimental results have demonstrated the effectiveness of the proposed method.

scholarly journals Overview of fault detection methods for power plant control systems purposes

2019 ◽  
pp. 127-129
Author(s):  
Dmytro Shram ◽  
Oleksandr Stepanets
Keyword(s):  
Fault Detection ◽  
Control Systems ◽  
Power Plants ◽  
Fault Detection And Isolation ◽  
Data Driven ◽  
Detection Methods ◽  
Model Free ◽  
Power Plant Control ◽  
Technical Details ◽  
Plant Control

The main objective of this paper is to review of fault detection and isolation (FDI) methods and applications on various power plants. Due to the focus of the topic, on model and model-free FDI methods, technical details were kept in the references. We will overview the methods in terms of model-based, data driven and signal based methods further in the paper. Principles of three FDI methods are explained and characteristics of number of some popular techniques are described. It also summarizes data-driven methods and applications related to power generation plants. Parts of control system applications of FDI in TPPs with possible faults are shown in the Table I. Some popular techniques for the various faults in TPPs are discussed also.

scholarly journals An Adaptive Early Fault Detection Model of Induced Draft Fans Based on Multivariate State Estimation Technique

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4787
Author(s):  
Ruijun Guo ◽  
Guobin Zhang ◽  
Qian Zhang ◽  
Lei Zhou ◽  
Haicun Yu ◽  
...  
Keyword(s):  
Fault Detection ◽  
State Estimation ◽  
Power Plants ◽  
Similarity Index ◽  
Adaptive Strategy ◽  
Estimation Technique ◽  
Operating Characteristics ◽  
Model Accuracy ◽  
Auxiliary Equipment ◽  
Early Fault Detection

The induced draft (ID) fan is an important piece of auxiliary equipment in coal-fired power plants. Early fault detection of the ID fan can provide predictive maintenance and reduce unscheduled shutdowns, thus improving the reliability of the power generation. In this study, an adaptive model was developed to achieve the early fault detection of ID fans. First, a non-parametric monitoring model was constructed to describe the normal operating characteristics with the multivariate state estimation technique (MSET). A similarity index representing operation status was defined according to the prediction deviations to produce warnings of early faults. To deal with the model accuracy degradation because of variant condition operation of the ID fan, an adaptive strategy was proposed by using the samples with a high data quality index (DQI) to manage the memory matrix and update the MSET model, thereby improving the fault detection results. The proposed method was applied to a 300 MW coal-fired power plant to achieve the early fault detection of an ID fan. In addition, fault detection by using the model without an update was also compared. Results show that the update strategy can greatly improve the MSET model accuracy when predicting normal operations of the ID fan; accordingly, the fault can be detected more than 4 h earlier by using the strategy with the adaptive update when compared to the model without an update.

scholarly journals Characterization and Corrections for Clamp-On Fluid Temperature Measurements in Turbulent Flows

2018 ◽  
Vol 10 (3) ◽  
Author(s):  
Bijan Nouri ◽  
Marc Röger ◽  
Nicole Janotte ◽  
Christoph Hilgert
Keyword(s):  
Fluid Flow ◽  
Turbulent Flows ◽  
Power Plants ◽  
Measurement Data ◽  
Temperature Measurements ◽  
Correction Function ◽  
Acceptance Testing ◽  
Fluid Temperature ◽  
Parabolic Trough ◽  
Operation Conditions

A clamp-on measurement system for flexible and accurate fluid temperature measurements for turbulent flows with Reynolds numbers higher than 30,000 is presented in this paper. This noninvasive system can be deployed without interference with the fluid flow while delivering the high accuracies necessary for performance and acceptance testing for power plants in terms of measurement accuracy and position. The system is experimentally validated in the fluid flow of a solar thermal parabolic trough collector test bench, equipped with built-in sensors as reference. Its applicability under industrial conditions is demonstrated at the 50 MWel AndaSol-3 parabolic trough solar power plant in Spain. A function based on large experimental data correcting the temperature gradient between the measured clamp-on sensor and actual fluid temperature is developed, achieving an uncertainty below ±0.7 K (2σ) for fluid temperatures up to 400 °C. In addition, the experimental results are used to validate a numerical model. Based on the results of this model, a general dimensionless correction function for a wider range of application scenarios is derived. The clamp-on system, together with the dimensionless correction function, supports numerous combinations of fluids, pipe materials, insulations, geometries, and operation conditions and should be useful in a variety of industrial applications of the power and chemical industry where temporal noninvasive fluid temperature measurement is needed with good accuracy. The comparison of the general dimensionless correction function with measurement data indicates a measurement uncertainty below 1 K (2σ).

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