Machine health monitoring is of great importance in industrial informatics field. Recently, deep learning methods applied to machine health monitoring have been proven effective. However, the existing methods face enormous difficulties in extracting heterogeneous features indicating the variation until failure and revealing the inherent high-dimensional features of massive signals, which affect the accuracy and efficiency of machine health monitoring. In this paper, a novel data-driven machine health monitoring method is proposed using adaptive kernel spectral clustering (AKSC) and deep long short-term memory recurrent neural networks (LSTM-RNN). This method include three steps: First, features in the time domain, frequency domain, and time-frequency domain are, respectively, extracted from massive measured signals. And, an Euclidean distance based algorithm is designed to select degradation features. Second, the AKSC algorithm is introduced to adaptively identify machine anomaly behaviors from multiple degradation features. Third, a new deep learning model (LSTM-RNN) is constructed to update and predict the failure time of the machine. The effectiveness of the proposed method is validated using a set of test-to-failure experimental data. The results show that the performance of the proposed method is competitive with other existing methods.

中文翻译：

---

使用自适应内核谱聚类和深度长短期记忆循环神经网络进行机器健康监控


机器健康监测在工业信息学领域非常重要。最近，应用于机器健康监测的深度学习方法已被证明是有效的。然而，现有方法在提取表示故障前变化的异构特征以及揭示海量信号固有的高维特征方面面临着巨大的困难，这影响了机器健康监测的准确性和效率。本文提出了一种使用自适应核谱聚类（AKSC）和深度长短期记忆循环神经网络（LSTM-RNN）的新型数据驱动机器健康监测方法。该方法包括三个步骤：首先，从大量测量信号中分别提取时域、频域和时频域的特征。并且，设计了基于欧几里得距离的算法来选择退化特征。其次，引入AKSC算法从多个退化特征中自适应地识别机器异常行为。第三，构建新的深度学习模型（LSTM-RNN）来更新和预测机器的故障时间。使用一组测试失败的实验数据验证了所提出方法的有效性。结果表明，该方法的性能与其他现有方法相比具有竞争力。