# US 11,308,404 B2 · Synthesizing high-fidelity signals with spikes for prognostic-surveillance applications > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,308,404 B2 - Document ID: US11308404B2 - Status: Granted - Kind: B2 - Application number: 16/215,345 - Filed: 2018-12-10 - Published: 2020-06-11 - Granted: 2022-04-19 - Earliest claimed: 2018-12-10 - Assignees: Oracle International Corporation - Applicants: Oracle International Corporation - Inventors: Guang C. Wang; Kenny C. Gross - Web page: https://patentgrep.com/patent/US11308404B2 ## CPC - G06N 5/04: Inference or reasoning models - G01R 23/20: Measurement of non-linear distortion - G06N 20/00: Machine learning - H04L 63/1425: {Traffic logging, e.g. anomaly detection} - G01R 31/343: {in operation} - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G01R: MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES (indicating correct tuning of resonant circuits H03J3/12) - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) ## Abstract The system receives original time-series signals from sensors in a monitored system. Next, the system detects and removes spikes from the original time-series signals to produce despiked original time-series signals, which involves using the original time-series data to optimize a damping factor, which is applied to a threshold for a spike-detection technique, and using the spike-detection technique with the optimized damping factor to detect the spikes. The system then generates despiked synthetic time-series signals, which are statistically indistinguishable from the despiked original time-series signals. The system also includes synthetic spikes, which have the same temporal, amplitude and width distributions as the spikes in the original time-series signals, in the despiked synthetic time-series signals to produce synthetic time-series signals with spikes. The system uses the synthetic time-series signals with spikes to train an inferential model, and uses the inferential model to perform prognostic-surveillance operations on subsequently-received signals from the monitored system. ## Claims ### Claim 1 A method for performing prognostic-surveillance operations on a monitored system, comprising: receiving original time-series signals from sensors in the monitored system; detecting and removing spikes from the original time-series signals to produce despiked original time-series signals, wherein detecting the spikes involves, using the original time-series data to optimize a damping factor, which is applied to a threshold for a spike-detection technique, and using the spike-detection technique with the optimized damping factor to detect the spikes; generating despiked synthetic time-series signals, which are statistically indistinguishable from the despiked original time-series signals; including synthetic spikes, which have the same temporal distribution, amplitude distribution and width distribution as the spikes in the original time-series signals, in the despiked synthetic time-series signals to produce synthetic time-series signals with spikes; using the synthetic time-series signals with spikes to train an inferential model; and using the inferential model to perform prognostic-surveillance operations on subsequently-received time-series signals from the monitored system. ### Claim 2 (depends on 1) The method of claim 1, wherein optimizing the damping factor comprises: using the spike-detection technique with an initial damping factor to detect and remove the spikes from the original time-series signals to produce despiked original time-series signals; generating despiked synthetic time-series signals, which are statistically indistinguishable from the despiked original time-series signals; including synthetic spikes in the despiked synthetic time-series signals to produce ground truth synthetic time-series signals, wherein the synthetic spikes have expanded and contracted temporal distributions, amplitude distributions and width distributions with respect to the spikes in the original time-series signals; performing tests, which involve varying the damping factor while using the spike-detection technique to detect spikes in the ground truth synthetic time-series signals; and determining an optimized damping factor based on true and false detections resulting from the tests. ### Claim 3 (depends on 1) The method of claim 1, wherein the spike-detection technique comprises one of the following: a phase-space thresholding technique, which uses a phase-space-related threshold; an acceleration-thresholding technique, which uses an acceleration threshold; and a wavelet-thresholding technique, which uses a wavelet-space-related threshold. ### Claim 4 (depends on 1) The method of claim 1, wherein generating the despiked synthetic time-series signals includes: decomposing the despiked original time-series signals into deterministic and stochastic components; and using the deterministic and stochastic components to produce the despiked synthetic time-series signals. ### Claim 5 (depends on 1) The method of claim 1, wherein the despiked synthetic time-series signals have the same serial-correlation structure, cross-correlation structure, and stochastic content as the despiked original time-series signals. ### Claim 6 (depends on 5) The method of claim 5, wherein the stochastic content includes one or more of the following: a mean; a variance; a skewness; a kurtosis; and Kolmogorov-Smirnov test statistics. ### Claim 7 (depends on 1) The method of claim 1, wherein generating the despiked synthetic time-series signals involves using a telemetry parameter synthesis (TPSS) technique, which creates a high-fidelity synthesis equation, and then uses the high-fidelity synthesis equation to produce the despiked synthetic time-series signals. ### Claim 8 (depends on 7) The method of claim 7, wherein using the TPSS technique to generate the despiked synthetic time-series signals includes: using an autocorrelation technique to determine a longest period for each signal in the despiked original time-series signals; selecting a portion of the despiked original time-series signals that contains an integer number of periods; determining a number of Fourier modes Nmode, which equals a number of peaks in a spectral-density function for the despiked original time-series signals; selecting the maximum Nmode Fourier modes; and using the selected Nmode Fourier modes to produce the despiked synthetic time-series signals. ### Claim 9 (depends on 1) The method of claim 1, wherein using the inferential model to perform prognostic-surveillance operations on the subsequently-received time-series signals comprises: using the prognostic inferential model to generate estimated values for the subsequently-received time-series sensor data; performing a pairwise differencing operation between actual values and the estimated values for the subsequently-received time-series sensor data to produce residuals; and performing a sequential probability ratio test (SPRT) on the residuals to detect the incipient anomalies. ### Claim 10 (depends on 9) The method of claim 9, wherein detecting the incipient anomalies comprises detecting one or more of the following: an impending failure of the monitored system; and a malicious-intrusion event in the monitored system. ### Claim 11 A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for performing prognostic-surveillance operations on a monitored system, the method comprising: receiving original time-series signals from sensors in the monitored system; detecting and removing spikes from the original time-series signals to produce despiked original time-series signals, wherein detecting the spikes involves, using the original time-series data to optimize a damping factor, which is applied to a threshold for a spike-detection technique, and using the spike-detection technique with the optimized damping factor to detect the spikes; generating despiked synthetic time-series signals, which are statistically indistinguishable from the despiked original time-series signals; including synthetic spikes, which have the same temporal distribution, amplitude distribution and width distribution as the spikes in the original time-series signals, in the despiked synthetic time-series signals to produce synthetic time-series signals with spikes; using the synthetic time-series signals with spikes to train an inferential model; and using the inferential model to perform prognostic-surveillance operations on subsequently-received time-series signals from the monitored system. ### Claim 12 (depends on 11) The non-transitory computer-readable storage medium of claim 11, wherein optimizing the damping factor comprises: using the spike-detection technique with an initial damping factor to detect and remove the spikes from the original time-series signals to produce despiked original time-series signals; generating despiked synthetic time-series signals, which are statistically indistinguishable from the despiked original time-series signals; including synthetic spikes in the despiked synthetic time-series signals to produce ground truth synthetic time-series signals, wherein the synthetic spikes have expanded and contracted temporal distributions, amplitude distributions and width distributions with respect to the spikes in the original time-series signals; performing tests, which involve varying the damping factor while using the spike-detection technique to detect spikes in the ground truth synthetic time-series signals; and determining an optimized damping factor based on true and false detections resulting from the tests. ### Claim 13 (depends on 11) The non-transitory computer-readable storage medium of claim 11, wherein the spike-detection technique comprises one of the following: a phase-space thresholding technique, which uses a phase-space-related threshold; an acceleration-thresholding technique, which uses an acceleration threshold; and a wavelet-thresholding technique, which uses a wavelet-space-related threshold. ### Claim 14 (depends on 11) The non-transitory computer-readable storage medium of claim 11, wherein generating the despiked synthetic time-series signals includes: decomposing the despiked original time-series signals into deterministic and stochastic components; and using the deterministic and stochastic components to produce the despiked synthetic time-series signals. ### Claim 15 (depends on 11) The non-transitory computer-readable storage medium of claim 11, wherein the despiked synthetic time-series signals have the same serial-correlation structure, cross-correlation structure, and stochastic content as the despiked original time-series signals. ### Claim 16 (depends on 11) The non-transitory computer-readable storage medium of claim 11, wherein generating the despiked synthetic time-series signals involves using a telemetry parameter synthesis (TPSS) technique, which creates a high-fidelity synthesis equation, and then uses the high-fidelity synthesis equation to produce the despiked synthetic time-series signals. ### Claim 17 (depends on 11) The non-transitory computer-readable storage medium of claim 11, wherein using the inferential model to perform prognostic-surveillance operations on the subsequently-received time-series signals comprises: using the prognostic inferential model to generate estimated values for the subsequently-received time-series sensor data; performing a pairwise differencing operation between actual values and the estimated values for the subsequently-received time-series sensor data to produce residuals; and performing a sequential probability ratio test (SPRT) on the residuals to detect the incipient anomalies. ### Claim 18 (depends on 17) The non-transitory computer-readable storage medium of claim 17, wherein detecting the incipient anomalies comprises detecting one or more of the following: an impending failure of the monitored system; and a malicious-intrusion event in the monitored system. ### Claim 19 A system that performs prognostic-surveillance operations on a monitored system, comprising: at least one processor and at least one associated memory; and a prognostic-surveillance mechanism that executes on the at least one processor, wherein during operation, the prognostic-surveillance mechanism: receives original time-series signals from sensors in the monitored system; detects and removes spikes from the original time-series signals to produce despiked original time-series signals, wherein while detecting the spikes, the prognostic-surveillance mechanism, uses the original time-series data to optimize a damping factor, which is applied to a threshold for a spike-detection technique, and uses the spike-detection technique with the optimized damping factor to detect the spikes; generates despiked synthetic time-series signals, which are statistically indistinguishable from the despiked original time-series signals; includes synthetic spikes, which have the same temporal distribution, amplitude distribution and width distribution as the spikes in the original time-series signals, in the despiked synthetic time-series signals to produce synthetic time-series signals with spikes; uses the synthetic time-series signals with spikes to train an inferential model; and uses the inferential model to perform prognostic-surveillance operations on subsequently-received time-series signals from the monitored system. ### Claim 20 (depends on 19) The system of claim 19, wherein while optimizing the damping factor, the prognostic-surveillance mechanism: uses the spike-detection technique with an initial damping factor to detect and remove the spikes from the original time-series signals to produce despiked original time-series signals; generates despiked synthetic time-series signals, which are statistically indistinguishable from the despiked original time-series signals; includes synthetic spikes in the despiked synthetic time-series signals to produce ground truth synthetic time-series signals, wherein the synthetic spikes have expanded and contracted temporal distributions, amplitude distributions and width distributions with respect to the spikes in the original time-series signals; performs tests, which involve varying the damping factor while using the spike-detection technique to detect spikes in the ground truth synthetic time-series signals; and determines an optimized damping factor based on true and false detections resulting from the tests.