A method of trend analysis and automatic tuning of alarm parameters for a machine is provided. The method includes obtaining condition related measurements of the machine, checking a Condition Indicator (CI) value with respect to a set threshold, calculating the number of times the value is above the threshold during the N last measurements, displaying the number of times the value is above the threshold during the N last measurements in a diagram, triggering the alarm if the value has been above the threshold more times than the alarm level during the N last measurements, comparing historical measurement data when each alarm triggered with defects recorded, correlating a relationship between the alarms triggered and the defects detected, counting the number of true positives, false negatives and false positives from the current measurement data, comparing the number of counted true positives, false negatives and false positives with the acceptable defined limits.

A system and method for allocating machine behavioral models. The method includes analyzing, via unsupervised machine learning, a plurality of sensory inputs associated with a machine, wherein the unsupervised machine learning outputs at least one normal behavior pattern of the machine; selecting, based on the output at least one normal behavior pattern, at least one machine behavioral model; generating, based on the selected at least one machine behavioral model, an optimal machine behavioral model representing behavior of the machine; and allocating the generated optimal machine behavioral model to the machine.

A system and computer-implemented method for generating real-time performance advisories for a centrifugal compressor of a fleet of centrifugal compressors are provided. The method includes receiving an actual thermodynamic signature of the compressor, that is unique to the compressor, receiving compressor process parameter values during operation of the compressor, determining, in real-time, an actual performance of the compressor using the compressor process parameter values, determining, in real-time, a predicted performance of the compressor using the received actual thermodynamic signature of the compressor, determining a performance deviation of the compressor using the actual performance and the predicted performance, comparing the performance deviation to a predetermined dynamic threshold range of performance deviation specific to operating speed, and generating a notification to a user using the comparison.

Systems and methods for automatically generating a set of meta-parameters used to train invariant-based anomaly detectors are provided. Data is transformed into a first set of time series data and a second set of time series data. A fitness threshold search is performed on the first set of time series data to automatically generate a fitness threshold, and a time resolution search is performed on the set of second time series data to automatically generate a time resolution. A set of meta-parameters including the fitness threshold and the time resolution are sent to one or more user devices across a network to govern the training of an invariant-based anomaly detector.

Described herein are systems and methods for real-time fault detection in electrical circuits. Various embodiments provide a fault detection circuit that uses a resistor network that is controlled to detect an internal current leak in multiple directions, e.g., to ground or to a power supply. The magnitude of the leakage current may be estimated from voltage measurements at voltage pins. In addition, as part of circuit diagnostics, open and short circuit fault conditions may be identified by using current sources and measuring deflections at the voltage pins.

A diagnostic method localizes technical faults in a motion system that includes a base adapted to receive a motion stage for equipment, a machine frame resting on the floor, dampers adapted to support the base, and an active isolation system arranged between the base and the machine frame. The active isolation system and the base form a mechanical system. The active isolation system includes actuators, adapted to impart six degree-of-freedom (DOF) motion to the base in a reference frame, and inertial sensors adapted to provide a six DOF measurement of the base's motion. The method includes: i) applying a control signal for actuating or contributing to the actuation of the actuators of the active isolation system to impart a motion to the base; ii) obtaining, with the inertial sensors, a six DOF measurement of the base's motion relative to a reference point; iii) creating a measured process sensitivity matrix of the mechanical system using the six DOF measurement; and iv) determining, based on the measured process sensitivity matrix, whether all the actuators and sensors of the active isolation system are working as expected and/or whether there is a pivot point impeding the movement of the base.

A computer-readable medium may include instructions that may cause a processor to perform operations that may include receiving audio data representative of sound waves generated by industrial devices and extracting features from the audio data. The features may be representative of a portion of the audio data. The operations may also include identifying a subset of the features based on distances between each of the plurality of features in an information space. The information space may include known clusters. The operations may then include determining that the subset of the features corresponds to an unknown cluster in the information space, performing a constrained classification operation based on each feature of the subset of the features to identify a new known cluster for the information space, and modifying operations of the industrial devices based on the new known cluster.

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.

A computer-implemented method, system, and computer program product are provided. A plurality of maintenance messages (MMSGs) are identified. Each MMSG is associated with at least one shut-off valve. A sensor parameter is identified based on an analysis of sensor parameters associated with the shut-off valves of each MMSG. A threshold value for the sensor parameter is identified as being associated with abnormal operation of the respective shut-off valves. A sensor associated with a first shut-off valve captures values for the sensor parameter during a first and second predefined time period, the first and second predefined time periods associated with an opening and a closing of the first shut-off valve. Upon determining that a difference between the maximum values of the sensor values captured during the first and second predefined time periods exceeds the first threshold value, a determination is made that the first shut-off valve is operating abnormally.

A testing system computer device for dynamically updating a test plan of an apparatus includes at least one processor in communication with at least one memory device. The testing system computer device is configured to store a plurality of historical data and generate a simulation model of the apparatus based in part on the historical data. The simulation model includes a plurality of inputs and a plurality of outputs of the apparatus. The testing system computer device is also configured to determine a plurality of tests to perform on the apparatus based on the simulation model and the plurality of historical data. The testing system computer device is further configured to receive a plurality of desirability ratings from a user, rank the plurality of tests to perform based on the plurality of desirability ratings, and present the ranked plurality of tests to the user.