![]() However, some faults have to be considered because those faults can reduce the bearing life and damage the shaft. The magnetic bearing is effectively employed in mechanical applications to run the device or particular applications. This study proposes an effective model based on multi–output classification, which can be trained using only single fault data for the classification of compound faults and confirms the robustness of the model to changes in unbalance. Finally, the proposed model was evaluated based on its performance and robustness for classification of compound faults. Then, a model was built for classification of the state of the system based on multi–output classification. In this study, input data were first preprocessed with short–time Fourier transform. Diagnosis of untrained compound faults is also a merit. It is valuable to focus on the capability of detecting compound faults because multiple faults can exist simultaneously. faults into different single types, whereas multi–label classification classifies faults into compound types. Multi–class classification is used to classify. Two important elements of fault diagnosis using machine learning are data preprocessing and model structure. The advancements in machine learning and deep learning have led to enhanced performance of classification. The effectiveness of the proposed method has been investigated and demonstrated for roller bearing contact fatigue experiments, and the results show that IEPF can timely and accurately detect the incipient sub-surface fault.įault diagnosis is important in rotor systems because severe damage can occur during the operation of systems under harsh conditions. We propose a hybrid parameter - Information Entropy Penalty Factor (IEPF), which uses the advantages of the Entropy theory and Deep learning methods. In this work, the AE technique is applied to monitor a run-to-failure process of a roller bearing, and the use of multiple known parameters, such as RMS, skewness, crest factor, impulse factor etc., fails to characterise the evolution of the acquired AE signals, thus highlighting the long-standing necessity and significance to develop new AE indicators that are more adequate to failure of rotating machines. The parameter-based analysis remains to be the most widely used approach to interpret the AE waveforms partly due to the challenges arising in the processing of a large amount of streaming data. ![]() The acoustic emission (AE) technique offers the advantage of earlier detection of defects and failures of bearings in comparison to traditional vibration techniques. Bearing is a crucial component of wind turbines.
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