基于主元分析(pca)的统计检测方法已经被广泛应用于各种化工过程的故障检测和识别。
Numerous statistical process monitoring methods based on principal component analysis (PCA) have been developed and applied to various chemical processes for fault detection and identification.
提出了一种在模型参数变动和存在外部扰动条件下的控制系统故障检测与识别方法。
A method for fault detection and identification in control system under external disturbance and variations of plant parameters is presented.
将该自适应算法应用于驱动桥故障检测中，结果表明该算法能够区分和识别驱动桥存在的不同故障类型。
The algorithm was applied to faults diagnosis of driver axle. The result showed that the algorithm could distinguish and recognize different faults of driver axle.
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