## 5 Conclusions  
In this paper, an anomaly diagnosis method for RINS is proposed. The features of different anomaly signals are analyzed, and five indicators are designed base on these features. These five indicators reflect the characteristics of the main aspects of the measurement signals. The new method monitors the observation data of RIMU and identifies anomalies with the proposed five indicators. In this paper, the simulation signals with anomalies serve as samples to test the proposed method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Anomalies Types** & \begin{tabular}{c} **Number of** \\ **Experiments** \\ \end{tabular} & \begin{tabular}{c} **Number of Correct** \\ **Detection** \\ \end{tabular} & \
\begin{tabular}{c} **PCD** \\ \end{tabular} &
\begin{tabular}{c} **Number of Correct** \\ **Recognition** \\ \end{tabular} & **PCR** \\ \hline Isolated Outliers & 500 & 496 & 99.20\% & 492 & 99.19\% \\ Outlier Patches & 500 & 495 & 99.00\% & 493 & 99.60\% \\ Transient Fault & 500 & 493 & 98.60\% & 482 & 97.77\% \\ Noise Fault & 500 & 492 & 98.40\% & 483 & 98.17\% \\ Constant Drift Fault & 500 & 490 & 98.00\% & 488 & 99.59\% \\ Multiplicative Fault & 500 & 489 & 97.80\% & 483 & 98.77\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Simulation experiment statistics of the new method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Anomalies Types** & **Traditional Method** & **Number of Experiments** & **Number of Correct Detection** & **PCD** \\ \hline \multirow{2}{*}{Outliers} & 3\(\sigma\) Detection Method & 1000 & 982 & 98.20\% \\  & Grubbs Detection & 1000 & 976 & 97.60\% \\ \hline \multirow{2}{*}{Faults} & GLT & 2000 & 1962 & 98.10\% \\  & OPT & 2000 & 1959 & 97.95\% \\ \multicolumn{2}{c}{Chi-square Test} & 2000 & 1953 & 97.65\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Simulation experiment statistics of the traditional method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Data Anomalies Types**} & \multicolumn{4}{c}{**PCD**} \\ \cline{2-5}  & **GLT** & **OPT** & **Chi-Square Test** & **The New Method** \\ \hline Transient Fault & 92.63\% & 91.46\% & 92.02\% & 97.77\% \\ Noise Fault & 90.12\% & 91.53\% & 90.97\% & 98.17\% \\ Constant Drift Fault & 93.69\% & 93.77\% & 94.45\% & 99.59\% \\ Multiplicative Fault & 92.36\% & 92.53\% & 91.78\% & 98.77\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Statistics of diagnostic results of different methods after outliers eliminating.  
Experiments prove that the detection performance of the proposed method is better than the previous FDI methods. Further, our proposed method can identify the specific anomaly types. Based on the recognition results of this paper, the different anomalies can be identified and handled in a more reasonable way. At the same time, this method can solve interaction between the outlier eliminating algorithms and FDI technologies. Both the reliability and stability of RIMU can be improved.  
Due to the manufacturing technology and the structure of MEMS, the MEMS sensors are packaged during the manufacturing. It is difficult to set practical faults artificially on the sensors, so plenty of typical sample data is hard to collect. As a result, the theories of statistical learning are not applicable to MEMS RINS. Besides, the proposed anomaly recognition method also needs more practical samples to test. These issues remain to be resolved in future research.  
B.D.: Conceptualization, Formal analysis, Writing--original draft; J.S.: Conceptualization, Supervision, Writing--review and editing; Z.S.: Conceptualization, Supervision, Writing--review and editing, funding acquisition.  
This research received no external funding.  
The authors declare no conflict of interest.