Many automated sound-based fault diagnosis or classification methods have been presented in the literature. A novel automatic fault diagnosis method is presented by using sounds for the cooling system of the data center. A novel feature generator and an iterative feature selector are used together to present an automated data center cooling system (DCCS) fault diagnosing method. A new feature generator is proposed inspired by knitting hence, it is called a local knit pattern (LKP). A multiple pooling based decomposition method is presented as a preprocessor. The LKP generates features from each signal. Iterative neighborhood component analysis (INCA) feature selector selects the most discriminative. Twelve classifiers are calculated in the classification phase. The selected classifiers were achieved greater than 90.0% classification accuracies, and the best-resulted classifier is Quadratic SVM. It reached 96.40% classification accuracy. Results show that new generation automated sound fault diagnosis applications can also be developed as novel sound-based fault detection applications.

文献中已经提出了许多基于声音的自动故障诊断或分类方法。提出了一种利用声音对数据中心冷却系统进行故障诊断的新颖方法。新颖的特征生成器和迭代的特征选择器一起使用，提出了一种自动数据中心冷却系统（DCCS）故障诊断方法。提出了一种受编织启发的新特征生成器，因此称为局部编织花样（LKP）。提出了一种基于多重池的分解方法作为预处理器。LKP从每个信号生成特征。迭代邻域分量分析（INCA）功能选择器选择最有区别的。在分类阶段计算了十二个分类器。所选分类器的分类精度达到90.0％以上，效果最好的分类器是Quadratic SVM。达到96.40％的分类精度。结果表明，新一代的自动声音故障诊断应用程序也可以开发为新颖的基于声音的故障检测应用程序。