Most rolling element bearing (REB) fault diagnosis algorithms are evaluated on the Case Western Reserve University (CWRU) bearing dataset for its popularity and simplicity. However, the diagnosis accuracy on CWRU bearing dataset is overly saturated; it is nearly up to 100%. In this study, an input feature mappings (IFMs)-based deep residual network (ResNet) is proposed to conduct detailed and comprehensive fault diagnosis on REB with complicated bearing dataset. Firstly, a new data preprocessing method named as a signal-to-IFMs method is proposed to automatically extract features from raw signals without predefined parameters. Then, a deep ResNet is used as the fault classifier to learn the discriminative features from IFMs and identify the faults of REB. Finally, the proposed model is evaluated on the artificial, real, and mixed damages of the Paderborn university bearing dataset. The proposed method yields the average testing accuracies of 99.7%, 99.7%, and 99.81% in artificial, real, and mixed bearing damages, which outperforms other methods. INDEX TERMS Rolling element bearing, fault diagnosis, signal-to-input feature mappings, deep residual networks.
The fuel oil supply system of the marine diesel engine contains many components, which fits plenty of sensors to monitor the condition of all components. A fault sample consists of data collected from all the sensors at certain time, which lead the dimension of the fault sample is very high. When the ship is sailing, there is a randomness in fault categories and fault duration, which leads the fault data unbalanced. This paper proposes an appropriate combinational approach to address the above problems. First, to reduce computational complexity, the high dimensional fault samples are converted into the low dimensional ones using the principal component analysis (PCA). Second, a sample size optimization (SSO) strategy is proposed to address the problem of the learning from the imbalanced datasets, which improve the classification performance of support vector machine (SVM). Third, a three-dimensional Arnold mapping is introduced into the particle swarm optimization (PSO) algorithm to improve its generalization capability. Finally, the SVM optimized by the improved PSO is trained as the classifier to identify the ten faults in the fuel oil supply system. Results demonstrate that the average correct diagnosis ratio can be as high as 93.9%.
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