Abstract: A method includes obtaining data representative of a state or condition of an evaluation target. The method also includes providing first input based on the data to a trained classifier to generate a first result. The method further includes providing second input based on the data to an adaptive neuro-fuzzy inference system to generate a second result. The method also includes assigning a classification to the state or condition of the evaluation target based on the first result and the second result.

Abstract: A method of detecting deviation from an operational state of a device includes obtaining preprocessed data corresponding to data sensed by one or more sensor devices coupled to the device, where obtaining the preprocessed data includes applying a transform to the data sensed by the one or more sensor devices to generate a set of features in a frequency domain. The method also includes processing the preprocessed data using a trained anomaly detection model to generate an anomaly score. The method also includes processing the anomaly score using an alert generation model to determine whether to generate an alert.

Abstract: A method of detecting deviation from an operational state of a device includes obtaining preprocessed data corresponding to data sensed by one or more sensor devices coupled to the device. The method also includes processing the preprocessed data using a trained anomaly detection model to generate an anomaly score. The method also includes processing the anomaly score using an alert generation model to determine whether to generate an alert.

Abstract: A method of detecting deviation from an operational state of a rotational device includes receiving, from one or more sensor devices coupled to the rotational device, frequency domain data indicative of vibration data sensed during a sensing period. The method also includes processing the frequency domain data using a trained anomaly detection model to generate an anomaly score for the sensing period and processing the anomaly score using an alert generation model to determine whether to generate an alert.

Abstract: A method of detecting deviation from an operational state of a device includes obtaining preprocessed data corresponding to data sensed by one or more sensor devices coupled to the device. The method also includes processing the preprocessed data using a trained anomaly detection model to generate an anomaly score. The method also includes processing the anomaly score using an alert generation model to determine whether to generate an alert.

Abstract: A method includes receiving time series source data that is associated with a source asset and that includes a set of classification labels. The method also includes receiving time series target data that is associated with a target asset and that lacks classification labels. The method further includes determining time series representations from the time series source data and the time series target data. The method also includes, based on the set of classification labels included in the time series source data and at least on raw time series data or the time series representations, generating a classifier operable to classify unlabeled data associated with the target asset. The raw time series data includes the time series source data and the time series target data.