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 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: 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.