A method for vehicle diagnostics includes receiving vehicle information from a vehicle computer system and identifying, at the data processing hardware, at least one vehicle module from the vehicle information. The at least one vehicle module represents a detected fault of the vehicle computer system. The method also includes executing a diagnostic program configured to display on a display screen a graphical user interface having a fault topology window associated with at least one detected fault of the at least one vehicle module. The fault topology window has a vehicle information panel and a fault topology view panel. The diagnostic program is configured to receive a detected fault selection input of the at least one detected fault of the at least one vehicle module and display a fault topology view of the at least one detected fault of the at least one vehicle module.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining causal models for controlling environments. One of the methods includes obtaining data specifying baseline probability distributions for each of a plurality of controllable elements; maintaining a causal model; repeatedly performing the following: selecting control settings for the environment based on the causal model and values for a particular internal parameter of the control system that are sampled from a range of possible values; selecting control settings for the environment based on the baseline probability distributions; monitoring environment responses to the control settings selected based on the causal model and the control settings selected based on the baseline probability distributions; determining, for each of the possible values, a measure of a difference between a current system performance and a baseline system performance; and updating how frequently each of the possible values is sampled.

A system is provided for determining causes of faults in a manufacturing system. The system stores data associated with a processing system which includes machines and associated processes, wherein the data includes timestamp information, machine status information, and product-batch information. The system determines, based on the data, a topology of the processing system, wherein the topology indicates flows of outputs between the machines as part of the processes. The system determines information of machine faults in association with the topology. The system generates, based on the machine-fault information, one or more fault parameters which indicates frequency and severity of a respective fault. The system constructs, based on the topology and the machine-fault information, a system model which includes the one or more fault parameters, thereby facilitating diagnosis of the processing system.

A system and methods for multivariant learning and optimization repeatedly generate self-organized experimental units (SOEUs) based on the one or more assumptions for a randomized multivariate comparison of process decisions to be provided to users of a system. The SOEUs are injected into the system to generate quantified inferences about the process decisions. Responsive to injecting the SOEUs, at least one confidence interval is identified within the quantified inferences, and the SOEUs are iteratively modified based on the at least one confidence interval to identify at least one causal interaction of the process decisions within the system. The causal interaction can be used for testing, diagnosis, and optimization of the system performance.

A method is provided for diagnosing a failure on an aircraft that includes aircraft systems configured to report faults to an onboard reasoner. The method includes receiving a fault report at an onboard computer of the aircraft from an aircraft system of the aircraft systems, the fault report indicating failed tests reported by the aircraft system. The onboard reasoner accesses an onboard diagnostic causal model represented by a graph describing known causal relationships between possible failed tests reported by the respective ones of the aircraft systems, and possible failure modes of the respective ones of the aircraft systems. The onboard reasoner diagnoses a failure mode of the aircraft system or another of the aircraft systems, from the failed tests, and using a graph-theoretic algorithm and the onboard diagnostic causal model. A maintenance action is determined for the failure mode, and a maintenance message is generated including the maintenance action.

A method and system for eliminating a fault condition in a technical installation is provided. In one aspect, the method includes predicting an occurrence of the fault condition in at least a portion of the technical installation. The method also includes determining a root cause of the predicted fault condition. Additionally, the method includes identifying one or more mitigation actions to resolve the fault condition. Furthermore, the method includes determining an outcome associated with at least one of the one or more mitigation actions on the technical installation. The method also includes outputting on a device associated with a user at least one mitigation action to be implemented in the technical installation based on the determined impact.

A method for automatically detecting an abnormal process flow for a process in an industrial control system (ICS) comprises providing process flow (PF) strings that define normal PFs for processes in the ICS, each of the PF strings defining a time-ordered sequence of events that is a time-ordered recurring sequence of learned events associated with learned changes between learning values of parameters that affect an operation of the ICS, wherein a respective PF string of the PF strings includes an attributed process flow node that represents an attributed event and one or more attributes that are associated therewith. The method further comprises: obtaining monitoring values of the parameters, analyzing the monitoring values to detect monitored events that are associated with monitored changes between monitoring values of the parameters, and detecting the abnormal process flow upon determining a lack of conformance of a monitored event with one of the PF strings.

In at least one embodiment, a class 7 or 8 vehicle is provided. A first controller is configured to control a vehicle operation and to detect one or more failures related to the vehicle operation. The first controller is configured to transmit first data indicative of the one or more failures. A vehicle interface controller is configured to receive the first data indicative of the one or more failures on the data communication bus and to receive a signal corresponding to at least one of vehicle speed or park brake status. The vehicle interface controller is further configured to retrieve at least one diagnostic screen and to display the at least one diagnostic screen after the signal indicates the at least one of the vehicle speed being equal to a predetermined vehicle speed or the park brake status indicating that a park brake is set in the vehicle.

The present disclosure discloses method and control system for detecting condition of plurality of process equipment in industrial plant. The proposed methodology implements machine learning techniques for detecting the fault. The machine learning techniques include supervised learning and unsupervised learning. Real-time values of plurality of parameters associated with each of the plurality of process equipment along with weight matrix and threshold attribute is used in the unsupervised learning to detect the condition. The weight matrix associated with each of the plurality of process equipment is generated using the supervised learning. Plurality of historic values of the plurality of parameters relating to the corresponding process equipment are analysed in the supervised learning to generate the weight matrix for the corresponding process equipment. The threshold attribute associated with each of the plurality of parameters for the corresponding process equipment is determined using the real-time values of said plurality of parameters.

A system and method of configuring monitor blocks and effect blocks associated with a process control system for a process plant includes causing a display device to display a graphical user interface, the graphical user interface indicating a first monitor block, a second monitor block, and an effect block. The system and method further includes enabling a user to input configuration data via the input device, including: (i) configuring one of the outputs of the first monitor block to serve as one of the inputs of the second monitor block, (ii) configuring an additional one of the outputs of the first monitor block and one of the outputs of the second monitor block to serve as inputs to the effect block, and (iii) designating at least one of the plurality of cells of each of the first monitor block, the second monitor block, and the effect block as a trigger associated with the respective input/output pair for the respective cell and corresponding to a condition in the process plant.