System and methods for learning data patterns predictive of an outcome are described. An example system may include a plurality of input sensors communicatively coupled to a controller; a data collection circuit structured to collect output data from the plurality of input sensors; and a machine learning data analysis circuit structured to receive the output data, learn received output data patterns indicative of an outcome, and learn a preferred input data collection band among a plurality of available input data collection bands. The machine learning data analysis circuit may be structured to learn received output data patterns by being seeded with a model based on industry-specific feedback. The outcome may be at least one of: a reaction rate, a production volume, or a required maintenance.

A combination of a component-based automation framework, software-based redundancy patterns, and a distributed, reliable runtime manager, is able to detect host failures and to trigger a reconfiguration of the system at runtime. This combined solution maintains system operation in case a fault occurs and, in addition, automatically restores fault tolerance by using backup contingency plans, and without the need for operator intervention or immediate hardware replacement. A fault-tolerant fault tolerance mechanism is thus provided, which restores the original level of fault tolerance after a failure has occurredautomatically and immediately, i.e., without having to wait for a repair or replacement of the faulty entity. In short, the invention delivers increased availability or uptime of a system at reduced costs and complexity for an operator or engineer by adapting automatically to a new environment.

The monitoring and control system is configured to obtain a hook load data at predetermined time intervals, and determine variation of hook load between the predetermined time intervals using the hook load data obtained. The monitoring and control system further determines a slip status of a drill string corresponding to each of the variation of hook load. The slip status is determined by comparing each of the variation of hook load with a threshold value of noise. The threshold value of noise is determined based on predetermined parameters of the drill string. The slip status of the drill string corresponding to each of the variation of hook load may be verified based on one or more predetermined conditions. The slip status of the drill string corresponding to each of the variation of hook load may be corrected by adjusting predetermined parameters, based on verification of one or more predetermined conditions.

A controller has an encoder that outputs four-phase pulse signals according to a rotation of a rotor of a motor by a rule. During a rotational drive of the motor, when (i) an abnormal pulse state is observed in which the pulse signal is output in a non-compliant manner with the rule and (ii) a lapse time from a last normal output timing, which is a last timing of an output of the pulse signal by the rule, is longer than a threshold determination time, it is conclusively determined that the encoder has abnormality. Thus, the encoder is provided with an improved noise-proof character, and is prevented from being falsely determined as abnormal due to the abnormal pulse state, even when an output of the pulse signal from the encoder is temporarily ridden by a noise.

A computer-facilitated method and a computerized system for providing optimization or improvement measures for one or more buildings are disclosed. Based on asset data regarding the building and on corresponding performance data, improvement measures related to a consumable resource in the one or more buildings are determined using a computer system configured for analyzing the asset data and the respective corresponding performance data based on internal and/or external key performance indicators and rules provided by a database.

A controller according to an embodiment includes a main processing unit and a monitoring unit. The main processing unit executes a control program. The monitoring unit monitors the main processing unit by a first monitoring method for transmitting, to the main processing unit, a transmission content corresponding to a question and for evaluating an answer to the question from the main processing unit. The main processing unit causes, when receiving an updating request of the control program, the monitoring unit to perform switching from the first monitoring method into a second monitoring method for monitoring a watchdog signal of the main processing unit.

A method of assisted machine learning for condition monitoring for process equipment or process health includes providing a subject matter expert (SME) assisted monitoring rule generation algorithm for generating mathematical monitoring rules. The algorithm implements receiving SME rating instructions whether to include or ignore each of a plurality of time-series data samples which include at least one process parameter in a pattern, a time stamp and the process equipment the data is sensed from and the equipment's location in the process to provide SME selected time-series data samples, and an initial first rule precursor. Rule results are generated from running the initial first rule precursor on the data samples. Rule results are compared to the SME rating instructions to provide an agreement or disagreement finding. At least once a received change from the SME is implemented which modifies the initial first rule precursor to generate a first mathematical monitoring rule.

A method for performing configuration of a control unit test system with hardware components connected thereto, wherein control units can be tested with the test system in an environment simulated by the test system by means of a model, and wherein the test system comprises at least one computer, in particular a computer executing the model, as well as hardware components, connected to one another by means of at least one network, in which at least a portion of the hardware components comprises a dedicated server (MIS) that, by means of communication, provides access to the configuration data associated with the hardware component, in particular stored in the hardware component, and the model and/or the hardware component is adapted, in particular configured, as a function of the configuration data that are made accessible.

The systems and methods disclosed herein include an assessment system and process for assessing an industrial machine and its various sections, sub-sections, and parts. In embodiments, the assessment system includes an assessment device that includes an assessment overviewer and a part assessor. The assessment overviewer provides an assessor with a selectable industrial machine schematic that illustrates the assessment status of each section of the industrial machine, such as by changing the appearance of each section based its assessment status. The part assessor provides a part assessment interface with engineering instructions and a part grading user interface that provides for a comparison of digital images to complete the part grading.

A system and method for monitoring an air filtering system are disclosed. The system includes at least one station system attached to the air filtering system configured to monitor the air filtering system. The at least one station system includes an air filter microprocessor and an air filtering sensor to determine various aspects associated with the air filtering system and output sensed data. At least one location system is in communication with the at least one station system and also includes a location display for outputting and rendering a location graphical user interface based on the sensed data. At least one remote system is also in communication with the at least one station system and is configured to monitor and interact with the at least one location system and includes a remote display coupled to the remote microprocessor for outputting and rendering a remote graphical user interface based on the sensed data.