Abstract Remaining useful life (RUL) prediction plays an important role in increasing the availability and productivity of industrial manufacturing systems. This paper proposes a joint classification-regression scheme for multi-stage RUL prediction. First, the time domain and frequency domain features are extracted from various types of raw sensory data (e.g., acoustic, current, vibration and temperature) to constitute the training data set. Second, the system health stage is classified based on the trained model and real-time sensory data. Third, we perform stage-level RUL prediction with regression algorithm to estimate overall useful life. Distinct from the existing RUL estimation algorithms, the proposed multi-stage remaining useful life (MS-RUL) prediction effectively integrates the machine/deep learning based classification and regression to improve overall estimation accuracy. We conduct the performance evaluation with sensory data from real manufacturing systems. Experimental results demonstrate that the proposed MS-RUL achieves approximately 6.5% accuracy improvements over the state-of-the-art algorithms in the RUL prediction.

中文翻译：

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多阶段剩余使用寿命预测的联合分类回归方法

摘要 剩余使用寿命 (RUL) 预测在提高工业制造系统的可用性和生产力方面发挥着重要作用。本文提出了一种用于多阶段 RUL 预测的联合分类回归方案。首先，从各种类型的原始感官数据（如声学、电流、振动和温度）中提取时域和频域特征，构成训练数据集。其次，根据训练好的模型和实时感知数据对系统健康阶段进行分类。第三，我们使用回归算法进行阶段级 RUL 预测，以估计整体使用寿命。与现有的 RUL 估计算法不同，提出的多阶段剩余使用寿命 (MS-RUL) 预测有效地集成了基于机器/深度学习的分类和回归，以提高整体估计精度。我们使用来自真实制造系统的感官数据进行性能评估。实验结果表明，所提出的 MS-RUL 在 RUL 预测中比最先进的算法实现了大约 6.5% 的准确度提高。