A control apparatus for controlling an industrial machine includes a deterioration diagnostic unit that has a diagnostic function, which is repeatedly executed at predetermined timing, for diagnosing deterioration of each component of the industrial machine and outputs an alarm signal in response to deterioration equal to or more than a predetermined level in each component, an alarm control unit that outputs a stop command to the industrial machine based on the alarm signal and informs that the industrial machine is in an alarm stop state, and a cancellation operation receiving unit that receives the cancellation operation for cancelling the alarm stop state regardless of deterioration of each component. The deterioration diagnostic unit cancels the alarm stop state brought on by the diagnostic function based on the cancellation operation, and allows the industrial machine to operate until the diagnostic function is executed next.

A system for controlling automation includes a machine which collects data generated by performance of an operation by the machine. A user device displays a machine control interface (MCI) corresponding to the machine. The MCI displays the collected data to a touch interface of the user device, and defines at least one touch activated user interface element (UIE) for manipulating the data. The user device can be enabled as an automation human machine interface (HMI) device for controlling an operation performed by the machine, such that a touch action applied to a UIE of the MCI controls the operation. A prerequisite condition to enabling the user device as an automation HMI device can include activation of an enabling switch selectively connected to the user device. The MCI can be stored in a memory of the enabling switch and retrieved from the enabling switch by the user device.

The confidentiality of data is maintained in a case where analysis of an operation state of a facility is entrusted to the outside. Time difference information in operation between the manufacturing apparatuses (RB1), (RB2), . . . , arranged on the production line (LN) is stored for each of the apparatuses, in an inter-apparatus time difference information storage unit (33). At the occurrence of a failure in one of the manufacturing apparatuses (RB1), (RB2), . . . , the operation estimation unit (13) selects, from among the peripheral apparatuses on the upstream side and the downstream side with respect to the manufacturing apparatus concerned, one on the upstream side and one on the downstream side, estimates an operation period of each selected peripheral apparatus associated with the failure, reads log data corresponding to the estimated operation period from the operation history storage unit (31), and transmits the read piece to the external support center (SC).

According to an embodiment, an error log list and alarm service system comprises an execution terminal receiving an alarm including error information according to an operation error in a machine facility designated through an error log list and alarm service platform and a monitoring server collecting a result of monitoring an operation state and operation error in the machine facility, extracting the error information according to an information setting value set by the execution terminal, and providing the extracted error information through a web or app platform.

Tools and techniques are described to automate line testing when wiring devices (such as equipment and sensors) to controllers. Controllers have access to databases of the devices that are controlled by them, including wiring diagrams and protocols, such that the controller can automatically check that each wire responds correctly to stimulus from the controller. After testing, a reporting device rapidly shows the results of the line testing.

An abnormality analysis device including: an overall information obtainer that obtains overall information indicating an overall feature amount of a manufacturing system; an overall abnormal degree calculator that calculates an overall abnormal degree that is an abnormal degree of a whole of the manufacturing system by statistically processing the overall information; an individual information obtainer that obtains individual information indicating a feature amount of each of the plurality of constituent elements; an individual abnormal degree calculator that calculates an individual abnormal degree that is an abnormal degree of each of the plurality of constituent elements by statistically processing the individual information; and a determiner that determines whether or not the overall abnormal degree exceeds a threshold value, wherein the individual abnormal degree calculator calculates the individual abnormal degree when the determiner determines that the overall abnormal degree exceeds the threshold value.

This disclosure describes at least embodiments of an aircraft monitoring system for an electric or hybrid airplane. The aircraft monitoring system can be constructed to enable the electric or hybrid aircraft to pass certification requirements relating to a safety risk analysis. The aircraft monitoring system can have different subsystems for monitoring and alerting of failures of a component, such as a battery pack, a motor controller, and/or a motors. The failures that pose a greater safety risk may be monitored and indicated by one or more subsystems without use of programmable components.

The invention comprises a method for analyzing malfunctions and/or changes of device statuses in a system of automation technology, wherein the system has a plurality of field devices communicating with one another directly via a communication network and being designed to issue an appropriate diagnostic notice depending on a malfunction or a change of a device status in the system. The diagnostic notices are transmitted to a data bank and stored in same. The method comprises: reading the diagnostic notices from the data bank; filtering the read diagnostic notices using at least one selection criterion; linking the filtered diagnostic notices using time stamps; defining time intervals; grouping the diagnostic notices linked using the time stamps into the defined time intervals which correspond to their respective time stamps; and evaluating the grouped diagnostic notices with regard to defined abnormalities.

Systems and methods for asset management are provided. Event data characterizing events experienced by assets distributed among different sites of a fleet is maintained. The event data includes an asset location within an asset hierarchy of the fleet and an event parameter corresponding to the event. A graphical user interface (GUI) is generated that displays a first window including a hierarchical list of assets organized according to their position within the asset hierarchy. When the GUI receives a selection of a level within the hierarchical list, events associated with the selected level can be identified. Identified events can be classified based upon their event data as a unique event having a single occurrence or a repeat event having multiple occurrences. In response to receipt of the selection, the GUI is updated to display a second window listing single entries for respective unique events and single entries for respective repeat events.

A method and system for reliability reporting for electrical hardware can involve analyzing stress data referenced to a predicted reliability of electrical hardware, the stress data including real-time stress information related to the electrical hardware. Reliability data associated with the electrical hardware based on the real-time information and the predicted reliability of the electrical hardware can be calculated. The reliability data can be presented in a graphical user interface that displays indicators of the reliability data including runtime data associated with the electrical hardware.