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.
With the growing demand for emission reductions and fuel efficiency improvements, alternative energy sources and energy storage technologies are becoming popular in a ship microgrid. In order to balance the two non-compatible objectives, a new differential evolution variant, which is named as SaCIDE-r, was proposed to solve the optimization problem. In this algorithm, a Collective Intelligence (CI) based mutation operator was proposed by mixing some promising donor vectors in the current population. Besides, a self-adaptive mechanism which was developed to avoid introducing extra control parameters. Further, to avoid being trapped in local optima, a re-initialization mechanism was developed. Then, we have evaluated the performances of the proposed SaCIDE-r approach by studying some numerical optimization problems of Congress on Evolutionary Computation (CEC) 2013 with D = 30, compared with seven stateof-the-art DE algorithms. Moreover, the proposed SaCIDE-r method was applied for economic scheduling of a shipboard microgrid under different cases compared with other multi-objective optimizing methods, resulting in very competitive performances. The comprehensive experimental results have demonstrated that the presented SaCIDE-r method might be a feasible solution for such a kind of optimization problem. INDEX TERMS Shipboard microgrid, global optimization, collective intelligence (CI), differential evolution (DE).
In this paper, a Self-learning Collective Intelligence Differential Evolution (SLCIDE) algorithm was proposed to optimize both the architecture and parameters of a Feedforward Neural Network (FNN). In order to improve the exploration-exploitation capability, a new Collective Intelligence (CI) based mutation operator was proposed by mixing some promising donor vectors in the current population. Besides, a self-learning mechanism which was designed to adaptively select m top ranked donor vectors was developed by using a widely used unsupervised learning method, k-means. As a result, the proposed approach can be more adaptive and statistically powerful on versatile problems. Then, we evaluated the performances of the proposed SLCIDE approach by studying some numerical optimization problems of CEC 2013 with D = 30 and D = 50. Further, the proposed SLCIDE method was applied to train a FNN on four most popular datasets, resulting in very competitive performances. The comprehensive experimental results have demonstrated that the presented SLCIDE method obtain better results compared with other state-of-the-art algorithms. INDEX TERMS Evolutionary artificial neural network, global optimization, collective intelligence (CI), differential evolution (DE).
In this article, a multilabel support vector machine (SVM)-based approach is investigated to address the simultaneous decay detection of the marine propulsion system. To verify the performance of the algorithm, we perform some experiments using a simulation dataset from a real-data validated numerical simulator of a Frigate. In particular, we try to train the model without simultaneous decay data, considering the great difficulty of obtaining simultaneous decay data in practice. The experimental results show that the proposed approach can identify the complex decay modes of the marine propulsion system effectively using only simple decay data in the training process.
Introduction
The propulsion system is considered to be the “heart” of a marine ship (Li et al. 2019a). Its safety and reliability are critical to the regular operation of the ship (Bayer et al. 2018; Cheliotis & Lazakis, 2018; Lazakis et al. 2016). However, performance decay may occur to the propulsion system due to the high humidity and high salt characteristics of the marine environment (Fang et al. 2018; Kang et al. 2019; Wang et al. 2019). The decay modes can be divided into single decay and simultaneous decay. Single decay indicates a simple decay mode that only one kind of decay occurs at a time, and simultaneous decay indicates a complex decay mode that multiple decays occur at the same time. To improve the safety and reliability of the marine propulsion system, researchers have proposed many related approaches from the perspective of fault diagnosis.
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