Siemens Corporate Research, Inc.

Siemens Energy, Inc.

Siemens Corporation

Siemens Westinghouse Power Corporation

Siemens Power Generation, Inc.

Zehra Cataltepe

Claus Neubauer

Chao Yuan

Ming Fang

Hans-Gerd Johann Brummel

Wesley McCorkle

Jie Cheng

A method and apparatus for detecting faults in power plant equipment is discloses using sensor confidence and an improved method of identifying the normal operating range of the power generation equipment as measured by those sensors. A confidence is assigned to a sensor in proportion to the residue associated with that sensor. If the sensor has high residue, a small confidence is assigned to the sensor. If a sensor has a low residue, a high confidence is assigned to that sensor, and appropriate weighting of that sensor with other sensors is provided. A feature space trajectory (FST) method is used to model the normal operating range curve distribution of power generation equipment characteristics. Such an FST method is illustratively used in conjunction with a minimum spanning tree (MST) method to identify a plurality of nodes and to then connect those with line segments that approximate a curve.

Method and apparatus for improved fault detection in power generation equipment

A method to use a receiver operator characteristics curve for model comparison in machine condition monitoring. The method and systems of using this method may be used to evaluate different monitoring models. These models may be used to monitor a variety of different systems such as power plant systems or magnetic resonance imaging systems. The methods use training data and designate one or more points in the data as a false negative, thereby permitting a receiver operator characteristics analysis to be performed. Multiple receiver operator characteristics analyses may be performed either on different models or on different points within a single model, thereby permitting the receiver operator characteristics analyses to be used to select a beneficial model for monitoring a particular system.

Method to use a receiver operator characteristics curve for model comparison in machine condition monitoring

A system for updating a plurality of monitoring models is provided. The system includes a model association module that, for each of a plurality of monitored systems determines, an association between a particular monitored system and at least one of a plurality of estimation models. Each estimation model is based upon one of a plurality of distinct sets of estimation properties, and each set uniquely corresponds to a particular estimation model. The system also includes an updating module that updates at least one of the estimation properties and propagates the updated estimation properties to each estimation model that corresponds to a distinct set containing at least one estimation property that is updated. The system further includes a model modification module that modifies each estimation model that corresponds to a distinct set containing at least one estimation property that is updated.

System, device, and methods for updating system-monitoring models

A joint approach of out-of-range detection and fault detection for power plant monitoring. The method initially determines whether a sensor is an independent sensor or a dependent sensor. If the sensor is an independent sensor, then an operating range is established for each independent sensor. A reading from each independent sensor is then compared with the operating range that has been established. If the reading is out-of-range, an alarm may be activated. If the reading is not out-of-range, then this reading is used to determine an expected operating range for each dependent sensor. A reading from each dependent sensor is then compared with the predicted operating range. Again, if the reading from the dependent sensor is out of the expected range, an alarm may be sounded.

Joint approach of out-of-range detection and fault detection for power plant monitoring

An apparatus for detecting faults in a system monitored by a plurality of sensors is provided. The apparatus includes a hidden process driver unit that generates a hidden process driver based upon sensor values received from a group of correlated sensors selected from among the plurality of sensors. The apparatus also includes a sensor estimating unit that generates sensor estimates for each of the plurality of sensors based upon the hidden process driver and a known process driver provided by an uncorrelated sensor. The apparatus further includes a fault determining unit that indicates a fault when a residual based upon a difference between a sensor value supplied by one of the plurality of sensors and a corresponding one of the sensor estimates lies outside an acceptable range of residual values.

Apparatus and methods for detecting system faults using hidden process drivers

A method and apparatus for detecting out-of-range conditions representing normal operations is disclosed. A support vector machine is used to generate an improved representation of historical training data from power generation equipment that facilitates a more accurate determination of the boundary between measurements that should be considered faults and those that represent normal operating conditions. The SVM receives data collected from a plurality of independent sensors associated with the power generating equipment in order to generate a boundary substantially separating a first class of data (e.g., a fault) from a second class of data (e.g., a normal operating condition) in a support vector machine feature space. Elements of operational data are collected and compared to the boundary generated from historical training data. A determination is then made whether the element of operational data is in a particular class, such as a class associated with out-of-range conditions.

Method and apparatus for detecting out-of-range conditions in power generation equipment operations

A tool for sensor management and fault visualization in machine condition monitoring. The method and system are able to monitor a plurality of sensors at one time. The sensors may be used in a power plant system monitoring system. The method and system may display a fault status for each sensor in the plurality of sensors in a single display, wherein the fault status for each sensor is displayed over time. The method and system also provide a mechanism that permits a user to examine details of each sensor in the plurality of sensors at any given time. In addition, the method and system are capable of categorizing each fault in the fault status using one or more properties or categorizing criteria. The method and system also permit sensors to be tested such that different operating models may be examined by utilizing different sensors.

Tool for sensor management and fault visualization in machine condition monitoring

A system for detecting one or more faulty sensors in a multi-sensor monitor includes a partitioning module for partitioning sensor values generated by the multi-sensor monitor into two distinct sets, a training set and a validation set. The system also includes a training module for training a model using the sensor values belonging to the training set and applying the model to each sensor value belonging to the validation set so as to determine a range of acceptable sensor values. The system further includes an estimating module for obtaining an estimated sensor value for each sensor using the model, and a fault-determining module for testing at least one sensor combination if a sensor value is not within its range of acceptable sensor values. A sensor combination includes at least one sensor whose estimated sensor value is not within the range of acceptable values.

System and method for detecting and excluding outlier sensors in sensor-based monitoring

A system for generating a sensor model for use in sensor-based monitoring is provided. The system includes a segmenting module for segmenting a collection of sensor vectors into a plurality of bins comprising distinct sensor vectors. The system also includes a set-generating module for generating a set of statistically significant sensor vectors for each bin. The system further includes a consistency determination module for generating at least one consistent set of sensor vectors from the sets of statistically significant sensor vectors. Additionally, the system includes a model-generating module for generating a sensor model based upon the at least one consistent set.

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Fair Haven, NJ, United States of America

Zehra Cataltepe