Abstract: Techniques for managing machine operations using encoded multi-scale time series data are provided. In one technique, operational data is received from a sensor coupled to an industrial device. For each portion in a first set of portions of the operational data (where each portion corresponds to a first time scale), first aggregated data is generated based on time series data from that portion and a first encoding is generated based on the first aggregated data. For each portion in a second set of portions of the operational data (where each portion of the second set corresponds to a second time scale that is different than the first time scale), second aggregated data is generated based on time series data from that portion and a second encoding is generated based on the second aggregated data. The operational data is classified to determine a condition of the industrial device during the time interval based on the first and second encodings.

Abstract: A system and methods to identify which signals are significant to an assessment of a complex machine system state in the presence of non-linearities and disjoint groupings of condition types. The system enables sub-grouping of signals corresponding to system sub-components or regions. Explanations of signal significance are derived to assist in causal analysis and operational feedback to the system is prescribed and implemented for the given condition and causality.

Abstract: A computer system for managing a machine learning model that detects potential anomalies in the operation of a complex system is disclosed. In some embodiments, the computer system is programmed to receive sensor signal data originally produced by sensors of the complex system. The sensor signal data can include values for multiple sensor signals at multiple resolutions. The computer system is programmed to train, from given sensor signal data, the machine learning model that comprises one or more transformers, each transformer capturing a set of relationships between signals in a predetermined group of signals. During training, the computer system is programmed to also establish an expected range for an indicator of the relationship. The computer system is programmed to then execute the machine learning model on new sensor signal data and take remedial steps when any computed indicator falls outside the expected range, indicating a potential anomaly in the operation of the complex system.

Abstract: Apparatus and methods for non-invasively monitoring an oscillation signal in an effort to provide a more reliable oscillation signal. An example oscillation circuit generally includes an oscillator configured to generate an oscillation signal, the oscillator comprising an oscillator core circuit for coupling to a resonator and configured to generate the oscillation signal to enable the resonator to resonate and an adjustable current source coupled to the oscillator core circuit and configured to control an amplitude of the oscillation signal; a first automatic gain control (AGC) circuit having an input coupled to an output of the oscillator and having an output coupled to a control input of the adjustable current source; a second AGC circuit configured to replicate the first AGC circuit; and logic having a first input coupled to the output of the first AGC circuit and having a second input coupled to an output of the second AGC circuit.

Abstract: Apparatus and methods for non-invasively monitoring an oscillation signal in an effort to provide a more reliable oscillation signal. An example oscillation circuit generally includes an oscillator configured to generate an oscillation signal, the oscillator comprising an oscillator core circuit for coupling to a resonator and configured to generate the oscillation signal to enable the resonator to resonate and an adjustable current source coupled to the oscillator core circuit and configured to control an amplitude of the oscillation signal; a first automatic gain control (AGC) circuit having an input coupled to an output of the oscillator and having an output coupled to a control input of the adjustable current source; a second AGC circuit configured to replicate the first AGC circuit; and logic having a first input coupled to the output of the first AGC circuit and having a second input coupled to an output of the second AGC circuit.

Abstract: Techniques for managing machine operations using encoded multi-scale time series data are provided. In one technique, operational data is received from a sensor coupled to an industrial device. For each portion in a first set of portions of the operational data (where each portion corresponds to a first time scale), first aggregated data is generated based on time series data from that portion and a first encoding is generated based on the first aggregated data. For each portion in a second set of portions of the operational data (where each portion of the second set corresponds to a second time scale that is different than the first time scale), second aggregated data is generated based on time series data from that portion and a second encoding is generated based on the second aggregated data. The operational data is classified to determine a condition of the industrial device during the time interval based on the first and second encodings.

Abstract: A system and method to identify patterns in sets of signals produced during operation of a complex system and combines the identified patterns with records of past conditions to generate operational feedback to one or more machines of the complex system while it operates.

Abstract: A system and method to identify patterns in sets of signals produced during operation of a complex system and combines the identified patterns with records of past conditions to generate operational feedback to one or more machines of the complex system while it operates.

Abstract: A system and methods to identify which signals are significant to an assessment of a complex machine system state in the presence of non-linearities and disjoint groupings of condition types. The system enables sub-grouping of signals corresponding to system sub-components or regions. Explanations of signal significance are derived to assist in causal analysis and operational feedback to the system is prescribed and implemented for the given condition and causality.