A system and method for detecting and fixing robotic process automation failures, including collecting tasks from at least one client computerized device, processing the tasks via robotic process automation, collecting tasks that failed to complete per task type, recording successful execution steps per each of the failed tasks, evaluating the recorded successful execution steps with respect to the failed task types, and providing selected execution steps that best fix the failed tasks, thereby fixing the robotic process automation failures.

Embodiments of systems and methods for platform framework error handling are described. A platform framework may receive registration requests from framework participants that provide operation of a plurality of hardware devices of an IHS (Information Handling System). The framework registration requests by participants specify remediation policies for addressing error conditions related to respective participants. The received remediation policies are mapped to the registered participants, where remediation policies may include handles for invoking remediation procedures for a registered participant. Error conditions are detected during operation of the platform framework. The registered participant is identified as a source of the error condition and a remediation policy that is mapped to the registered participant is identified. Handles in the remediation policy are used to invoke remediation procedures for the registered participant. Remediation procedures invoked by the handles may be provided by a remediation agent that provides support for registered participants.

Methods and articles of manufacture for hosting a safety critical application on an uncontrolled data processing device are provided. Various combinations of installation, functional, host integrity, coexistence, interoperability, power management, and environment checks are performed at various times to determine if the safety critical application operates properly on the device. The operation of the SCA on the UDPD may be controlled accordingly.

A system, method, and computer-readable medium are disclosed for performing a customer operating system installation operation. The customer operating system installation operation includes performing a customer operating system installation operation onto an information handling system, comprising: performing a customer operating system installation operation; and, performing a UEFI boot entry operation, the UEFI boot entry operation accessing a UEFI boot entry when performing the customer operating system installation operation, the UEFI boot entry operation providing a communication abstraction between a manufacturing operating system and the customer operating system.

A safety module having a plurality of microcontrollers receives an analog input and determines a value of the analog input. The microcontrollers each determine a respective ternary state of the device by identifying, from three candidate ranges of values, a range of values in which the value falls, wherein at least two of the plurality of microcontrollers uses different candidate ranges of values, determining, based on the identified range, a ternary state corresponding to the range, and assigning the determined ternary state as the respective ternary state. The safety module determines whether the ternary states from the two microcontrollers map to a fault state, and, where they do, cause a command a command to be output to the device to enter a safe state.

Systems and methods for determining a source of anomaly in a cyber-physical system (CPS). A forecasting tool can obtain a plurality of CPS feature values during an input window and forecast the plurality of CPS feature values for a forecast window. An anomaly identification tool can determine a total forecast error for the plurality of CPS features in the forecast window, identify an anomaly in the cyber-physical system when the total forecast error exceeds a total error threshold, and identify at least one CPS feature as the source of the anomaly.

Provided is a log analysis system including: an identifying unit that identifies transactions from logs output from a device; a grouping unit that categorizes the transactions having both the same log related to start and the same log related to end into the same group; a learning unit that creates a learning model that defines the number of occurrences on a log type basis in the transactions of the same group; and an inspection unit that inspects a transaction of an inspection target based on the learning model.

A method is provided for diagnostic checking of a variable memory 14 in a safety critical system in order to detect variable memory failures; wherein the safety critical system comprises a central processing unit (CPU) with an operating system, an internal volatile memory 12 and an external volatile memory 14 including the variable memory 14; and the CPU can access a plurality of address spaces including one or more address spaces of the external volatile memory 14 that are utilised by the operating system and/or by a safety critical application of the safety critical system during normal use of the safety critical system.

Automated recovery of execution roles in a distributed historian system in accordance with actions and rules customized to each execution role. A monitoring service monitors the health status of execution roles and automatically performs a corrective action in response to the health state of an execution role triggering a predetermined rule.

An industrial asset may have a plurality of monitoring nodes, each monitoring node generating a series of monitoring node values over time representing current operation of the industrial asset. An abnormality detection computer may determine that an abnormal monitoring node is currently being attacked or experiencing a fault. An autonomous, resilient estimator may continuously execute an adaptive learning process to create or update virtual sensor models for that monitoring node. Responsive to an indication that a monitoring node is currently being attacked or experiencing a fault, a level of neutralization may be automatically determined. The autonomous, resilient estimator may then be dynamically reconfigured to estimate a series of virtual node values based on information from normal monitoring nodes, appropriate virtual sensor models, and the determined level of neutralization. The series of monitoring node values from the abnormal monitoring node or nodes may then be replaced with the virtual node values.