# Enhanced K-Nearest Neighbor for Intelligent Fault Diagnosis of Rotating Machinery
## 5 Conclusions  
For fault diagnosis of rotating machineries, EKNN is proposed to diagnose faults in a more automatic way, which conducts feature extraction via unsupervised methods, and a new case-based classification method is proposed to diagnose faults via nearest neighbors determined automatically. Aiming to address the problems of the expensive and inefficient global search for nearest neighbors in KNN, the new classification method can perform a more efficient search via obtained correlation vectors of testing sample reconstruction. Particularly, as the feature extraction part is unsupervised and focuses on sparse feature extraction, the extracted features will be more discriminative for fault diagnosis.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Fault Classes** & **TP** & **FP** & **TN** & **FN** & **Precision** & **Recall** \\ \hline NO & 1000 & 1.25 & 8844.9 & 0 & 0.999 & 1.000 \\ \hline IF02 & 999.95 & 8.5 & 8844.95 & 0.05 & 0.992 & 1.000 \\ \hline IF06 & 998.9 & 3.95 & 8846 &  
diagnosis. Meanwhile, unlabeled data can be used to train the feature extraction part simply, and a very small number of labeled samples is needed in its classification, which also needs no supervision and distinguishes it from other intelligent fault diagnosis methods.  
Compared with existing case-based methods, it takes advantage of powerful feature extraction and realizes more automatic and precise classification. Compared with existing parameter-based diagnosis methods, it can utilize training samples and needs no training in classification. Meanwhile, when a new fault appears, the proposed method can utilize the case-based fault diagnosis part and learn to diagnose new faults, which is superior to existing parameter-based methods.  
Extensive experiments conducted on a bearing fault dataset confirm the effectiveness of the proposed method. In addition, although the proposed method can take advantage of case-based methods, it still needs to investigate how to absorb new samples into the network to improve its self-learning process in our future work.  
Conceptualization, J.L. and W.Q.; methodology, J.L. and S.L.; software, W.Q. and R.C.; validation, J.L., W.Q. and S.L.; formal analysis, J.L., W.Q., and S.L.; investigation, S.L.; resources, S.L.; data curation, S.L.; writing--original draft preparation, W.Q.; writing--review and editing, J.L. and R.C.; visualization, J.L. and W.Q.; supervision, S.L.; project administration, S.L.; funding acquisition, J.L. and S.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (51975276), the Major National Science and Technology Projects (2017-IV-0008-0045), the Advance Research Field Fund Project of China (61400040304), the National Key Research and Development Program of China (2020 YFB1709801) and the Fundamental Research Funds for the Central Universities (1002-YAH20008).  
The authors would like to thank [PERSON] from China University of Geosciences for providing language help.  
The authors declare no conflict of interest.