A system and method for predicting remaining lifetime of a component of equipment is provided. The prediction system includes a data module, a feature module, a current data-based prediction module, a historical data-based prediction module, and a confidence module. The data module obtains a test sensor data of the component of equipment. The feature module obtains a historical health indicator and the current-health indicator. The current data-based prediction module obtains a first predicted remaining lifetime and a first prediction confidence according to the current-health indicator. The historical data-based prediction module obtains a second predicted remaining lifetime and a second prediction confidence according to the historical health indicator. The confidence module generates a final predicted remaining lifetime of the component of equipment according to the first predicted remaining lifetime, the second predicted remaining lifetime, the first prediction confidence and the second prediction confidence.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for predicting the remaining lifetime of the component of equipment, comprising: a data module used for obtaining a test sensor data of a component of equipment; a feature module used for obtaining a historical health indicator according to a historical failure data of the component of equipment, and obtaining a current-health indicator according to the test sensor data; a current data-based prediction module used for obtaining a current-health indicator function according to the current-health indicator, and obtaining a first predicted remaining lifetime and a first prediction confidence according to the current-health indicator function; a historical data-based prediction module used for obtaining a historical health indicator model, comprising at least one historical health indicator function, according to the historical health indicator, obtaining a second predicted remaining lifetime according to the historical health indicator model and the current-health indicator function, and obtaining a second prediction confidence according to a relationship between the historical health indicator model and the current-health indicator function; and a confidence module used for generating a final predicted remaining lifetime of the component of equipment according to the first predicted remaining lifetime, the second predicted remaining lifetime, the first prediction confidence and the second prediction confidence; wherein at least one of the data module, the feature module, the current data-based prediction module, the historical data-based prediction module, and the confidence module is implemented by at least one circuit or a processor performing a program. 2. The prediction system according to claim 1, wherein the current-health indicator function predicts a time point at which a predicted health indicator will exceed a threshold in the future, the predicted time point is a failure time point of the component of equipment, and the difference between the failure time point and a current time point is the first predicted remaining lifetime. 3. The prediction system according to claim 1, wherein the historical failure data comprises the sensor data of at least one training cycle, the sensor data of each training cycle corresponds to a historical health indicator function, in the historical health indicator model, a possible trend interval corresponding to the current-health indicator function is defined, if at least one historical health indicator function exists in the possible trend interval, the historical data-based prediction module obtains a historical fitness function by fitting the at least one historical health indicator function in the possible trend interval and obtains the second predicted remaining lifetime according to the historical fitness function. 4. The prediction system according to claim 3, wherein the historical fitness function predicts a time point at which a predicted health indicator will exceed a threshold, the predicted time point is a failure time point of the component of equipment, and a difference between the failure time point and a current time point is the second predicted remaining lifetime. 5. The prediction system according to claim 3, wherein if a historical health indicator function exists in the possible trend interval, a value of the second prediction confidence is a first value; otherwise, the value of the second prediction confidence is a second value; when the value of the second prediction confidence is the first value, the confidence module uses the second predicted remaining lifetime as the final predicted remaining lifetime of the component of equipment. 6. The prediction system according to claim 5, wherein when the value of the second prediction confidence is the second value, the confidence module uses the first predicted remaining lifetime as the final predicted remaining lifetime of the component of equipment. 7. The prediction system according to claim 1, wherein the second prediction confidence is related to the similarity between the historical health indicator model and the current-health indicator function. 8. The prediction system according to claim 1, wherein the confidence module calculates a combined coefficient by normalizing the first prediction confidence and the second prediction confidence and calculates a weighting average of the first predicted remaining lifetime and the second predicted remaining lifetime according to the combined coefficient to generate a final predicted remaining lifetime. 9. The prediction system according to claim 1, wherein the current data-based prediction module obtains the current-health indicator function by using a regression model according to the test sensor data, and a regression error corresponding to the regression model can be used as the first prediction confidence. 10. The prediction system according to claim 1, wherein in the confidence module determines whether the first prediction confidence or the second prediction confidence is larger than a confidence threshold, and uses the one of the first predicted remaining lifetime and the second predicted remaining lifetime corresponding to the first prediction confidence or the second prediction confidence that is larger than the confidence threshold as the final predicted remaining lifetime. 11. A method for predicting a remaining lifetime of a component of equipment, comprising: obtaining a test sensor data of the component of equipment; obtaining a historical health indicator according to a historical failure data of the component of equipment, and obtaining a current-health indicator according to the test sensor data; obtaining a current-health indicator function according to the current-health indicator, and obtaining a first predicted remaining lifetime and a first prediction confidence according to the current-health indicator function; obtaining a historical health indicator model, comprising at least one historical health indicator function, according to the historical health indicator, obtaining a second predicted remaining lifetime according to the historical health indicator model and the current-health indicator function, and obtaining a second prediction confidence according to a relationship between the historical health indicator model and the current-health indicator function; and generating a final predicted remaining lifetime of the component of equipment according to the first predicted remaining lifetime, the second predicted remaining lifetime, the first prediction confidence and the second prediction confidence. 12. The prediction method according to claim 11, wherein the current-health indicator function predicts a time point at which a predicted health indicator will exceed a threshold in the future, the predicted time point is a failure time point of the component of equipment, and a difference between the failure time point and a current time point is the first predicted remaining lifetime. 13. The prediction method according to claim 11, wherein the historical failure data comprises the sensor data of at least one training cycle, the sensor data of each training cycle corresponds to a historical health indicator function, in the historical health indicator model, a possible trend interval corresponding to the current-health indicator function is defined, if at least one historical health indicator function exists in the possible trend interval, the historical data-based prediction module obtains a historical fitness function by fitting the at least one historical health indicator function in the possible trend interval and obtains the second predicted remaining lifetime according to the historical fitness function. 14. The prediction method according to claim 13, wherein the historical fitness function predicts a time point at which a predicted health indicator will exceed a threshold in the future, the predicted time point is a failure time point of the component of equipment, and the difference between the failure time point and a current time point is the second predicted remaining lifetime. 15. The prediction method according to claim 13, wherein if at least one historical health indicator function exists in the possible trend interval, a value of the second prediction confidence is a first value; otherwise, the value of the second prediction confidence is a second value; when the value of the second prediction confidence is the first value, the confidence module uses the second predicted remaining lifetime as the final predicted remaining lifetime of the component of equipment. 16. The prediction method according to claim 15, wherein when the value of the second prediction confidence is the second value, the confidence module uses the first predicted remaining lifetime as the final predicted remaining lifetime of the component of equipment. 17. The prediction method according to claim 11, wherein the second prediction confidence is related to the similarity between the historical health indicator model and the current-health indicator function. 18. The prediction method according to claim 11, wherein in the step of generating a final predicted remaining lifetime, a combined coefficient is calculated by normalizing the first prediction confidence and the second prediction confidence, and a weighting average of the first predicted remaining lifetime and the second predicted remaining lifetime is calculated according to the combined coefficient to generate the final predicted remaining lifetime. 19. The prediction method according to claim 11, wherein in the step of obtaining a current-health indicator function, a current-health indicator function is obtained by using a regression model according to the test sensor data, and a regression error corresponding to the regression model can be used as the first prediction confidence. 20. The prediction method according to claim 11, wherein in the step of obtaining a final predicted remaining lifetime, whether the first prediction confidence or the second prediction confidence is larger than a confidence threshold is determined, and the one of the first predicted remaining lifetime and the second predicted remaining lifetime corresponding to the first prediction confidence or the second prediction confidence that is larger than the confidence threshold is used as the final predicted remaining lifetime.