## 6 Conclusions  
This paper proposes an improved STM based on the morphological pattern and symbolic aggregate approximation-based similarity measurement method, which can directly and effectively use the original monitoring data for life prediction without extracting the degradation trend of the monitoring data. According to the characteristics that the equipment performance degradation is reflected in the trend change of monitoring data, while the changes of operating conditions and environment are reflected in the detail change of monitoring data, the MP-SAX is first used to measure the similarity between the monitoring data under multiple operating conditions. Then, the similar life candidate set and corresponding weight are obtained according to the MP-SAX. Finally, the life prediction results of equipment under multiple operating conditions can be calculated by aggregating the similar life candidate set. Through the analysis and verification of public datasets of the turbofan engine from the NASA Ames Prognostics Data Repository, it is proved that the proposed method can achieve life prediction only using original monitoring data without extracting degradation trend of monitoring data. In addition, the prediction result of STM can be effectively improved by improving the similarity measurement accuracy of STM. Although some important techniques associated with the proposed method have been investigated in this paper, the STM can only obtain the prediction point of life and do not consider the uncertainty in the prediction. Figure 11: RUL error of the single tested sample. (a) Set #2. (b) Set #4. Figure 10: RUL result of the single tested sample. (a) Set #2. (b) Set #4. **Author Contributions:** Conceptualization, J.Y. ; formal analysis, J.Y. ; investigation, J.Y. ; methodology, J.Y. ; project administration, M.X. ; software, J.Y. and R.W. ; supervision, M.X. ; validation, Y.L. ; writing--original draft, J.Y. ; writing--review and editing, J.Y., Y.L., R.W. and M.X. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [[[https://ti.arc.nasa.gov/m/project/prognostic-repository/CMAPSSData.zip](https://ti.arc.nasa.gov/m/project/prognostic-repository/CMAPSSData.zip)]([https://ti.arc.nasa.gov/m/project/prognostic-repository/CMAPSSData.zip](https://ti.arc.nasa.gov/m/project/prognostic-repository/CMAPSSData.zip))]. **Conflicts of Interest:** The authors declare no conflict of interest. * [PERSON] et al. (2020) [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Role of artificial intelligence in rotor fault diagnosis: A comprehensive review. _Artif. Intell. Rev._**2020**, _54_, 2609-2668. [CrossRef]
* [PERSON] (2021) [PERSON] Intelligent Fault Diagnosis and Forecast of Time-Varying Bearing Based on Deep Learning VMD-DenseNet. _Sensors_**2021**, _21_, 7467. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Multi-Sensor Fault Diagnosis of Underwater Thruster Propeller Based on Deep Learning. _Sensors_**2021**, _21_, 7187. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A Semi-Supervised Fault Diagnosis Method Based on Improved Bidirectional Generative Adversarial Network. _Appl. Sci._**2021**, _11_, 9401. [CrossRef]
* [PERSON] and [PERSON] (2020) [PERSON]; [PERSON] A BiGRU method for remaining useful life prediction of machinery. _Measurement_**2021**, _167_, 108277. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Recurrent convolutional neural network: A new framework for remaining useful life prediction of machinery. _Neurocomputing_**2020**, _379_, 117-129. [CrossRef]
* [PERSON] et al.