Claus Neubauer

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We present a Bayesian framework to tackle the problem of sensor estimation, a critical step of fault diagnosis in machine condition monitoring. A Gaussian mixture model is employed to model the normal operating range of the machine. A Gaussian random vector is introduced to model the possible deviations of the observed sensor values from their corresponding(More)
This paper has three contributions to the fields of power plant monitoring. First, we differentiate out-of-range detection from fault detection. An out-of-range refers to a normal operating range of a power plant unseen in the training data. In the case of an out-of-range, instead of producing a fault alarm, the system should notify the operator to include(More)
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Continuously monitoring or even real-time forecasting the performance of civil infrastructures based on gathered sensing information offers a tremendous opportunity to increase safety by detecting and localizing damage before it reaches a critical level. Such monitoring and analysis help reduce costs by shifting from current " run-to-failure " or preventive(More)
We propose a dynamic Bayesian framework for sensor estimation, a critical step of many machine condition monitoring systems. The temporal behavior of normal sensor data is described by a stationary switching autoregressive (SSAR) model that possesses two advantages over traditional switching autoregressive (SAR) models. First, the SSAR model removes time(More)
Classification of corrupted images, for example due to occlusion or noise, is a challenging problem. Most existing methods tackled this problem using a two-step strategy: image reconstruction and classification of reconstructed images. However, their performances heavily relied on the accuracy of reconstruction and parameter estimation. We present a full(More)
We present a robust method to identify and isolate faulty sensors among a set of correlated sensors. For each sensor, we estimate the sensor a number of times, using each of the other correlated sensors separately. We use the median of these estimates as the estimate for the sensor. When up to less than half of the sensors are faulty, this method identifies(More)