A system and method for unsupervised prediction of machine failures. The method includes monitoring sensory inputs related to at least one machine; analyzing, via at least unsupervised machine learning, the monitored sensory inputs, wherein the output of the unsupervised machine learning includes at least one indicator; identifying, based on the at least one indicator, at least one pattern; and determining, based on the at least one pattern and the monitored sensory inputs, at least one machine failure prediction.

A system and method for unsupervised prediction of machine failures. The method includes monitoring sensory inputs related to at least one machine; analyzing, via at least unsupervised machine learning, the monitored sensory inputs, wherein the output of the unsupervised machine learning includes at least one indicator; identifying, based on the at least one indicator, at least one pattern; and determining, based on the at least one pattern and the monitored sensory inputs, at least one machine failure prediction.

A process control device for use in an industrial process control or automation system of an industrial process plant includes a sensor configured to measure a parameter of a process in the industrial process plant and to output to a controller in the industrial process plant the parameter measured. The process control device also or alternatively includes a control element configured to perform an action in the industrial process plant according to an input received from the controller in the industrial process plant. The process control device also includes an embedded device identifier, unique to the process control field device and associated with one or more of an owner of the process control field device, a plant location of the process control field device, a country or geographical or geopolitical region, and a device tag.

A system and method for unsupervised prediction of machine failures. The method includes monitoring sensory inputs related to at least one machine; analyzing, via at least unsupervised machine learning, the monitored sensory inputs, wherein the output of the unsupervised machine learning includes at least one indicator; identifying, based on the at least one indicator, at least one pattern; and determining, based on the at least one pattern and the monitored sensory inputs, at least one machine failure prediction.

A process plant and industrial control system architecture includes a generalized compute fabric that is agnostic or indifferent to the physical location at which the compute fabric is implemented, includes one or more physical control or field devices located at one or more specific sites at which a product or process is being manufactured and further includes a transport network that securely provides communications between the compute fabric and the pool of physical devices. The compute fabric includes an application layer that includes configured containers or containerized software modules that perform various control, monitoring and configuration activities with respect to one or more devices, control strategies and control loops, sites, plants, or facilities at which control is performed, and includes a physical layer including computer processing and data storage equipment that can be located at any desired location, including at or near a site, plant, or facility at which control is being performed, at a dedicated location away from the location at which control is being performed, in re-assignable computer equipment provided in the cloud, or any combination thereof. This control architecture enables significant amounts of both computer processing and IT infrastructure that is used to support a process plant, an industrial control facility or other automation facility to be implemented in a shared, in an offsite and/or in a virtualized manner that alleviates many of the communications and security issues present in current process and industrial control systems that attempt to implement control with shared or virtualized computing resources set up according to the well-known Purdue model. The industrial control system architecture is protected via more secure and customizable techniques as compared to those used in Purdue model-based control systems. For example, communications between any (and in some cases, all) endpoints of the system may be protected via one or more virtual private networks to which authenticated endpoints must be authorized to access. Endpoints may include, for example, containerized components, physical components, devices, sites or locations, the compute fabric, and the like, and the VPNs may include mutually-exclusive and/or nested VPNs. External applications and services, whether automated or executing under the purview of a person, may access information and services provided by the system via only APIs, and different sets of APIs may be exposed to different users that have been authenticated and authorized to access respective sets of APIs. A configuration system operates within the compute fabric to enable a user to ea

A process control or automation system configured to control a plurality of process control or automation field devices in a process, includes a compute fabric executing a plurality of instantiated micro-encapsulated execution environments (MEEEs) that cooperate to control the process. Each of the plurality of field devices operates to sense a parameter of the process and output the sensed parameter of the process to an input of the compute fabric and/or to affect a parameter of the process according to an input received from the compute fabric. The system also includes a plurality of digital twin services, each instantiated as one or more of the plurality of instantiated MEEEs, each having an associated one or group of field devices, and each operable to mimic non-physical operation of the one or group of field devices.