# A Hierarchical Decision Fusion Diagnosis Method for Rolling Bearings
## 4 Conclusions  
In this paper, a hierarchical diagnosis method for rolling bearings based on decision fusion is proposed, and bearing data of West Reserve University are used to verify the method. The results show that the proposed method can accurately realize the detection of bearing running state, the isolation of fault position and estimation of the fault severity of the bearing. Because most bearings are in a normal state, so the calculation cost of the proposed method is low. At the same time, the hierarchical diagnosis method only needs to update the related neural networks, which enhances the maintainability of the algorithm. In addition, the proposed method has good adaptability to load change. Compared with the raw vibration signal, the fault detection accuracy is equivalent, but the accuracy of fault isolation and fault severity estimation is obviously improved. The decision fusion is realized by the voting method, which reflects the comprehensive diagnosis results of each characteristic and avoids the missed diagnosis and misdiagnosis caused by the insensitivity of certain characteristics to specific faults. However, whether this method is  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Diagnostic Layer**} & \multicolumn{1}{c}{**Fault Detection**} & \multicolumn{1}{c}{**Fault Isolation**} & \multicolumn{2}{c}{**Inner Race**} & \multicolumn{2}{c}{**Ball Degree**} & \multicolumn{2}{c}{**Outer Race**} \\  & & & & & & **Degree** & & & & **Degree** \\ \hline \multirow{3}{*}{**Signal**} & **Raw** & **WPT** & **Raw** & **WPT** & **Raw** & **WPT** & **Raw** & **WPT** & **Raw** & **WPT** \\  & **Signal** & **Signal** & **Signal** & **Signal** & **Signal** & **Signal** & **Signal** & **Signal** & **Signal** & **Signal** \\ \hline \multirow{3}{*}{Load} & HP0 & 97.86 & 100 & 91.43 & 98.57 & 100 & 100 & 80.95 & 99.05 & 100 & 100 \\  & HP0\(\rightarrow\)HP1 & 97.14 & 98.57 & 87.9 & 93.81 & 96.19 & 100 & 72.38 & 76.19 & 100 & 100 \\  & HP0\(\rightarrow\)HP2 & 98.57 & 97.14 & 90.48 & 89.05 & 98.1 & 100 & 80.48 & 80.95 & 100 & 100 \\  & HP0\(\rightarrow\)HP3 & 96.43 & 99.29 & 86.19 & 95.24 & 97.62 & 100 & 68.57 & 70.95 & 99.52 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Diagnosis result under variable load  
Figure 19: Comparison of fault diagnosis effect of rolling bearings based on decision fusion.  
suitable for bearing multi-fault diagnosis needs further study. In addition, decision fusion considering frequency-domain feature diagnosis results and fusion of different machine learning algorithms need to be further studied.  
Conceptualization, J.F.; methodology, Y.C. and X.L.; software, J.F.; validation, S.L. and Y.C.; formal analysis, Y.C.; investigation, X.L.; resources, Y.C.; data curation, S.L.; writing--original draft preparation, J.F.; writing--review and editing, X.L.; visualization, J.F.; supervision, S.L.; project administration, Y.C.; funding acquisition, Y.C. 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 (No. 51979059) and National Science and Technology Major Project (2017-I-0007-0008).  
Not applicable.  
The authors declare no conflict of interest.