The disclosed embodiments relate to a technique for training a prognostic pattern-recognition system to detect incipient anomalies that arise during execution of a computer system. During operation, the system gathers and stores telemetry data obtained from n sensors in the computer system during operation of the computer system. Next, the system uses the telemetry data gathered from the n sensors to train a baseline model for the prognostic pattern-recognition system. The prognostic pattern-recognition system then uses the baseline model in a surveillance mode to detect incipient anomalies that arise during execution of the computer system. The system also uses the stored telemetry data to train a set of additional models, wherein each additional model is trained to operate with one or more missing sensors. Finally, the system stores the additional models to be used in place of the baseline model when one or more sensors fail in the computer system.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for training a prognostic pattern-recognition system to detect incipient anomalies that arise during execution of a computer system, comprising: gathering and storing telemetry data obtained from n sensors in the computer system during operation of the computer system; using the telemetry data gathered from the n sensors to train a baseline model for the prognostic pattern-recognition system; using the prognostic pattern-recognition system with the baseline model in a surveillance mode to detect incipient anomalies that arise during execution of the computer system; using the stored telemetry data to train a set of additional models, wherein each additional model is trained to operate with one or more missing sensors; and storing the additional models to be used in place of the baseline model when one or more sensors fail in the computer system. 2. The method of claim 1, wherein training the set of additional models includes: training additional models to operate with any one of the n sensors missing; training additional models to operate with any combination of two of the n sensors missing; and training additional models to operate with more than two of the n sensors missing. 3. The method of claim 1, wherein the set of additional models are trained using background processes while the prognostic pattern-recognition system operates in the surveillance mode. 4. The method of claim 1, wherein when one or more of the n sensors in the computer system fails, the method further comprises: selecting a substitute model from the set of additional models, wherein the substitute model was trained without using telemetry data from the one or more failed sensors; and updating the prognostic pattern-recognition system to use the substitute model while operating in the surveillance mode. 5. The method of claim 4, wherein while the prognostic pattern-recognition system is operating using the substitute model, the method further comprises training supplemental models to be included in the set of additional models, wherein the supplemental models are trained without using telemetry data from the one or more failed sensors, and without using telemetry data from one or more other non-failed sensors. 6. The method of claim 1, wherein detecting an incipient anomaly includes detecting one or more of the following: an impending failure of the computer system; and a malicious-intrusion event in the computer system. 7. The method of claim 1, wherein the baseline model and the set of additional models are trained using a nonlinear, nonparametric (NLNP) regression technique. 8. The method of claim 1, wherein the telemetry data includes: data gathered from physical sensors in the computer system that monitor physical performance parameters, including at least one or more of temperature, voltage, fan speeds, current, vibration, and acoustic parameters; and software-related telemetry parameters, including one or more of processor load, memory usage, cache usage, system throughput, queue lengths, I/O traffic, and quality of service (QOS). 9. A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for training a prognostic pattern-recognition system to detect incipient anomalies that arise during execution of a computer system, the method comprising: gathering and storing telemetry data obtained from n sensors in the computer system during operation of the computer system; using the telemetry data gathered from the n sensors to train a baseline model for the prognostic pattern-recognition system; using the prognostic pattern-recognition system with the baseline model in a surveillance mode to detect incipient anomalies that arise during execution of the computer system; using the stored telemetry data to train a set of additional models, wherein each additional model is trained to operate with one or more missing sensors; and storing the additional models to be used in place of the baseline model when one or more sensors fail in the computer system. 10. The non-transitory computer-readable storage medium of claim 9, wherein using the stored telemetry data to train the set of additional models involves: training additional models to operate with any one of the n sensors missing; training additional models to operate with any combination of two of the n sensors missing; and training additional models to operate with more than two of the n sensors missing. 11. The non-transitory computer-readable storage medium of claim 9, wherein using the stored telemetry data to train the set of additional models involves using background processes to train the set of additional models while the prognostic pattern-recognition system operates in the surveillance mode. 12. The non-transitory computer-readable storage medium of claim 9, wherein when one or more of the n sensors in the computer system fails, the method performed by the instructions stored in the computer-readable storage medium further comprises: selecting a substitute model from the set of additional models, wherein the substitute model was trained without using telemetry data from the one or more failed sensors; and updating the prognostic pattern-recognition system to use the substitute model while operating in the surveillance mode. 13. The non-transitory computer-readable storage medium of claim 12, wherein while the prognostic pattern-recognition system is operating using the substitute model, the method performed by the instructions stored in the computer-readable storage medium further comprises training supplemental models to be included in the set of additional models, wherein the supplemental models are trained without using telemetry data from the one or more failed sensors, and without using telemetry data from one or more other non-failed sensors. 14. The non-transitory computer-readable storage medium of claim 9, wherein using the prognostic pattern-recognition system with the baseline model in the surveillance mode to detect incipient anomalies that arise during execution of the computer system involves detecting one or more of the following: an impending failure of the computer system; and a malicious-intrusion event in the computer system. 15. The non-transitory computer-readable storage medium of claim 9, wherein using the telemetry data gathered from the n sensors to train the baseline model involves using a nonlinear, nonparametric (NLNP) regression technique. 16. The non-transitory computer-readable storage medium of claim 9, wherein the telemetry data includes: data gathered from physical sensors in the computer system that monitor physical performance parameters, including at least one or more of temperature, voltage, fan speeds, current, vibration, and acoustic parameters; and software-related telemetry parameters, including one or more of processor load, memory usage, cache usage, system throughput, queue lengths, I/O traffic, and quality of service (QOS). 17. A system that trains a prognostic pattern-recognition system to detect incipient anomalies that arise during execution of a computer system, comprising: a training mechanism that trains a prognostic pattern-recognition system, wherein during operation, the training mechanism, gathers and stores telemetry data obtained from n sensors in the computer system during operation of the computer system, uses the telemetry data gathered from the n sensors to train a baseline model for the prognostic pattern-recognition system, uses the stored telemetry data to train a set of additional models, wherein each additional model is trained to operate with one or more missing sensors, and stores the additional models to be used in place of the baseline model when one or more sensors fail in the computer system; and a detection mechanism that uses the prognostic pattern-recognition system with the baseline model in a surveillance mode to detect incipient anomalies that arise during execution of the computer system. 18. The system of claim 17, wherein the training mechanism trains the set of additional models using background processes while the prognostic pattern-recognition system operates in the surveillance mode. 19. The system of claim 17, wherein when one or more of the n sensors in the computer system fails, the detection mechanism: selects a substitute model from the set of additional models, wherein the substitute model was trained without using telemetry data from the one or more failed sensors; and updates the prognostic pattern-recognition system to use the substitute model while operating in the surveillance mode. 20. The system of claim 12, wherein while the prognostic pattern-recognition system is operating using the substitute model, the training mechanism trains supplemental models to be included in the set of additional models, wherein the supplemental models are trained without using telemetry data from the one or more failed sensors, and without using telemetry data from one or more other non-failed sensors.