数据基故障诊断算法更新问题研究

机内测试被广泛应用于故障诊断、装备健康管理与预测等领域。针对机内测试设备在设计和升级时遇到的分类器更新、样本数量不平衡、硬件条件限制问题,提出初步解决方案。利用基于密度的聚类和人工免疫方法处理原始数据;提出基于代表样本点的混合学习方法;利用支持向量机和仿真案例验证所提方法。结果表明所提方法能够解决上述问题,有助于基于数据的机内测试设备设计与升级。

Study of data based fault diagnosis algorithm update problem

BITE (built-in test equipment) is widely used in many fields such as fault diagnosis, equipment prognosis and health management. The problems encountered in the process of BITE design and update, including the classifiers update, samples imbalance and hardware limitation, were analyzed, and the initial solutions were proposed. The density-based cluster and artificial immune system were applied to process the raw data; the delegates-based hybrid learning methods were proposed. The evaluation of the solution was validated by the numerical and experiment examples with support vector machine. Results show that the proposed solution can solve the mentioned problems well and is helpful for data based fault diagnosis design and update in the process of BITE maturation.