A fault prediction system based on sensor data on a numerical control machine tool and a method therefor. The fault prediction system includes a plurality of sensors for collecting numerical control machine tool operation state data serving as multi-channel data, wherein an output end of a sensor is connected to an input end of a multi-channel sensor interface circuit, and an output end of the multi-channel sensor interface circuit is connected to a controller. The plurality of sensors can be multi-path temperature sensors, multi-path vibration sensors or multi-path noise sensors. The defects in the prior art of there being no model for researching a cross correlation of multi-channel data, collected by a plurality of sensors, of an operation state of a numerical control machine tool, and a device fault subspace of the multi-channel data being unable to be obtained are effectively prevented.

A programmable electronic power regulator includes a power module for controlling an actuator, a control module for actuating the power module, and an internal monitoring module for transferring the control module to an emergency operation. The internal monitoring module is configured to monitor a system state, detect a critical operating state, and output an error signal. The control module comprises: a basic controller, which is configured to output a power module control signal, and in which functions for open- and closed-loop control of the actuator are implemented, which are required for an emergency operation in a critical operating state; an additional controller, in which functions that are not needed for emergency operation are implemented; and a controller disconnection point, which connects the basic controller with the additional controller via a control connection, and which is configured to at least partially disconnect the control connection upon receipt of the error signal.

Scheme for detection and classification of semiconductor equipment faults. Sensor traces are monitored and processed to separate known abnormal operating conditions from unknown abnormal operating conditions. Feature engineering permits focus on relevant traces for a targeted feature. A machine learning model is built to detect and classify based on an initial classification set of anomalies. The machine learning model is continuously updated as more traces are processed and learned.

A method for controlling a wind turbine includes detecting, via a controller, a plurality of analytic outputs of the wind turbine from a plurality of different analytics. The method also includes analyzing, via the controller, the plurality of analytic outputs of the wind turbine. Further, the method includes generating, via the controller, at least one computer-based model of the wind turbine using at least a portion of the analyzed plurality of analytic outputs. Moreover, the method includes training, via the controller, the at least one computer-based model of the wind turbine using annotated analytic outputs of the wind turbine. As such, the method includes checking the plurality of analytic outputs for anomalies using the at least one computer-based model. Accordingly, the method includes implementing a control action when at least one anomaly is detected.

A data processing apparatus includes a first processing unit that executes real-time processing with respect to data, a second processing unit that executes batch processing with respect to data that is output from the first processing unit as a result of processing by the first processing unit, and a monitor that monitors a status of the processing by the first processing unit and a status of processing by the second processing unit. The first processing unit includes a plurality of subprocessing units and buffers, and the second processing unit also includes a plurality of subprocessing units and buffers. The second processing unit includes a storage. The monitor includes a first monitor that monitors, for each of the buffers included in the first processing unit, an amount of the data stored in the corresponding buffer and a second monitor that monitors a total amount of the data stored in the buffers included in the second processing unit and the data stored in the storage.

A method of detecting abnormalities includes: calculating a reference failure rate using failure data at a plurality of points in time included in a particular period; calculating a detection failure rate and weighting, corresponding to failure data at a detection time point after the particular period, using the reference failure rate; calculating an abnormality index based on multiplying the detection failure rate by the weighting; comparing the abnormality index with an index corresponding to a control limit for stably controlling a failure rate; and detecting whether the failure data at the detection time point is abnormal, based on a result of the comparison of the abnormality index with the index corresponding to the control limit.

A disclosed example meter to monitor a media device includes a touch sense circuit including a circuit input and a circuit output, the circuit input to electrically couple with a first power line of the meter and a capacitor, the circuit output to provide a first value based on the capacitor when the first power line of the meter is not electrically coupled with a second power line of the media device, the circuit output to provide a second value different from the first value when the first power line of the meter is electrically coupled with the second power line of the media device. The disclosed example meter also includes at least one processor to detect the first power line of the meter is not electrically coupled with the second power line of the media device when the circuit output of the touch sense circuit provides the first value.

The present invention relates to a method and apparatus for fault diagnosis of a programmable robot, and includes the steps of collecting sensing data corresponding to an operation of a programmable robot, identifying a program and motion related to the sensing data, generating an execution pattern based on the sensing data, extracting a standard pattern corresponding to the identified program and motion, and diagnosing a fault of the programmable robot by comparing the standard pattern with the execution pattern, and the present invention may be applicable as another embodiment.

A server-based environment for reporting a status of a security, monitoring and automation controller is provided. Detecting cessation of an always-on persistent network connection between the SMA controller and the server is also provided. Reporting the cessation of the network connection to an end user and defined others is further provided. A further aspect provides for automatically reporting an alarm event to a central station, the end user, and others, in the event the cessation of the network connection occurs while the SMA controller is armed and after a zone fault event, and not receiving a disarm notification prior to expiration of a preset entry delay.

The present disclosure relates to analyzing a firmware or a finite state machine, decomposing the same into a plurality of routines or states, individuating significative instructions or states, associating each significative instruction or state with a watchpoint, calculating first HASH values of the watchpoints using a HASH function before running the firmware or finite state machine for all allowable paths in the firmware or finite state machine corresponding to a correct working of the same, storing the set of first HASH values as calculated, calculating second HASH values of the watchpoints using a HASH function when running of the firmware or finite state machine, comparing the second HASH value of each watchpoint as calculated with the stored set of first HASH values, and validating the instruction or state of a watchpoint as correct if its second HASH value is comprised in the first HASH values of the allowed paths.