Abstract: A data processing system for generating predictive maintenance models is disclosed, including one or more processors, a memory, and a plurality of instructions stored in the memory. The instructions are executable to receive a historical dataset relating to each system of a plurality of systems, the historical dataset including maintenance data and operational data. The instructions are further executable to evaluate correlation between each of a plurality of operational data features and maintenance events of the maintenance data, using a supervised machine learning classification model. The instructions are further executable to display a quantitative result of the evaluation for each operational data feature in a graphical user interface, receive a selection of one or more operational data features of the plurality of operational data features, and generate a predictive maintenance model using the selected one or more operational data features according to a machine learning method.

Abstract: A fraud prevention system that includes a client server and a fraud prevention server. The fraud prevention server includes an electronic processor and a memory. The memory including a trust scoring service. When executing the trust scoring service, the electronic processor is configured to receive a trust score request of a device from the client server, generate, with a trust model, a trust score of the device, and responsive to generating the trust score, output the trust score to the client server in satisfaction of the trust score request, wherein the trust score is distinct from a risk factor, the trust score representing a predicted trust level of the device, and the risk factor representing a fraud risk level associated with the device based on one or more device behaviors.

Abstract: A data processing system for generating predictive maintenance models is disclosed, including one or more processors, a memory including one or more digital storage devices, and a plurality of instructions stored in the memory. The instructions are executable by the one or more processors to receive a historical dataset relating to each system of a plurality of systems, and including maintenance data and operational data. The instructions are further executable to receive a first selection of a first operational data feature and a first system, and display operational data associated with the first operational data feature and the first system, and maintenance data associated with the first system, on a timeline in a graphical user interface. The instructions are further executable to receive a second selection of a second operational data feature and generate a predictive maintenance model using the second operational data feature according to a machine learning method.

Abstract: A data processing system for generating predictive maintenance models is disclosed, including one or more processors, a memory, and a plurality of instructions stored in the memory. The instructions are executable to receive a historical dataset relating to each of a plurality of systems, including maintenance data and operational data. The instructions are further executable to display one or more algorithm templates and one or more data features calculated from the operational data, in a graphical user interface. The instructions are further executable to receive a selection of an algorithm template, a data feature, and a value of a parameter associated with the algorithm template, and to train and evaluate the selected algorithm template on the selected data feature according to the received value. The instructions are further executable to display a result of a metric of the evaluation, and generate a predictive maintenance model using the selected algorithm template.

Abstract: A data processing system for generating predictive maintenance models is disclosed, including one or more processors, a memory, and a plurality of instructions stored in the memory. The instructions are executable to receive a historical dataset relating to each system of a plurality of systems, the historical dataset including maintenance data and operational data. The instructions are further executable to evaluate correlation between each of a plurality of operational data features and maintenance events of the maintenance data, using a supervised machine learning classification model. The instructions are further executable to display a quantitative result of the evaluation for each operational data feature in a graphical user interface, receive a selection of one or more operational data features of the plurality of operational data features, and generate a predictive maintenance model using the selected one or more operational data features according to a machine learning method.