Intelligent Bearing Fault Diagnosis with Convolutional Long-Short-Term-Memory Recurrent Neural Network

Fault diagnosis is an important topic both in practice and research. There is an intense pressure on industrial plants to continue reducing unscheduled downtime, performance degradation, and safety hazards, which requires detecting and recovering of potential fault in its early stages. Intelligent fault diagnosis is a promising tool due to its ability in rapidly and efficiently processing collected signals and providing accurate diagnosis results. In the literature, although many studies have developed algorithms for detecting bearing fault, the results have generally been limited to relatively small train/test datasets and the input data has been manipulated (selective features used) in order to reach a high accuracy. In the following paper a Convolutional Long-Short-Term-Memory Recurrent Neural Network (CRNN) is proposed for intelligent fault diagnosis of bearings. The purpose is to introduce an algorithm which takes directly the raw time-series sensor data as the input and detects the health condition of the bearing as the output with a high accuracy and in a short period of time. The method can reach the highest accuracy to the best knowledge of author of the present paper voiding any sort of pre-processing or manipulation of the input data. The paper starts with a brief description of the new approach of diagnosis using a CRNN network, which the author plans to develop and implement for diagnosing bearing faults and concludes with the identification of the most significant advantages of the proposed method as well as a comparison between the proposed method and the other methods in the literature.

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