Abstract: A sampling device receives, from each a plurality of transducer computing devices, respective vibration input samples. The sampling device generates a loop buffer corresponding to a most recent threshold number of samples received from each of the plurality of transducer computing devices including first and second transducer devices each located within a predefined proximity to first and second flow tubes of an equipment. The sampling device accesses, from the loop buffer, a most recent first sample logged by the first transducer computing device. The sampling device assigns, using a trained model, the first input sample to a first category of a set of categories. The sampling device accesses, from the loop buffer subsequent to accessing the first sample, a most recent second sample logged by a second transducer computing device and, using the model, assigns the second input sample to a second category of the set of categories.

Abstract: A sampling device receives, from a transducer computing device located within a predefined proximity to an equipment in an operating environment, a vibration sample from the operating environment. The sampling device predicts, using a model, (1) an anomalous designation or a non-anomalous designation for the vibration sample and (2) a cluster assignment, to a particular cluster of a set of clusters, for the vibration sample when the model predicts the non-anomalous designation for the vibration sample. The sampling device transmits, to a computing device of the equipment, instructions to cease to perform the operation responsive to predicting the anomalous designation.

Abstract: A sampling device receives, from each a plurality of transducer computing devices, respective vibration input samples. The sampling device generates a loop buffer corresponding to a most recent threshold number of samples received from each of the plurality of transducer computing devices including first and second transducer devices each located within a predefined proximity to first and second flow tubes of an equipment. The sampling device accesses, from the loop buffer, a most recent first sample logged by the first transducer computing device. The sampling device assigns, using a trained model, the first input sample to a first category of a set of categories. The sampling device accesses, from the loop buffer subsequent to accessing the first sample, a most recent second sample logged by a second transducer computing device and, using the model, assigns the second input sample to a second category of the set of categories.

Abstract: A sampling device receives, from a transducer computing device located within a predefined proximity to an equipment in an operating environment, a vibration sample from the operating environment. The sampling device predicts, using a model, (1) an anomalous designation or a non-anomalous designation for the vibration sample and (2) a cluster assignment, to a particular cluster of a set of clusters, for the vibration sample when the model predicts the non-anomalous designation for the vibration sample. The sampling device transmits, to a computing device of the equipment, instructions to cease to perform the operation responsive to predicting the anomalous designation.

Abstract: A sampling device receives, from a transducer computing device located within a predefined proximity to an equipment in an operating environment, a vibration sample from the operating environment and increments a retrain counter. In response to determining that the incremented retrain counter does not meet or exceed a retrain threshold, the sampling device predicts, using a model, an anomalous or non-anomalous designation for the vibration sample and a cluster assignment, to a particular cluster of a set of clusters, for the vibration sample when the model predicts the non-anomalous designation for the vibration sample. The sampling device receives a subsequent vibration sample and further increments the retrain counter. In response to determining that the further incremented retrain counter exceeds a retrain threshold, the sampling device receives a subsequent set of vibration samples and retrains, using the subsequent vibration sample and the subsequent set of vibration samples, the model.

Abstract: A sampling device receives, from a transducer computing device located within a predefined proximity to an equipment in an operating environment, a vibration sample from the operating environment and increments a retrain counter. In response to determining that the incremented retrain counter does not meet or exceed a retrain threshold, the sampling device predicts, using a model, an anomalous or non-anomalous designation for the vibration sample and a cluster assignment, to a particular cluster of a set of clusters, for the vibration sample when the model predicts the non-anomalous designation for the vibration sample. The sampling device receives a subsequent vibration sample and further increments the retrain counter. In response to determining that the further incremented retrain counter exceeds a retrain threshold, the sampling device receives a subsequent set of vibration samples and retrains, using the subsequent vibration sample and the subsequent set of vibration samples, the model.

Abstract: A data transformation system and method for importing data from an implantable medical device from a particular manufacturer is provided. The data transformation system includes a data transformation engine. The data transformation engine includes a bootstrap subsystem that receives the data in a native format and determines the particular manufacturer. The data transformation engine also includes a data transformation component that categorizes at least some of the data into an object model representation and a data normalization component that normalizes at least some of the data in the object model representation and serializes the object model representation into an extensible markup language file. The data transformation engine further includes a schema transformer that validates the extensible markup language file.