单模型构建的滚动轴承性能健康指标仅能从本身的"单角度"来描述滚动轴承的性能退化状态，具有一定的局限性。为解决这个问题，提出一种基于主成分分析(principal compo-nent analysis，PCA)多模型融合的滚动轴承健康指标构建方法。该方法分别采用支持向量数据描述(support vector data description，SVDD)模型、自联想核回归(auto-associative kernel re-gression，AAKA)模型和高斯混合模型(gaussian mixture module，GMM)构建相应单模型的健康指标，再将3个单模型的健康指标经主成分分析(PCA)融合，并选取第一主成分作为能够包含"多角度"性能退化信息的健康指标(SAG-HI)。试验结果表明，相比于各单模型的健康指标，SAG-HI与滚动轴承保持可靠度的灰置信水平达到98。38%，其相关性、单调性和鲁棒性也均表现为最优，且通过包络谱分析验证了其能够准确且及时监测到早期故障发生时刻。
Construction of performance degradation indicators for rolling bearings based on PCA multi-model fusion
The performance health indicator of rolling bearings constructed from a single model can on-ly describes the performance degradation states of rolling bearings from a single perspective,which has certain limitations.To solve this problem,a method for constructing HI based on PCA multi-model fusion was proposed.The idea was to use SVDD,AAKR,and GMM models to construct the corre-sponding single model HI,and then fuse them through PCA.The first principal component was select-ed as SAG-HI,containing"multi angle"performance degradation information.Experimental results shows that compared to the HI of each single model,SAG-HI achieved 98.06%grey confidence level in maintaining reliability with rolling bearings,and its correlation,monotonicity,and robustness were the best.Envelope spectrum analysis verified its ability to accurately monitor early fault occurrences.
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