Rolling Bearing Fault Diagnosis Algorithm Based on FMCNN-Sparse Representation

• Published in: IEEE access Vol. 7; pp. 102249 - 102263
• Main Author: An, Feng-Ping
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Basis functions; Convolution; Convolutional neural networks; Deep learning; Dimensional analysis; Fault diagnosis; Fault location; Feature extraction; Feedback; feedback mechanism; gradient descent; Machine learning; Optimization; Pattern recognition; Roller bearings; rolling bearing; Rolling bearings; sparse representation; Time-frequency analysis; Vibration analysis; Vibrations; Wavelet transforms
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2931616

The time-frequency analysis of vibration signals is an effective means to analyze the fault characteristics of rolling bearings. The traditional pattern recognition method is difficult to adapt to the complex mapping relationship between the high-dimensional feature space and the state space. The deep learning method has high-dimensional feature adaptive analysis ability, which is suitable for the intelligent analysis of the high-dimensional feature space in fault states. The feedforward deep convolutional neural network (CNN) has achieved some success in mechanical fault diagnosis. However, the rolling bearing fault signal is complex, and there are many interference factors. The CNN relying on the simple feedforward method cannot effectively meet the actual needs in the field of fault diagnosis. Although there are some CNNs with feedback methods, the CNNs of these feedback methods cannot systematically obtain the characteristic information of rolling bearing faults. Therefore, they do not solve the feature extraction problem of rolling bearing faults well. In view of this, this paper provides a specific mathematical definition of the feedback mechanism for constructing the feedback mechanism in the deep CNN, models the feedback mechanism into an optimization problem, determines the basic framework of the feedback mechanism, and an effective feedback mechanism calculation model is proposed. Based on this, a solution algorithm based on the gradient descent method is proposed. Then, an effective supervised feature extraction method based on sparse expression is proposed. It maps the sample features to the feature domain through the effective transform method. In the process, the wavelet packet transform (WPT) transform is used as the basis function to construct a dictionary with structural effects, and mixed penalty terms are introduced to further optimize the performance of structural sparse expression. Finally, the sparse expression is combined with the feedback mechanism CNN (FCNN) to establish a sub-module fault diagnosis network so that a diagnosis can determine the fault severity while assessing the bearing fault location. The example shows that the method proposed in this paper has high accuracy in determining the state of rolling bearings and has great application potential in engineering.