Abstract：
Remaining useful life prediction is of huge significance in preventing equipment malfunctions and reducing maintenance costs. Currently, machine learning algorithms have become hotspots in remaining useful life prediction due to their high flexibility and convenience. However, machine learnings require large amounts of data, and their prediction performance depends heavily on the selection of hyper-parameters. To overcome these shortcomings, a novel remaining useful life prediction method for small sample cases is proposed based on multi-support vector regression fusion. In the offline training phase, the fusion model is established, consisting of multiple support vector regression sub-models To obtain the optimal sub-model parameters, the Bayesian optimization algorithm is applied and an improved optimization target is formulated with various metrics describing regression and prediction performance. In the online prediction phase, an adaptive weight updating algorithm based on dynamic time warping is developed to measure the fitness of each sub-model and determine the corresponding weight value. The C-MAPSS engine dataset is used to test the performance of the proposed method, along with some existing machine learning methods as comparison. The proposed method only requires 30% of the training data sample to achieve high accuracy, with a root mean square error of 14.98, which is superior to other state-of-the-art methods. The results demonstrate the superiority of the proposed method.
摘要：
剩余使用寿命预测在防止设备故障和降低维护成本方面具有重要意义。目前,机器学习算法以其较高的灵活性和便捷性成为剩余使用寿命预测的热点。然而,机器学习需要大量数据，它们的预测性能在很大程度上取决于超参数的选择。为了克服这些缺点，提出了一种基于多支持向量回归融合的小样本剩余寿命预测方法。在离线训练阶段,建立了融合模型，由多个支持向量回归子模型组成，以获得最优子模型参数，应用贝叶斯优化算法，用描述回归和预测性能的各种指标制定改进的优化目标。在在线预测阶段，提出了一种基于动态时间规整的自适应权值更新算法，用于测量每个子模型的适应度并确定相应的权值。C-MAPSS引擎数据集用于测试所提出方法的性能,以及一些现有的机器学习方法作为比较。所提出的方法只需要30%的训练数据样本就能达到很高的准确率,均方根误差为14.98，优于其他最先进的方法。结果表明了该方法的优越性。
