This paper presents a deep learning approach for detecting early fault in bearings. The identification of bearings defects represents an important problem in the field of rotating machines. Sudden failures may occur, leading to breakdown of the machinery. For this reason, the prediction of possible faults has become a major issue in the study of bearing elements. Different fault diagnosis techniques have been developed during the years based on aggregated parameters (i.e. features) that are computed starting from time domain, frequency domain or time frequency domain analysis, relying on prior knowledge about signal processing. These approaches present major limitations, that can be overcome by adopting a convolutional LSTM (long short-term memory) neural network model. In this case, a more complex architecture is built, and the algorithm can identify effective features from accelerometer signal, that could not be considered by a manual computation approach. The algorithm has been applied on data obtained from a complex test rig to assess bearings failure on high speed trains. The outcome of this work indicates that the adopted approach leads to satisfactory performances.
This paper proposes a method for optimal sensor placing for experimental modal analysis purposes. While many methods for optimal placing can be found in the literature, the one proposed here focuses on the use of continuous optical fiber sensors, such as those based on the Optical Backscatter Reflectometer (OBR) technology. The main difference with respect to classical sensors is that all the measurement points are placed on the same wire (the fiber) and so they cannot be placed independently on the structure. Moreover, OBR sensors measure strain, which must be converted into displacement to obtain the structure modal shapes. The paper will present the optimal method in detail, showing also numerical and experimental results on a plate, demonstrating its effectiveness.
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