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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)
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)
Convolutional neural networks provide an eecient method to constrain the complexity of feedforward neural networks by weightsharing. This network topology has been applied in particular to image classiication when raw images are to be classi-ed without preprocessing. In this paper two variations of convolutional networks-Neocognitron and Neoperceptron-are(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)