Siemens Aktiengesellschaft

Siemens Aktiengesell Schaft

Holger Hackstein

Chao Yuan

Amit Chakraborty

Leif Wiebking

Andreas Ziroff

Florian Poprawa

Christian Morhart

Claus Neubauer

Erhard Magori

Gerit Ebelsberger

Alexander Michael Gigler

Kerstin Wiesner

Dominikus Joachim Müller

Remigiusz Pastusiak

Dominik Zoeke

Eberhard Ritzhaupt-Kleissl

Hans Weber

Bernhard Glomann

Dominik Marco Zoeke

A method for monitoring a condition of a system or process includes acquiring sensor data from a plurality of sensors disposed within the system (Sand S). The acquired sensor data is streamed in real-time to a computer system (Sand S). A discriminative framework is applied to the streaming sensor data using the computer system (Sand S). The discriminative framework provides a probability value representing a probability that the sensor data is indicative of an anomaly within the system. The discriminative framework is an integration of a Kalman filter with a logistical function (S).

Discriminative hidden kalman filters for classification of streaming sensor data in condition monitoring

A method of predicting an amount of power that will be generated by a solar power plant at a future time includes: forecasting a value of a data variable at the future time that is likely to affect the ability of the solar power plant to produce electricity (S); computing a plurality of features from prior observed amounts of power generated by the power plant during different previous durations (S); determining a trending model from the computed features and the forecasted value (S); and predicting the amount of power that will be generated by the power plant at the future time from the determined model (S).

Combining multiple trending models for photovoltaics plant output forecasting

A thermal power plant, in particular to a steam-electric power plant or a combined cycle power plant (CCPP), and a method for operating a thermal power plant is adapted to accelerate, or to technically and/or economically optimize the start-up of the thermal power plant, in particular to accelerate/optimize a cold-start phase of the thermal power station. The thermal power plant has an auxiliary energy store integrated into the power plant. The store, during the start-up of the thermal power plant, delivers energy for heating/pre-heating components and/or media of the thermal power plant, or supplies an electrical power distribution network.

Optimization of cold starts in thermal power stations, in particular in steam-electric power plants or in combined cycle power plants (CCPPS)

Detection for localizing at least one particle moving in a flow includes emitting a transmission signal by a transmitter, and receiving a reflected reception signal. The reflected reception signal is frequency and phase modulated in comparison with the transmission signal. The reflected reception signal is convolved with at least one kernel representative of a conjugate estimated channel pulse response. A reconstructed particle position function is formed, and the position of the particle is determined from the reconstructed particle position function.

Detection for localizing a particle

A method for solar forecasting includes receiving a plurality of solar energy data as a function of time of day at a first time, forecasting from the solar energy data a mode, where the mode is a sunny day, a cloudy day, or an overcast day, and the forecast predicts the mode for a next solar energy datum, receiving the next solar energy datum, updating a probability distribution function (pdf) of the next solar energy datum given the mode, updating a pdf of the mode for the next solar energy datum from the updated pdf of the new solar energy datum given the mode, forecasting a plurality of future unobserved solar energy data from the updated pdf of the mode, where the plurality of future unobserved solar energy data and the plurality of solar energy data have a Gaussian distribution for a given mode determined from training data.

Solar power forecasting using mixture of probabilistic principal component analyzers

A method for predicting failure modes in a machine includes learning () a multivariate Gaussian distribution for each of a source machine and a target machine from data samples from one or more independent sensors of the source machine and the target machine, learning () a multivariate Gaussian conditional distribution for each of the source machine and the target machine from data samples from one or more dependent sensors of the source machine and the target machine using the multivariate Gaussian distribution for the independent sensors, transforming () data samples for the independent sensors from the source machine to the target machine using the multivariate Gaussian distributions for the source machine and the target machine, and transforming () data samples for the dependent sensors from the source machine to the target machine using the transformed independent sensor data samples and the conditional Gaussian distributions for the source machine and the target machine.

Transferring failure samples using conditional models for machine condition monitoring

The present embodiments relate to a device and a method for determining at least one concentration of coal particles in a gas flow flowing through a channel, wherein at least part of the gas flow having coal particles contained therein is measured by at least one microwave sensor and at least one measurement signal characterizing the concentration of the coal particles is provided, wherein an autocorrelation function of the measurement signal is determined and at least one distance value characterizing a distance of a point of the gas flow belonging to the concentration from the microwave sensor is determined in dependence on the autocorrelation function.

Method and device for determining at least one concentration of coal particles in a gas flow

A sensor apparatus for analyzing a gas in a process chamber, having a housing, a gas sensor for analyzing at least a part of the gas, the gas sensor being arranged at a determined position in the housing, a gas feed for connecting the housing to the process chamber to feed the part of the gas from the process chamber into the housing and to the determined position, and a gas discharge for discharging the gas from the housing, wherein the gas feed and the gas discharge are configured as tubes lying inside one another, characterized by a closure cap at the combustion chamber-side end of the tubes lying inside one another, the closure cap including an even number of at least four openings with the same area, which are connected alternately as a gas inlet and a gas outlet to the tubes lying inside one another is provided.

Sensor apparatus for analyzing a gas mixture in a process chamber

An optical sensor is arranged in an indentation of a dust line, the indentation being equipped with at least one gas inlet nozzle for removing the dust from the optical sensor. Dust is transported through the dust line. An optical property of the dust is measured using at least one optical sensor arranged in an indentation of the dust line, and the dust is then removed from the optical sensor by blowing in air using the at least one gas inlet nozzle arranged in the indentation.

Dust line with optical sensor, and method for measuring the composition of dust

A generalized pattern recognition is used to identify faults in machine condition monitoring. Pattern clusters are identified in operating data. A classifier is trained using the pattern clusters in addition to annotated training data. The operating data is also used to cluster the signals in the operating data into signal clusters. Monitored data samples are then classified by evaluating confidence vectors that include substitutions of signals contained in the training data by signals in the same signal clusters as the signals contained in the training data.

Generalized pattern recognition for fault diagnosis in machine condition monitoring

A machine fault diagnosis system is provided. The system combines a rule-based predictive maintenance strategy with a machine learning system. A simple set of rules defined manually by human experts is used to generate artificial training feature vectors to portray machine fault conditions for which only a few real data points are available. Those artificial training feature vectors are combined with real training feature vectors and the combined set is used to train a supervised pattern recognition algorithm such as support vector machines. The resulting decision boundary closely approximates the underlying real separation boundary between the fault and normal conditions.

Supervised fault learning using rule-generated samples for machine condition monitoring

A method for monitoring operation behavior of a component of an industrial plant is provided. Parameter identifiers of operation parameters of the industrial plant are read from an element of a control system of the plant. Some of these parameter identifiers are selected and the selected parameter identifiers are forwarded to an element of the control system. Further, a time of a specified operation of the component is defined and behavior of the selected parameters of the component in the defined time is used as guide model for monitoring selected parameters of the component during component operation.

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Dietzenbach, Germany

Holger Hackstein