快速且准确的处理工业数据是工业高效生产的必要保证之一，但是目前大多数工业数据处理依然由效率较低的人力来实现。使用高效的计算机技术替代现有的人工操作，既是形势所迫，也是时代所趋。本论文尝试通过实验将模式识别技术应用于工业设备故障诊断中，采用传统的KNN方法和PCA方法，对一份来自美国凯斯西储大学轴承数据中心网站的工业设备数据进行故障诊断。希望尝试两种方法的多种组合，找到可以高效诊断同类工业故障的方法；并在实验过程中，掌握两种方法的原理和算法，充分认识到它们各自的优点和缺点。我们在实验中选择多个变量，设置多组对照组，通过比较在不同条件下两种方法的表现，发现先进行人工提取数据特征，再利用PCA方法对特征进行一定程度上的降维，最后选择合适的K值使用KNN方法对降维后的数据进行分类，由此得到故障诊断的效率最高。但是该方法仍存在一定的不足，如人工提取特征存在一定的不确定性、传统KNN方法计算量过多等，需要在日后进一步研究模式识别乃至深度学习相关应用时加以改进。

Fast and accurate processing of industrial data is one of the necessary guarantees for efficient industrial production, but most industrial data processing is still carried out by less efficient labor. The use of efficient computer technology to replace existing manual operations is both a situation and a trend. This thesis attempts to apply the pattern recognition technology to the fault diagnosis of industrial equipment through experiments. The traditional KNN method and PCA method are used to diagnose the industrial equipment data from the Case Western Reserve University Bearing Data Center website. It is hoped that various combinations of the two methods can be tried to find one that can efficiently diagnose similar industrial faults; and in the course of the experiment, the principles and algorithms of the two methods are mastered, and their respective advantages and disadvantages are fully recognized. At the same time, in the experiment, multiple variables are selected and multiple groups of control groups are set up. By comparing the performance of the two methods under different conditions, it is found that if we can extract the data characteristics manually at first, then reduce the features to some extent by PCA method, and finally, use KNN method with an appropriate K value, the data after dimension reduction is classified, and the fault diagnosis in this way is the most efficient. However, there are still some shortcomings in this method, such as the existence of certain uncertainty in the artificial extraction feature and the excessive computation of the traditional KNN method. It needs to be improved in the future to further learn pattern recognition and even deep learning related knowledge.