The Experts below are selected from a list of 186048 Experts worldwide ranked by ideXlab platform
Ilmar Santos - One of the best experts on this subject based on the ideXlab platform.
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wind turbine condition monitoring based on scada data using Normal Behavior models part 2 application examples
Applied Soft Computing, 2014Co-Authors: Meik Schlechtingen, Ilmar SantosAbstract:This paper is part two of a two part series. The originality of part one was the proposal of a novelty approach for wind turbine supervisory control and data acquisition (SCADA) data mining for condition monitoring purposes. The novelty concerned the usage of adaptive neuro-fuzzy interference system (ANFIS) models in this context and the application of a proposed procedure to a wide range of different SCADA signals. The applicability of the set up ANFIS models for anomaly detection was proven by the achieved performance of the models. In combination with the fuzzy interference system (FIS) proposed the prediction errors provide information about the condition of the monitored components. Part two presents application examples illustrating the efficiency of the proposed method. The work is based on continuously measured wind turbine SCADA data from 18 modern type pitch regulated wind turbines of the 2 MW class covering a period of 35 months. Several real life faults and issues in this data are analyzed and evaluated by the condition monitoring system (CMS) and the results presented. It is shown that SCADA data contain crucial information for wind turbine operators worth extracting. Using full signal reconstruction (FSRC) adaptive neuro-fuzzy interference system (ANFIS) Normal Behavior models (NBM) in combination with fuzzy logic (FL) a setup is developed for data mining of this information. A high degree of automation can be achieved. It is shown that FL rules established with a fault at one turbine can be applied to diagnose similar faults at other turbines automatically via the CMS proposed. A further focus in this paper lies in the process of rule optimization and adoption, allowing the expert to implement the gained knowledge in fault analysis. The fault types diagnosed here are: (1) a hydraulic oil leakage; (2) cooling system filter obstructions; (3) converter fan malfunctions; (4) anemometer offsets and (5) turbine controller malfunctions. Moreover, the graphical user interface (GUI) developed to access, analyze and visualize the data and results is presented.
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wind turbine condition monitoring based on scada data using Normal Behavior models part 1 system description
Applied Soft Computing, 2013Co-Authors: Meik Schlechtingen, Ilmar Santos, Sofiane AchicheAbstract:This paper proposes a system for wind turbine condition monitoring using Adaptive Neuro-Fuzzy Interference Systems (ANFIS). For this purpose: (1) ANFIS Normal Behavior models for common Supervisory Control And Data Acquisition (SCADA) data are developed in order to detect abNormal Behavior of the captured signals and indicate component malfunctions or faults using the prediction error. 33 different standard SCADA signals are used and described, for which 45 Normal Behavior models are developed. The performance of these models is evaluated in terms of the prediction error standard deviations to show the applicability of ANFIS models for monitoring wind turbine SCADA signals. The computational time needed for model training is compared to Neural Network (NN) models showing the strength of ANFIS in training speed. (2) For automation of fault diagnosis Fuzzy Interference Systems (FIS) are used to analyze the prediction errors for fault patterns. The outputs are both the condition of the component and a possible root cause for the anomaly. The output is generated by the aid of rules that capture the existing expert knowledge linking observed prediction error patterns to specific faults. The work is based on continuously measured wind turbine SCADA data from 18 turbines of the 2 MW class covering a period of 30 months. The system proposed in this paper shows a novelty approach with regard to the usage of ANFIS models in this context and the application of the proposed procedure to a wide range of SCADA signals. The applicability of the set up ANFIS models for anomaly detection is proved by the achieved performance of the models. In combination with the FIS the prediction errors can provide information about the condition of the monitored components. In this paper the condition monitoring system is described. Part two will entirely focus on application examples and further efficiency evaluation of the system.
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comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection
Mechanical Systems and Signal Processing, 2011Co-Authors: Meik Schlechtingen, Ilmar SantosAbstract:This paper presents the research results of a comparison of three different model based approaches for wind turbine fault detection in online SCADA data, by applying developed models to five real measured faults and anomalies. The regression based model as the simplest approach to build a Normal Behavior model is compared to two artificial neural network based approaches, which are a full signal reconstruction and an autoregressive Normal Behavior model. Based on a real time series containing two generator bearing damages the capabilities of identifying the incipient fault prior to the actual failure are investigated. The period after the first bearing damage is used to develop the three Normal Behavior models. The developed or trained models are used to investigate how the second damage manifests in the prediction error. Furthermore the full signal reconstruction and the autoregressive approach are applied to further real time series containing gearbox bearing damages and stator temperature anomalies. The comparison revealed all three models being capable of detecting incipient faults. However, they differ in the effort required for model development and the remaining operational time after first indication of damage. The general nonlinear neural network approaches outperform the regression model. The remaining seasonality in the regression model prediction error makes it difficult to detect abNormality and leads to increased alarm levels and thus a shorter remaining operational period. For the bearing damages and the stator anomalies under investigation the full signal reconstruction neural network gave the best fault visibility and thus led to the highest confidence level.
Shannon T. Marko - One of the best experts on this subject based on the ideXlab platform.
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a fixed moderate dose combination of tiletamine zolazepam outperforms midazolam in induction of short term immobilization of ball pythons python regius
PLOS ONE, 2018Co-Authors: Lynn J. Miller, Sean A Van Tongeren, Ginger Donnelly, David P. Fetterer, Nicole L. Garza, Matthew G. Lackemeyer, Jesse T. Steffens, Jimmy O. Fiallos, Joshua L. Moore, Shannon T. MarkoAbstract:Laboratory animals are commonly anesthetized to prevent pain and distress and to provide safe handling. Anesthesia procedures are well-developed for common laboratory mammals, but not as well established in reptiles. We assessed the performance of intramuscularly injected tiletamine (dissociative anesthetic) and zolazepam (benzodiazepine sedative) in fixed combination (2 mg/kg and 3 mg/kg) in comparison to 2 mg/kg of midazolam (benzodiazepine sedative) in ball pythons (Python regius). We measured heart and respiratory rates and quantified induction parameters (i.e., time to loss of righting reflex, time to loss of withdrawal reflex) and recovery parameters (i.e., time to regain righting reflex, withdrawal reflex, Normal Behavior). Mild decreases in heart and respiratory rates (median decrease of <10 beats per minute and <5 breaths per minute) were observed for most time points among all three anesthetic dose groups. No statistically significant difference between the median time to loss of righting reflex was observed among animals of any group (p = 0.783). However, the withdrawal reflex was lost in all snakes receiving 3mg/kg of tiletamine+zolazepam but not in all animals of the other two groups (p = 0.0004). In addition, the time for animals to regain the righting reflex and resume Normal Behavior was longer in the drug combination dose groups compared to the midazolam group (p = 0.0055). Our results indicate that midazolam is an adequate sedative for ball pythons but does not suffice to achieve reliable immobilization or anesthesia, whereas tiletamine+zolazepam achieves short-term anesthesia in a dose-dependent manner.
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Evaluation of the depth of anesthesia in ball pythons following injection of midazolam or a fixed combination of tiletamine+zolazepam.
2018Co-Authors: Lynn J. Miller, David P. Fetterer, Nicole L. Garza, Matthew G. Lackemeyer, Ginger C. Donnelly, Jesse T. Steffens, Sean A. Van Tongeren, Jimmy O. Fiallos, Joshua L. Moore, Shannon T. MarkoAbstract:Each snake was graphed chronologically through the stages of anesthesia experienced by the animal after intramuscular injection of midazolam (2 mg/kg), tiletamine+zolazepam (2 mg/kg), or tiletamine+zolazepam (3 mg/kg). Time spent in each anesthetic stage is displayed by colored bars. The duration of anesthesia of snake H01 following administration of 3 mg/kg of tiletamine+zolazepam is denoted by an interrupted X axis. Snake H01 did not return to Normal Behavior until 1,440 min post-injection.
Meik Schlechtingen - One of the best experts on this subject based on the ideXlab platform.
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wind turbine condition monitoring based on scada data using Normal Behavior models part 2 application examples
Applied Soft Computing, 2014Co-Authors: Meik Schlechtingen, Ilmar SantosAbstract:This paper is part two of a two part series. The originality of part one was the proposal of a novelty approach for wind turbine supervisory control and data acquisition (SCADA) data mining for condition monitoring purposes. The novelty concerned the usage of adaptive neuro-fuzzy interference system (ANFIS) models in this context and the application of a proposed procedure to a wide range of different SCADA signals. The applicability of the set up ANFIS models for anomaly detection was proven by the achieved performance of the models. In combination with the fuzzy interference system (FIS) proposed the prediction errors provide information about the condition of the monitored components. Part two presents application examples illustrating the efficiency of the proposed method. The work is based on continuously measured wind turbine SCADA data from 18 modern type pitch regulated wind turbines of the 2 MW class covering a period of 35 months. Several real life faults and issues in this data are analyzed and evaluated by the condition monitoring system (CMS) and the results presented. It is shown that SCADA data contain crucial information for wind turbine operators worth extracting. Using full signal reconstruction (FSRC) adaptive neuro-fuzzy interference system (ANFIS) Normal Behavior models (NBM) in combination with fuzzy logic (FL) a setup is developed for data mining of this information. A high degree of automation can be achieved. It is shown that FL rules established with a fault at one turbine can be applied to diagnose similar faults at other turbines automatically via the CMS proposed. A further focus in this paper lies in the process of rule optimization and adoption, allowing the expert to implement the gained knowledge in fault analysis. The fault types diagnosed here are: (1) a hydraulic oil leakage; (2) cooling system filter obstructions; (3) converter fan malfunctions; (4) anemometer offsets and (5) turbine controller malfunctions. Moreover, the graphical user interface (GUI) developed to access, analyze and visualize the data and results is presented.
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wind turbine condition monitoring based on scada data using Normal Behavior models part 1 system description
Applied Soft Computing, 2013Co-Authors: Meik Schlechtingen, Ilmar Santos, Sofiane AchicheAbstract:This paper proposes a system for wind turbine condition monitoring using Adaptive Neuro-Fuzzy Interference Systems (ANFIS). For this purpose: (1) ANFIS Normal Behavior models for common Supervisory Control And Data Acquisition (SCADA) data are developed in order to detect abNormal Behavior of the captured signals and indicate component malfunctions or faults using the prediction error. 33 different standard SCADA signals are used and described, for which 45 Normal Behavior models are developed. The performance of these models is evaluated in terms of the prediction error standard deviations to show the applicability of ANFIS models for monitoring wind turbine SCADA signals. The computational time needed for model training is compared to Neural Network (NN) models showing the strength of ANFIS in training speed. (2) For automation of fault diagnosis Fuzzy Interference Systems (FIS) are used to analyze the prediction errors for fault patterns. The outputs are both the condition of the component and a possible root cause for the anomaly. The output is generated by the aid of rules that capture the existing expert knowledge linking observed prediction error patterns to specific faults. The work is based on continuously measured wind turbine SCADA data from 18 turbines of the 2 MW class covering a period of 30 months. The system proposed in this paper shows a novelty approach with regard to the usage of ANFIS models in this context and the application of the proposed procedure to a wide range of SCADA signals. The applicability of the set up ANFIS models for anomaly detection is proved by the achieved performance of the models. In combination with the FIS the prediction errors can provide information about the condition of the monitored components. In this paper the condition monitoring system is described. Part two will entirely focus on application examples and further efficiency evaluation of the system.
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comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection
Mechanical Systems and Signal Processing, 2011Co-Authors: Meik Schlechtingen, Ilmar SantosAbstract:This paper presents the research results of a comparison of three different model based approaches for wind turbine fault detection in online SCADA data, by applying developed models to five real measured faults and anomalies. The regression based model as the simplest approach to build a Normal Behavior model is compared to two artificial neural network based approaches, which are a full signal reconstruction and an autoregressive Normal Behavior model. Based on a real time series containing two generator bearing damages the capabilities of identifying the incipient fault prior to the actual failure are investigated. The period after the first bearing damage is used to develop the three Normal Behavior models. The developed or trained models are used to investigate how the second damage manifests in the prediction error. Furthermore the full signal reconstruction and the autoregressive approach are applied to further real time series containing gearbox bearing damages and stator temperature anomalies. The comparison revealed all three models being capable of detecting incipient faults. However, they differ in the effort required for model development and the remaining operational time after first indication of damage. The general nonlinear neural network approaches outperform the regression model. The remaining seasonality in the regression model prediction error makes it difficult to detect abNormality and leads to increased alarm levels and thus a shorter remaining operational period. For the bearing damages and the stator anomalies under investigation the full signal reconstruction neural network gave the best fault visibility and thus led to the highest confidence level.
Matthew Glickman - One of the best experts on this subject based on the ideXlab platform.
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coverage and generalization in an artificial immune system
Genetic and Evolutionary Computation Conference, 2002Co-Authors: Justin Balthrop, Fernando Esponda, Stephanie Forrest, Matthew GlickmanAbstract:LISYS is an artificial immune system framework which is specialized for the problem of network intrusion detection. LISYS learns to detect abNormal packets by observing Normal network traffic. Because LISYS sees only a partial sample of Normal traffic, it must generalize from its observations in order to characterize Normal Behavior correctly. A variation of the r-contiguous bits matching rule is introduced, and its effect on coverage and generalization is studied. The effect of representation diversity on coverage and generalization is also explored by studying permutations in the order of bits in the representation.
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revisiting lisys parameters and Normal Behavior
Congress on Evolutionary Computation, 2002Co-Authors: Justin Balthrop, Stephanie Forrest, Matthew GlickmanAbstract:This paper studies a simplified form of LISYS, an artificial immune system for network intrusion detection. The paper describes results based on a new, more controlled data set than that used for earlier studies. The paper also looks at which parameters appear most important for minimizing false positives, as well as the trade-offs and relationships among parameter settings.
Zou Bo - One of the best experts on this subject based on the ideXlab platform.
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modeling Normal Behavior of network traffic
Computer Engineering, 2002Co-Authors: Zou BoAbstract:With the rapid development of the network, it now has a large size and complexity, increasing various applications based on it, consequently the network management is increasingly difficult. Generally the traditional network management systems alarm in terms of a fixed threshold, however, it has a shortcoming that it cannot adapt the changes of the network, moreover, it is not an easy thing to find the appropriate threshold, so the techniques of proactive network management occurred. Therefore, a Normal Behavior model is founded with the numbers of the non-unicasting packet collected from a real network, with the Analysis of Variance (ANOVA), stabilize the Normal Behavior serial and estimate the coefficients of the ARMA model. So network anomaly Behavior may be detected with this model, the traffic of the network may be predicted, and the trend of network can be seen.
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arma based traffic prediction and overload detection of network
Journal of Computer Research and Development, 2002Co-Authors: Zou BoAbstract:With its rapid development the network now has a large size and complexity, and consequently the network management is becoming increasingly difficult. Generally the network manager begins to solve the potential problems after the monitoring system alarms, i.e., it takes a re action way, so the service on the network is possibly affected. A Normal Behavior model is founded from the numbers of the non unicasting packet collected from a real network. The Normal Behavior serial is stabilized and the coefficients of the ARMA model are estimated. And then the traffic by the way of "minimal linear square error" is predicted, and the probability of the predicted value exceeding the threshold is calculated. So the overload in the network may be predicted, and the recovery measures may be taken beforehand to prevent communication from being impacted or to reduce its severity. This method changes the traditional network management from re action to prediction beforehand, so that the network overload may be predicted.
