Abstract: The disclosed embodiments provide a system that detects sensor anomalies in a univariate time-series signal. During a surveillance mode, the system receives the univariate time-series signal from a sensor in a monitored system. Next, the system performs a staggered-sampling operation on the univariate time-series signal to produce N sub-sampled time-series signals, wherein the staggered-sampling operation allocates consecutive samples from the univariate time-series signal to the N sub-sampled time-series signals in a round-robin ordering. The system then uses a trained inferential model to generate estimated values for the N sub-sampled time-series signals based on cross-correlations with other sub-sampled time-series signals. Next, the system performs an anomaly detection operation to detect incipient sensor anomalies in the univariate time-series signal based on differences between actual values and the estimated values for the N sub-sampled time-series signals.

Abstract: The disclosed system produces synthetic signals for testing machine-learning systems. During operation, the system generates a set of N composite sinusoidal signals, wherein each of the N composite sinusoidal signals is a combination of multiple constituent sinusoidal signals with different periodicities. Next, the system adds time-varying random noise values to each of the N composite sinusoidal signals, wherein a standard deviation of the time-varying random noise values varies over successive time periods. The system also multiplies each of the N composite sinusoidal signals by time-varying amplitude values, wherein the time-varying amplitude values vary over successive time periods. Finally, the system adds time-varying mean values to each of the N composite sinusoidal signals, wherein the time-varying mean values vary over successive time periods.

Abstract: Systems, methods, and other embodiments associated with acoustic fingerprint identification of devices are described. In one embodiment, a method includes generating a target acoustic fingerprint from acoustic output of a target device. A similarity metric is generated that quantifies similarity of the target acoustic fingerprint to a reference acoustic fingerprint of a reference device. The similarity metric is compared to a threshold. In response to a first comparison result of the comparing of the similarity metric to the threshold, the target device is indicated to match the reference device. In response to a second comparison result of the comparing of the similarity metric to the threshold, it is indicated that the target device does not match the reference device.

Abstract: The disclosed embodiments provide a system that performs seasonality-compensated prognostic-surveillance operations for an asset. During operation, the system obtains time-series sensor signals gathered from sensors in the asset during operation of the asset. Next, the system identifies seasonality modes in the time-series sensor signals. The system then determines frequencies and phase angles for the identified seasonality modes. Next, the system uses the determined frequencies and phase angles to filter out the seasonality modes from the time-series sensor signals to produce seasonality-compensated time-series sensor signals. The system then applies an inferential model to the seasonality-compensated time-series sensor signals to detect incipient anomalies that arise during operation of the asset. Finally, when an incipient anomaly is detected, the system generates a notification regarding the anomaly.

Abstract: We disclose a system that executes an inferential model in VRAM that is embedded in a set of graphics-processing units (GPUs). The system obtains execution parameters for the inferential model specifying: a number of signals, a number of training vectors, a number of observations and a desired data precision. It also obtains one or more formulae for computing memory usage for the inferential model based on the execution parameters. Next, the system uses the one or more formulae and the execution parameters to compute an estimated memory footprint for the inferential model. The system uses the estimated memory footprint to determine a required number of GPUs to execute the inferential model, and generates code for executing the inferential model in parallel while efficiently using available memory in the required number of GPUs. Finally, the system uses the generated code to execute the inferential model in the set of GPUs.

Abstract: Systems, methods, and other embodiments associated with detecting feedback control instability in computer thermal controls are described herein. In one embodiment, a method includes executing a workload on the computing system, wherein the workload varies between a minimum and a maximum at a workload frequency. The method includes recording thermal telemetry from the computing system during execution of the workload. The method includes converting the recorded thermal telemetry into a frequency domain. The method includes detecting whether thermal control of the computing system exhibits feedback control instability based on dissimilarity in the frequency domain between the transformed thermal telemetry and the workload frequency. And, the method includes generating an electronic alert that indicates whether the thermal control of the computing device exhibits the feedback control instability.

Abstract: Systems, methods, and other embodiments associated with auditing the results of a machine learning model are described. In one embodiment, a method accesses original time series data and machine learning estimates of the original time series data. The method generates reconstituted time series data from the machine learning estimates by reversing operations of a machine learning model trained for generating the machine learning estimates from the original time series data. The method detects tampering (or corruption) in the original time series data based on a difference between the original time series data and reconstituted time series data. And, the method generates an electronic verification report that indicates whether the tampering (or corruption) is detected in the original time series data.

Abstract: A double-blind comparison is performed between prognostic-surveillance systems, which are located on a local system and a remote system. During operation, the local system inserts random faults into a dataset to produce a locally seeded dataset, wherein the random faults are inserted into random signals at random times with variable fault signatures. Next, the local system exchanges the locally seeded dataset with a remote system, and in return receives a remotely seeded dataset, which was produced by the remote system by inserting different random faults into the same dataset. Next, the local system uses a local prognostic-surveillance system to analyze the remotely seeded dataset to produce locally detected faults. Finally, the local system determines a performance of the local prognostic-surveillance system by comparing the locally detected faults against actual faults in the remotely seeded fault information. The remote system similarly determines a performance of a remote prognostic-surveillance system.

Abstract: Systems, methods, and other embodiments associated with detecting feedback control instability in computer thermal controls are described herein. In one embodiment, a method includes for a set of dwell time intervals, wherein the dwell time intervals are associated with a range of periods of time from an initial period to a base period, executing a workload that varies from minimum to maximum over the period on a computer during the dwell time interval; recording telemetry data from the computer during execution of the workload; incrementing the period towards a base period; determining that either the base period is reached or a thermal inertia threshold is reached; and analyzing the recorded telemetry data over the set of dwell time intervals to either (i) detect presence of a feedback control instability in thermal control for the computer; or (ii) confirm feedback control stability in thermal control for the computer.

Abstract: In one embodiment, a method for auditing the results of a machine learning model includes: retrieving a set of state estimates for original time series data values from a database under audit; reversing the state estimation computation for each of the state estimates to produce reconstituted time series data values for each of the state estimates; retrieving the original time series data values from the database under audit; comparing the original time series data values pairwise with the reconstituted time series data values to determine whether the original time series and reconstituted time series match; and generating a signal that the database under audit (i) has not been modified where the original time series and reconstituted time series match, and (ii) has been modified where the original time series and reconstituted time series do not match.

Abstract: The disclosed embodiments relate to a system that characterizes susceptibility of an inferential model to follow signal degradation. During operation, the system receives a set of time-series signals associated with sensors in a monitored system during normal fault-free operation. Next, the system trains the inferential model using the set of time-series signals. The system then characterizes susceptibility of the inferential model to follow signal degradation. During this process, the system adds degradation to a signal in the set of time-series signals to produce a degraded signal. Next, the system uses the inferential model to perform prognostic-surveillance operations on the set of time-series signals with the degraded signal. Finally, the system characterizes susceptibility of the inferential model to follow degradation in the signal based on results of the prognostic-surveillance operations.

Abstract: Systems, methods, and other embodiments for passive component (e.g., spychip) detection through polarizability and advanced pattern recognition are described. In one embodiment a method includes applying an electromagnetic field to a target electronic system while the target electronic system is emitting a test pattern of electromagnetic interference. The method takes measurements of combined electromagnetic field strength emitted by the target electronic system while the electromagnetic field is being applied. The method detects the passive component based on dissimilarity between the measurements and estimates of electromagnetic field strength for the test pattern for a golden electronic system. The golden electronic system is of similar construction to the target electronic system and does not include the passive component. The method generates an electronic alert that the passive component is present in the target electronic system.

Abstract: A system is provided for training an inferential model based on selected training vectors. During operation, the system receives training data comprising observations for a set of time-series signals gathered from sensors in a monitored system during normal fault-free operation. Next, the system divides the observations into N subgroups comprising non-overlapping time windows of observations. The system then selects observations with a local minimum value and a local maximum value for all signals from each subgroup to be training vectors for the inferential model. Finally, the system trains the inferential model using the selected training vectors. Note that by selecting observations with local minimum and maximum values to be training vectors, the system maximizes an operational range for the training vectors, which reduces clipping in estimates subsequently produced by the inferential model and thereby reduces false alarms.

Abstract: Embodiments for passive spychip detection through polarizability and advanced pattern recognition are described. For example a method includes inducing a magnetic field in a passive component of a target system while the target system is emitting EMI with changes in amplitude repeating at a time interval; generating a time series of measurements of a combined magnetic field strength of the induced magnetic field and the EMI; executing a frequency-domain to time-domain transformation on the time series of measurements to create time series signals of combined magnetic field strength over time at a specific frequency range; monitoring the time series signals with an ML model trained to predict correct signal values to determine whether predicted and measured values of the time series agree; and indicating that the target device may contain a passive spychip where anomalies are detected, and is free of passive spychips where no anomalies are detected.

Abstract: Systems, methods, and other embodiments associated with autonomous discrimination of operation vibration signals are described herein. In one embodiment, a method includes automatically choosing a plurality of vibration frequencies that vary in correlation with variation of a load on a monitored device. Vibration amplitudes for the plurality of vibration frequencies are monitored for incipient failure using a machine learning model. The machine learning model is trained to expect the vibration amplitudes to be consistent with undegraded operation of the monitored device. The incipient failure is detected where vibration amplitudes are not consistent with undegraded operation of the monitored device. An alert is then transmitted to suggest maintenance to prevent the incipient failure of the monitored device.

Abstract: Systems, methods, and other embodiments associated with acoustic detection of changes in mass of cargo carried by a vehicle are described herein. In one example method for acoustic cargo surveillance, a first acoustic output of a vehicle carrying cargo at a first time of surveillance of the vehicle is recorded. Then, a second acoustic output of the vehicle at a subsequent time in the surveillance of the vehicle carrying the cargo is recorded. A change in a mass of the cargo carried by the vehicle is acoustically detected based at least on an acoustic change between the first acoustic output and the second acoustic output. An electronic alert is generated that the mass of the cargo has changed based on the acoustic change.

Abstract: Systems, methods, and other embodiments associated with acoustic detection of disguised vehicles are described. In one embodiment of a method for acoustic detection of disguised vehicles, a first acoustic output of a target vehicle that appears to be of a first type is recorded. A second acoustic output of a reference vehicle that is known to be of the first type is retrieved. It is acoustically detected that the target vehicle is not of the first type based at least on an acoustic dissimilarity between the first acoustic output and the second acoustic output. An electronic alert is then generated that the target vehicle is of a second type that is disguised as the first type based on the acoustic dissimilarity.

Abstract: Systems, methods, and other embodiments associated with acoustic fingerprint identification of devices are described. In one embodiment, a method includes generating a target acoustic fingerprint from acoustic output of a target device. A similarity metric is generated that quantifies similarity of the target acoustic fingerprint to a reference acoustic fingerprint of a reference device. The similarity metric is compared to a threshold. In response to a first comparison result of the comparing of the similarity metric to the threshold, the target device is indicated to match the reference device. In response to a second comparison result of the comparing of the similarity metric to the threshold, it is indicated that the target device does not match the reference device.

Abstract: Systems, methods, and other embodiments associated with eviction of weakly correlated signals from collections are described. In one embodiment, a mock signal that has random signal properties is generated. A mock correlation coefficient between the mock signal and a measured time series signal from a collection of measured time series signals is then generated. A discrimination value that indicates a weak signal correlation is then selected, based at least in part on the mock correlation coefficient. A first measured signal is then identified from the collection of measured time series signals that has the weak signal correlation by determining that a first correlation coefficient between the first measured signal and a second measured signal is weak based on the discrimination value. The first measured signal is then evicted from the collection of signals in response to the determination that the first measured signal has the weak signal correlation.

Abstract: The disclosed embodiments relate to a system that automatically adapts a prognostic-surveillance system to account for aging phenomena in a monitored system. During operation, the prognostic-surveillance system is operated in a surveillance mode, wherein a trained inferential model is used to analyze time-series signals from the monitored system to detect incipient anomalies. During the surveillance mode, the system periodically calculates a reward/cost metric associated with updating the trained inferential model. When the reward/cost metric exceeds a threshold, the system swaps the trained inferential model with an updated inferential model, which is trained to account for aging phenomena in the monitored system.