公开(公告)号：US08396689B2

公开(公告)日：2013-03-12

申请号：US12739782

申请日：2008-09-19

申请人： Uwe Pfeifer ,  Volkmar Sterzing

发明人： Uwe Pfeifer ,  Volkmar Sterzing

IPC分类号： G06F19/00

CPC分类号： G05B23/024

摘要： A method for analyzing the operation of a gas turbine is provided. A neural network based upon a normal operation of the gas turbine is learned. A dynamic pressure signal is read by a pressure sensor in or on the compressor of the turbine, and an operating parameter is read by a further sensor. The dynamic pressure signal is subjected to a frequency analysis, a parameter of a frequency spectrum of the pressure signal being determined. Based upon the measured operating parameter and the parameter of the frequency spectrum of the pressure signal, the neural network is learned. The measured operating parameter and the parameter of the frequency spectrum are input parameters, and a diagnostic characteristic value representing a probability of a presence of normal operation of the gas turbine as a function of the input parameters is output.

摘要翻译： 提供了一种用于分析燃气轮机的操作的方法。 了解到基于燃气轮机正常运行的神经网络。 动态压力信号由涡轮压缩机中或压缩机上的压力传感器读取，并且另一传感器读取操作参数。 对动态压力信号进行频率分析，确定压力信号的频谱的参数。 基于测量的操作参数和压力信号的频谱参数，学习神经网络。 测量的操作参数和频谱的参数是输入参数，并且输出表示作为输入参数的函数的燃气轮机的正常操作的概率的诊断特征值。

公开(公告)号：US20100262401A1

公开(公告)日：2010-10-14

申请号：US12739782

申请日：2008-09-19

申请人： Uwe Pfeifer ,  Volkmar Sterzing

发明人： Uwe Pfeifer ,  Volkmar Sterzing

IPC分类号： G01M15/14 ,  G06F15/00 ,  G06N3/08

CPC分类号： G05B23/024

摘要： A method for analyzing the operation of a gas turbine is provided. A neural network based upon a normal operation of the gas turbine is learned. A dynamic pressure signal is read by a pressure sensor in or on the compressor of the turbine, and an operating parameter is read by a further sensor. The dynamic pressure signal is subjected to a frequency analysis, a parameter of a frequency spectrum of the pressure signal being determined. Based upon the measured operating parameter and the parameter of the frequency spectrum of the pressure signal, the neural network is learned. The measured operating parameter and the parameter of the frequency spectrum are input parameters, and a diagnostic characteristic value representing a probability of a presence of operation of the gas turbine as a function of the input parameters is output.

摘要翻译： 提供了一种用于分析燃气轮机的操作的方法。 了解到基于燃气轮机正常运行的神经网络。 动态压力信号由涡轮压缩机中或压缩机上的压力传感器读取，并且另一传感器读取操作参数。 对动态压力信号进行频率分析，确定压力信号的频谱的参数。 基于测量的操作参数和压力信号的频谱参数，学习神经网络。 测量的操作参数和频谱的参数是输入参数，并且输出表示作为输入参数的函数的燃气轮机的存在概率的诊断特征值。

公开(公告)号：US20100241243A1

公开(公告)日：2010-09-23

申请号：US12740500

申请日：2008-09-29

申请人： Alexander Hans ,  Daniel Schneegass ,  Anton Maximilian Schäfer ,  Volkmar Sterzing ,  Steffen Udluft

发明人： Alexander Hans ,  Daniel Schneegass ,  Anton Maximilian Schäfer ,  Volkmar Sterzing ,  Steffen Udluft

IPC分类号： G05B13/02 ,  G06F15/18 ,  G05B9/02

CPC分类号： G05B13/026 ,  F23N2023/40 ,  F23N2041/20 ,  G05B13/027 ,  G06N99/005

摘要： A method for the computer-assisted exploration of states of a technical system is provided. The states of the technical system are run by carrying out an action in a respective state of the technical system, the action leading to a new state. A safety function and a feedback rule are used to ensure that a large volume of data of states and actions is run during exploration and that at the same time no inadmissible actions occur which could lead directly or indirectly to the technical system being damaged or to a defective operating state. The method allows a large number of states and actions relating to the technical system to be collected and may be used for any technical system, especially the exploration of states in a gas turbine. The method may be used both in the real operation and during simulation of the operation of a technical system.

摘要翻译： 提供了一种用于计算机辅助探索技术系统状态的方法。 技术系统的状态是通过在技术系统的各自的状态下执行动作，导致新的状态的动作。 使用安全功能和反馈规则来确保在勘探期间运行大量的状态和动作数据，并且同时不会发生不可接受的行为，这些行为可能直接或间接地导致技术系统被损坏或者导致 操作状态不良 该方法允许收集与技术系统相关的大量状态和动作，并且可以用于任何技术系统，特别是对燃气轮机状态的探索。 该方法可以在实际操作中和在模拟技术系统的操作期间使用。

公开(公告)号：US20150019276A1

公开(公告)日：2015-01-15

申请号：US14367914

申请日：2012-12-19

申请人： Per Egedal ,  Ralph Grothmann ,  Thomas Runkler ,  Volkmar Sterzing

发明人： Per Egedal ,  Ralph Grothmann ,  Thomas Runkler ,  Volkmar Sterzing

IPC分类号： G01R21/133 ,  G06N7/00 ,  G06N3/02

CPC分类号： G01R21/1333 ,  G06N3/02 ,  G06N3/0445 ,  G06N3/0454 ,  G06N7/005 ,  G06Q50/06 ,  H02J3/383 ,  H02J3/386 ,  H02J2003/003 ,  Y02E10/563 ,  Y02E10/763

摘要： A method for computer-assisted determination of usage of electrical energy produced by a power generation plant such as a renewable power generation plant is provided. The method uses a plurality of neural networks having a different structure or being learned differently for calculating future energy amounts produced by a power generation plant. To do so, the energy outputs of the power generation plant forecasted by the plurality of the neural networks are used to build histograms. Based on the histograms, energy amounts for different confidence levels describing the likelihood of the availability of the energy amount are determined, and different uses are assigned to different energy amounts. Energy amounts having a higher likelihood of availability in the future are sold at higher prices than other energy amounts.

摘要翻译： 提供了一种用于计算机辅助确定由可再生能源发电厂等发电厂生产的电能的使用的方法。 该方法使用具有不同结构或被不同学习的多个神经网络来计算由发电厂产生的未来能量。 为此，使用由多个神经网络预测的发电厂的能量输出来构建直方图。 基于直方图，确定描述能量可用性的可能性的不同置信水平的能量量，并且将不同的用途分配给不同的能量。 将来可能性较高的能源以比其他能源量更高的价格出售。

公开(公告)号：US20130013543A1

公开(公告)日：2013-01-10

申请号：US13583057

申请日：2011-02-15

申请人： Siegmund Düll ,  Volkmar Sterzing ,  Steffen Udluft

发明人： Siegmund Düll ,  Volkmar Sterzing ,  Steffen Udluft

IPC分类号： G06N3/08

CPC分类号： G05B13/027

摘要： A method for the computer-aided control of a technical system is provided. A recurrent neuronal network is used for modeling the dynamic behaviour of the technical system, the input layer of which contains states of the technical system and actions carried out on the technical system, which are supplied to a recurrent hidden layer. The output layer of the recurrent neuronal network is represented by an evaluation signal which reproduces the dynamics of technical system. The hidden states generated using the recurrent neural network are used to control the technical system on the basis of a learning and/or optimization method.

摘要翻译： 提供了一种用于技术系统的计算机辅助控制的方法。 复发神经元网络用于对技术系统的动态行为进行建模，其技术系统的输入层包含技术系统的状态和在技术系统上执行的动作，并提供给复发隐藏层。 复发神经元网络的输出层由再现动态技术系统的评估信号表示。 使用循环神经网络产生的隐藏状态用于在学习和/或优化方法的基础上控制技术系统。

公开(公告)号：US07945523B2

公开(公告)日：2011-05-17

申请号：US10574171

申请日：2004-09-27

申请人： Reimar Hofmann ,  Volkmar Sterzing

发明人： Reimar Hofmann ,  Volkmar Sterzing

IPC分类号： G06F15/18

CPC分类号： G05B17/02

摘要： The invention relates to sensitivity analysis of variables influencing a combustion process. A trainable, statistical model is trained in such a way that it describes the combustion process in the combustion chamber. The trained statistical model is used to determine the influence of the variables on said combustion process in the combustion chamber.

摘要翻译： 本发明涉及影响燃烧过程的变量的灵敏度分析。 可训练的统计模型以描述燃烧室中的燃烧过程的方式进行训练。 训练后的统计模型用于确定变量对燃烧室内燃烧过程的影响。

公开(公告)号：US20090271344A1

公开(公告)日：2009-10-29

申请号：US12386639

申请日：2009-04-21

申请人： Anton Maximillian Schafer ,  Volkmar Sterzing ,  Steffen Udluft

发明人： Anton Maximillian Schafer ,  Volkmar Sterzing ,  Steffen Udluft

IPC分类号： G06N3/08

CPC分类号： G05B13/027

摘要： A method for computer-aided control of any technical system is provided. The method includes two steps, the learning of the dynamic with historical data based on a recurrent neural network and a subsequent learning of an optimal regulation by coupling the recurrent neural network to a further neural network. The recurrent neural network has a hidden layer comprising a first and a second hidden state at a respective time point. The first hidden state is coupled to the second hidden state using a matrix to be learned. This allows a bottleneck structure to be created, in that the dimension of the first hidden state is smaller than the dimension of the second hidden state or vice versa. The autonomous dynamic is taken into account during the learning of the network, thereby improving the approximation capacity of the network. The technical system includes a gas turbine.

摘要翻译： 提供了一种用于任何技术系统的计算机辅助控制的方法。 该方法包括两个步骤：基于循环神经网络学习具有历史数据的动态，以及通过将再循环神经网络耦合到另一神经网络来进一步学习最佳调节。 循环神经网络具有在相应时间点包括第一和第二隐藏状态的隐藏层。 使用要学习的矩阵将第一隐藏状态耦合到第二隐藏状态。 这允许创建瓶颈结构，因为第一隐藏状态的维度小于第二隐藏状态的维度，反之亦然。 在网络学习期间考虑自主动态，从而提高网络的近似能力。 技术系统包括燃气轮机。

公开(公告)号：US06471581B1

公开(公告)日：2002-10-29

申请号：US09677838

申请日：2000-10-02

申请人： Werner Hauptmann ,  Tilmann Seubert ,  Volkmar Sterzing ,  Willfried Wienholt

发明人： Werner Hauptmann ,  Tilmann Seubert ,  Volkmar Sterzing ,  Willfried Wienholt

IPC分类号： B60S154

CPC分类号： B60H1/00735 ,  B60H1/247

摘要： A method and an apparatus for ventilating at least one window in a vehicle by blowing and extracting air in a region of the window is described. The airflow blown against the window is substantially laminar and the air is extracted in an upper region of the window. A dew point is lower than the window temperature in a region of extraction being the upper region of the window, and an absolute humidity of the blown air is lower than that of the extracted air.

摘要翻译： 描述了通过在窗口的区域中吹送和抽取空气来使车辆中的至少一个窗口通风的方法和装置。 吹向窗户的气流基本上是层流的，空气在窗口的上部区域被抽出。 露点低于窗口上部区域的抽出区域的窗口温度，吹出空气的绝对湿度低于抽出空气的绝对湿度。

公开(公告)号：US10352973B2

公开(公告)日：2019-07-16

申请号：US14367914

申请日：2012-12-19

申请人： Per Egedal ,  Ralph Grothmann ,  Thomas Runkler ,  Volkmar Sterzing

发明人： Per Egedal ,  Ralph Grothmann ,  Thomas Runkler ,  Volkmar Sterzing

IPC分类号： G06N3/02 ,  G06Q50/06 ,  G01R21/133 ,  G06N3/04 ,  H02J3/38 ,  G06N7/00 ,  H02J3/00

摘要： A method for computer-assisted determination of usage of electrical energy produced by a power generation plant such as a renewable power generation plant is provided. The method uses a plurality of neural networks having a different structure or being learned differently for calculating future energy amounts produced by a power generation plant. To do so, the energy outputs of the power generation plant forecasted by the plurality of the neural networks are used to build histograms. Based on the histograms, energy amounts for different confidence levels describing the likelihood of the availability of the energy amount are determined, and different uses are assigned to different energy amounts. Energy amounts having a higher likelihood of availability in the future are sold at higher prices than other energy amounts.

公开(公告)号：US20140100703A1

公开(公告)日：2014-04-10

申请号：US14123401

申请日：2012-06-01

申请人： Siegmund Düll ,  Alexander Hentschel ,  Volkmar Sterzing ,  Steffen Udluft

发明人： Siegmund Düll ,  Alexander Hentschel ,  Volkmar Sterzing ,  Steffen Udluft

IPC分类号： G06N99/00 ,  G05B13/04

CPC分类号： G06N99/005 ,  F03D7/046 ,  F03D17/00 ,  G05B13/04 ,  G05B17/02 ,  G05B23/024 ,  G06N3/0481 ,  Y02E10/723

摘要： A method for the computer-supported generation of a data-driven model of a technical system, in particular of a gas turbine or wind turbine, based on training data is disclosed. The data-driven model is preferably learned in regions of training data having a low data density. According to the invention, it is thus ensured that the data-driven model is generated for information-relevant regions of the training data. The data-driven model generated is used in a particularly preferred embodiment for calculating a suitable control and/or regulation model or monitoring model for the technical system. By determining optimization criteria, such as low pollutant emissions or low combustion dynamics of a gas turbine, the service life of the technical system in operation can be extended. The data model generated by the method according to the invention can furthermore be determined quickly and using low computing resources, since not all training data is used for learning the data-driven model.

摘要翻译： 公开了一种基于训练数据计算机支持生成技术系统，特别是燃气轮机或风力涡轮机的数据驱动模型的方法。 优选地，在具有低数据密度的训练数据的区域中学习数据驱动模型。 根据本发明，确保为训练数据的信息相关区域生成数据驱动模型。 在特别优选的实施例中使用产生的数据驱动模型来计算技术系统的合适的控制和/或调节模型或监控模型。 通过确定燃气轮机低污染物排放或低燃烧动力学等优化标准，可以延长运行中的技术系统的使用寿命。 由于不是所有的训练数据都用于学习数据驱动的模型，因此可以快速确定根据本发明的方法生成的数据模型并使用低计算资源。