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Condition classification of rolling element bearings in rotating machines is important to prevent the breakdown of industrial machinery. A considerable amount of literature has been published on bearing faults classification. These studies aim to determine automatically the current status of a roller element bearing. Of these studies, methods based on compressed sensing (CS) have received some attention...
Rolling element bearings have a pivotal role in rotating machine and their failures are the leading cause of more substantial failures in the machine. In response to their importance, there is a growing body of research looking at condition monitoring of rolling element bearings to avoid machine breakdowns. In this study, by taking advantages of Compressive Sampling (CS), Laplacian Score (LS) and...
This paper presents a novel method for fault classification based on Multiple Measurement Vector Compressive Sampling (MMV-CS), Fisher Score (FS), and Support Vector Machine (SVM). In this method, the original vibration signal passes through MMV-CS framework to obtain compressed samples that possess the quality of the original vibration signals. Afterwards FS algorithm is applied to select the most...
Owing to the importance of rolling element bearings in rotating machines, condition monitoring of rolling element bearings has been studied extensively over the past decades. However, most of the existing techniques require large storage and time for signal processing. This paper presents a new strategy based on compressive sensing for bearing faults classification that uses fewer measurements. Under...
Automatic fault detection and classification for roller element bearings is an important issue for rotating machine condition monitoring. In this paper, we classify roller element bearings fault classes under two and three hidden layers' deep neural network framework based on sparse Autoencoder. This allows us to learn and extract features for the bearing vibration samples in an unsupervised manner...
The ability of automatically determining the underlying fault type in-situ for a roller element bearing is highly desired in machine condition monitoring applications nowadays. In this paper, we classify roller element fault types under a compressed sensing framework. Firstly, vibration signals of roller element bearings are acquired in the time domain and resampled with a random Bernoulli matrix...
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