Abstract: A method includes sending a first safety instruction to a machine in response to, within an entry window of time, receiving an open alert that a movable barrier is in an open state, where the movable barrier is at an entry point of an enclosure protecting personnel from the machine, receiving tag information of a person from a reader at the movable barrier, and confirming that the tag information belongs to a person authorized to enter the enclosure. The method includes sending a second safety instruction to the machine in response to, within the entry window of time, receiving the open alert, and without receiving tag information or confirming that the tag information belongs to a person authorized to enter the enclosure. The first and second safety instructions are different and the first safety instruction is less restrictive than the second safety instruction in terms of actions to prevent injury.

Abstract: A method for predicting end-of-life for a component includes determining a baseline lifetime model for a component connected to a machine functional safety system. The component is part of a system with physical devices. The method includes monitoring environmental conditions and usage conditions of the component and modifying the baseline lifetime model based on the monitored environmental and usage conditions to produce a modified lifetime model for the component. The method includes tracking a lifetime progress of the component with respect to the modified lifetime model and sending an alert in response to lifetime progress of the component reaching a lifetime threshold associated with the modified lifetime model.

Abstract: A method for derivation of a machine signature includes receiving sensor information from a primary sensor, where the primary sensor is positioned to receive information from a portion of an industrial operation, and receiving sensor information from one or more secondary sensors. The secondary sensors are arranged to provide additional information about the industrial operation indicative of current operating conditions of the industrial operation. The method includes using the sensor information from the secondary sensors and machine learning to determine if the portion of the industrial operation is operating in a normal condition and, in response to determining that the portion of the industrial operation is operating normally, using sensor information from the primary sensor during the normal operating condition to derive a primary sensor signature for the sensor information from the primary sensor.

Abstract: A method includes sending a first safety instruction to a machine in response to, within an entry window of time, receiving an open alert that a movable barrier is in an open state, where the movable barrier is at an entry point of an enclosure protecting personnel from the machine, receiving tag information of a person from a reader at the movable barrier, and confirming that the tag information belongs to a person authorized to enter the enclosure. The method includes sending a second safety instruction to the machine in response to, within the entry window of time, receiving the open alert, and without receiving tag information or confirming that the tag information belongs to a person authorized to enter the enclosure. The first and second safety instructions are different and the first safety instruction is less restrictive than the second safety instruction in terms of actions to prevent injury.

Abstract: A component includes a housing mounted at an entry point of an enclosure protecting a machine. The component includes a first RFID sensor, in the housing, that reads a first RFID tag in close proximity to the first RFID sensor indicating a closed status of a movable barrier of the enclosure at the entry point. Opening of the movable barrier allows entry to the enclosure. The component includes a second RFID sensor, in the housing, that reads a second RFID tag of an authorized person. The component includes a barrier access module that sends an open alert that the movable barrier is in an open state in response to the first RFID sensor not being close enough to read the first RFID tag, and an identification module that sends tag information unique to a person from the second RFID tag.

Abstract: A method for using sensor data and operational data of an industrial process to identify problems includes gathering sensor data from one or more sensors sensing conditions on equipment of an industrial process, receiving command information about operational commands issued to the equipment of the industrial process, and for each sensor of the one or more sensors, comparing the sensor data with signature information for the sensor. The signature information for the sensor is relevant for operational commands issued to the equipment. The method includes determining if the sensor data of a sensor of the one or more sensors exceeds the signature information corresponding to the sensor, locating a problem with a piece of equipment of the industrial process monitored by the sensor of the one or more sensors based on the sensor data exceeding the signature information for the sensor and issuing an alert reporting the problem.

Abstract: A component includes a housing mounted at an entry point of an enclosure protecting a machine. The component includes a first RFID sensor, in the housing, that reads a first RFID tag in close proximity to the first RFID sensor indicating a closed status of a movable barrier of the enclosure at the entry point. Opening of the movable barrier allows entry to the enclosure. The component includes a second RFID sensor, in the housing, that reads a second RFID tag of an authorized person. The component includes a barrier access module that sends an open alert that the movable barrier is in an open state in response to the first RFID sensor not being close enough to read the first RFID tag, and an identification module that sends tag information unique to a person from the second RFID tag.

Abstract: A method for derivation of a machine signature includes receiving sensor information from a primary sensor, where the primary sensor is positioned to receive information from a portion of an industrial operation, and receiving sensor information from one or more secondary sensors. The secondary sensors are arranged to provide additional information about the industrial operation indicative of current operating conditions of the industrial operation. The method includes using the sensor information from the secondary sensors and machine learning to determine if the portion of the industrial operation is operating in a normal condition and, in response to determining that the portion of the industrial operation is operating normally, using sensor information from the primary sensor during the normal operating condition to derive a primary sensor signature for the sensor information from the primary sensor.

Abstract: A method for risk assessment violation monitoring during a functional safety process includes receiving parameters from a risk assessment of a portion of a system with physical devices. The parameters of the risk assessment are applicable to a safety device of a machine safety system. The safety device is configured to prevent a hazardous condition in the system. The method includes detecting a change of a condition of the safety device. The condition is indicative of a potential safety issue affecting operation of the machine safety system. The method includes comparing parameters related to the change of the condition of the safety device with the parameters from the risk assessment and sending an alert in response to determining that the change of the condition of the safety device results in a violation of the risk assessment.

Abstract: A method for predicting end-of-life for a component includes determining a baseline lifetime model for a component connected to a machine functional safety system. The component is part of a system with physical devices. The method includes monitoring environmental conditions and usage conditions of the component and modifying the baseline lifetime model based on the monitored environmental and usage conditions to produce a modified lifetime model for the component. The method includes tracking a lifetime progress of the component with respect to the modified lifetime model and sending an alert in response to lifetime progress of the component reaching a lifetime threshold associated with the modified lifetime model.

Abstract: A method for monitoring machine operation for various machine speed and loads includes measuring vibration information at a sensor associated with a machine and an associated machine speed. The machine is a rotating machine. The method includes performing an operational frequency analysis of the vibration information and comparing results from the operational frequency analysis with a vibration signature for the machine. The vibration signature is for a machine speed that matches the machine speed of the measured vibration information. The vibration signature is one of several vibration signatures for the machine where each is for a different machine speed. The method includes identifying a potential failure mode based on a frequency range where the frequency analysis of the measured vibration information exceeds, by a threshold amount, the vibration signature of the plurality of vibration signatures that matches the machine speed, and transmitting an alert comprising the identified potential failure mode.

Abstract: A component for light curtain alignment includes a light intensity receiver that receives a plurality of light intensity signals from beam receivers of a receiver unit of a light curtain. The light curtain includes a transmitter unit with beam transmitters arranged linearly on the transmitter unit. The light curtain includes the receiver unit with the plurality of beam receivers arranged linearly. Each beam receiver is configured to receive light from a corresponding beam transmitter. The component includes a light intensity transmitter configured to transmit, from the light curtain, the plurality of light intensity signals received by the light intensity receiver, where each light intensity signal is from one or more beam receivers, and a trip transmitter that transmits a trip signal in response to determining that a light intensity signal from a beam receiver of the plurality of beam receivers is below a trip threshold.

Abstract: An industrial sensor selection system guides the process of selecting a suitable industrial sensor for use in an industrial sensing application based on information provided by the user about the application. The system can allow the user to initially specify a target product or object that is to be detected or measured by the sensing application. Based on the product selection, the sensor selection system allows the user to select from among a set of sensor use cases commonly applied to the selected product. The selection system may also prompt the user to provide additional contextual information about the selected use case. Based on the user's selection of a target product, use case, and any applicable contextual information, the sensor selection system identifies one or more suitable industrial sensors registered within a sensor profile library suitable for use within the user's sensing application.

Abstract: The present invention relates to a visual efficacy measuring method for objects in different light environments, which comprises: (a) setting a light environment; (b) measuring a relationship between color temperature and time; (c) a driver visually recognizing a target object; (d) resetting the light environment, and repeating the step (c); (e) processing the visual recognition information data collected by the experiments so as to establish a relationship of the visual recognition time in relation with the light environmental parameters of color temperature, color rendering index and brightness; (f) performing visual efficacy analysis according to the processing results of the visual recognition information data.

Abstract: The invention relates to an illumination standard calculation method for a tunnel entrance section in daytime based on safe visual recognition.

Abstract: The present invention relates to a visual efficacy determining method for non-colored objects in different light environments, comprising: a) setting light environment; b) placing an object for performing visual recognition of a driver; c) resetting the light environment, and repeating the step b); d) processing visual recognition information data obtained after experiments, and establishing a correlating relationship between visual recognition time and color temperature, color rendering index and brightness parameters of the light environment; and e) performing visual efficiency analysis according to the visual recognition information data processing results. The present invention further provides a system for implementing the visual efficacy determining method for non-colored objects in different light environments, wherein the system comprises a simulated tunnel middle section subsystem, a testing subsystem and a data processing subsystem.

Abstract: The present invention relates to a visual efficacy measuring method for objects in different light environments, which comprises: (a) setting a light environment; (b) measuring a relationship between color temperature and time; (c) a driver visually recognizing a target object; (d) resetting the light environment, and repeating the step (c); (e) processing the visual recognition information data collected by the experiments so as to establish a relationship of the visual recognition time in relation with the light environmental parameters of color temperature, color rendering index and brightness; (0 performing visual efficacy analysis according to the processing results of the visual recognition information data.

Abstract: The invention relates to an illumination standard calculation method for a tunnel entrance section in daytime based on safe visual recognition.

Abstract: The present invention relates to a visual efficacy determining method for non-colored objects in different light environments, comprising: a) setting light environment; b) placing an object for performing visual recognition of a driver; c) resetting the light environment, and repeating the step b); d) processing visual recognition information data obtained after experiments, and establishing a correlating relationship between visual recognition time and color temperature, color rendering index and brightness parameters of the light environment; and e) performing visual efficiency analysis according to the visual recognition information data processing results. The present invention further provides a system for implementing the visual efficacy determining method for non-colored objects in different light environments, wherein the system comprises a simulated tunnel middle section subsystem, a testing subsystem and a data processing subsystem.

Abstract: This invention relates to an illumination standard calculation method for a tunnel middle section based on safe visual recognition: (a) Setting the light environment of the tunnel middle section; (b) Placing a target object in the tunnel middle section; (c) Making a driver drive a motor vehicle at different speeds toward the target object, and measuring the visual recognition distances D required by the driver to visually discover the target object at different speeds; (d) Resetting the average brightness L of the tunnel middle section and repeating the steps (b) and (c) to obtain a plurality of different sets of visual recognition distances D and corresponding brightness values L; (e) Using the S model to fit the data of the plurality of sets of visual recognition distances D and brightness values L to obtain the formula of the relational model of D and L to be L=0.683/(5.