Abstract: A solenoid actuator includes a stator assembly with a stator core of formed powder metal received in a stator housing. A ferromagnetic protective sleeve is in contact with and covers a majority of an inner end face and a cylindrical wall of the stator core, while a flux ring is in contact with and covers an outer end face of the stator core. An armature assembly includes an armature attached to a stem that is movable in an air gap relative to the ferromagnetic protective sleeve. A spring is operably positioned in the ferromagnetic protective sleeve but electrically isolated from the stator housing. The stator core is encapsulated to protect against erosion and fragmentation. A magnetic flux line around a solenoid coil passes through the stator core, the ferromagnetic protective sleeve, the armature, the flux ring and back to the stator core.

Abstract: A solenoid actuator includes a stator assembly with a stator core of formed powder metal received in a stator housing. A ferromagnetic protective sleeve is in contact with and covers a majority of an inner end face and a cylindrical wall of the stator core, while a flux ring is in contact with and covers an outer end face of the stator core. An armature assembly includes an armature attached to a stem that is movable in an air gap relative to the ferromagnetic protective sleeve. A spring is operably positioned in the ferromagnetic protective sleeve but electrically isolated from the stator housing. The stator core is encapsulated to protect against erosion and fragmentation. A magnetic flux line around a solenoid coil passes through the stator core, the ferromagnetic protective sleeve, the armature, the flux ring and back to the stator core.

Abstract: A method and framework are described for detecting changes in a multivariate data stream. A training set is formed by sampling time windows in a data stream containing data reflecting normal conditions. A histogram is created to summarize each window of data, and data within the histograms are clustered to form test distribution representatives to minimize the bulk of training data. Test data is then summarized using histograms representing time windows of data and data within the test histograms are clustered. The test histograms are compared to the training histograms using nearest neighbor techniques on the clustered data. Distances from the test histograms to the test distribution representatives are compared to a threshold to identify anomalies.

Abstract: A method and framework are described for detecting changes in a multivariate data stream. A training set is formed by sampling time windows in a data stream containing data reflecting normal conditions. A histogram is created to summarize each window of data, and data within the histograms are clustered to form test distribution representatives to minimize the bulk of training data. Test data is then summarized using histograms representing time windows of data and data within the test histograms are clustered. The test histograms are compared to the training histograms using nearest neighbor techniques on the clustered data. Distances from the test histograms to the test distribution representatives are compared to a threshold to identify anomalies.