This paper proposes a hybrid condition monitoring approach, which integrates bond graph model-based diagnostic technique and data-driven remaining useful life (RUL) prediction, for a nonlinear mechatronic system. In this approach, various degrading faults can be considered and the physical degradation model is not required for RUL prediction. Firstly, an integrated fault signature matrix is proposed by the causal path of bicausal-bond graph model to improve fault isolation performance. After that, a biogeography-based optimization (BBO)-particle filter is developed for fault identification. For prognosis, an optimized extreme learning machine (OELM) is proposed where the hidden layer biases and input weights are optimized by BBO. The fault identification results provide data set to train the OELM for prognosis. Finally, the effectiveness of the approach is verified by simulation and experiment results.

本文提出了一种混合状态监测方法，该方法集成了基于键合图模型的诊断技术和数据驱动的剩余使用寿命 (RUL) 预测，适用于非线性机电系统。在这种方法中，可以考虑各种退化故障，并且 RUL 预测不需要物理退化模型。首先，通过双因果-键图模型的因果路径提出了一个集成的故障特征矩阵，以提高故障隔离性能。之后，开发了一种基于生物地理学的优化（BBO）粒子滤波器用于故障识别。对于预测，提出了一种优化的极限学习机（OELM），其中隐藏层偏差和输入权重由 BBO 优化。故障识别结果为训练 OELM 进行预测提供了数据集。最后，