A system and method for monitoring a dry gas seal positionable between a stationary housing and a rotatable shaft. A plurality of sense elements may rotate in response to the rotation of the rotatable shaft and a speed sensor may sense the speed of the rotatable shaft at speeds below one thousand rotations per minute based on sensing the plurality of sense elements. An acoustic emissions sensor may sense when a first seal face and a second seal face forming a seal interface of the dry gas seal are in an operational condition relative to one another. A processor may receive output signals from the speed sensor and the acoustic emissions sensor, and may establish an operating condition of the dry gas seal based on the signal from the speed sensor when the first seal face and the second seal face reach the operational condition relative to one another.

A system and method for monitoring a dry gas seal positionable between a stationary housing and a rotatable shaft. A plurality of sense elements may rotate in response to the rotation of the rotatable shaft and a speed sensor may sense the speed of the rotatable shaft at speeds below one thousand rotations per minute based on sensing the plurality of sense elements. An acoustic emissions sensor may sense when a first seal face and a second seal face forming a seal interface of the dry gas seal are in an operational condition relative to one another. A processor may receive output signals from the speed sensor and the acoustic emissions sensor, and may establish an operating condition of the dry gas seal based on the signal from the speed sensor when the first seal face and the second seal face reach the operational condition relative to one another.

Techniques regarding root cause analyses based on time series data are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise maintenance component that can detect a cause of failure for a mechanical system by employing a greedy hill climbing process to perform a polynomial number of conditional independence tests to determine a Granger causality between variables from time series data of the mechanical system given a conditioning set.

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.

An automated external defibrillator (AED) and AED Monitoring system made up of an AED, the AED having a self-diagnostic subroutine and performing said subroutine at regular intervals, the AED having at least an audio indicator that indicates the results of the self-diagnostic when the diagnosis is that the AED is in need of maintenance and a remote AED monitoring system, the AED monitoring system having an electromagnetic coil, microphone, battery, microprocessor, and wireless communication device, wherein the microprocessor selectively powers up the AED monitoring system prior to the AED's self-diagnostic subroutine and utilizes the electromagnetic coil and microphone to monitor for the AED's audio indicator that the AED is in need of maintenance, and the microprocessor transmitting a wireless signal through the wireless communication device indicating whether the AED is in need of maintenance; the microprocessor selectively powering down the AED monitoring system after transmitting the wireless signal.

Operation of a materials handling vehicle is monitored for impacts by generating an electrical impact signal indicative of a force measurement electronically read from a sensor mounted on the materials handling vehicle, generating an electrical impulse signal indicative of a change of momentum of the materials handling vehicle, and outputting an electrical impact alarm signal when the electrical impact signal exceeds a selected force limit threshold and the electrical impulse signal exceeds an impulse signal limit within a predetermined time period.

Operation of a materials handling vehicle is monitored for impacts by generating an electrical impact signal indicative of a force measurement electronically read from a sensor mounted on the materials handling vehicle, generating an electrical impulse signal indicative of a change of momentum of the materials handling vehicle, and outputting an electrical impact alarm signal when the electrical impact signal exceeds a selected force limit threshold and the electrical impulse signal exceeds an impulse signal limit within a predetermined time period.

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.

A system and method for validating unsupervised machine learning models. The method includes: analyzing, via unsupervised machine learning, a plurality of sensory inputs associated with a machine, wherein the unsupervised machine learning outputs at least one normal behavior pattern of the machine; generating, based on the at least one normal behavior pattern, at least one artificial anomaly, wherein each artificial anomaly deviates from the at least one normal behavior pattern; injecting the at least one artificial anomaly into the plurality of sensory inputs to create an artificial dataset; and analyzing the artificial dataset to determine whether a candidate model is a valid representation of operation of the machine, wherein analyzing the artificial dataset further comprises running the candidate model using the artificial dataset as an input.

A system and method for validating unsupervised machine learning models. The method includes: analyzing, via unsupervised machine learning, a plurality of sensory inputs associated with a machine, wherein the unsupervised machine learning outputs at least one normal behavior pattern of the machine; generating, based on the at least one normal behavior pattern, at least one artificial anomaly, wherein each artificial anomaly deviates from the at least one normal behavior pattern; injecting the at least one artificial anomaly into the plurality of sensory inputs to create an artificial dataset; and analyzing the artificial dataset to determine whether a candidate model is a valid representation of operation of the machine, wherein analyzing the artificial dataset further comprises running the candidate model using the artificial dataset as an input.