A predictive diagnostics system for monitoring mechanical seals. The system autonomously detects a loss of lubrication within a sliding seal interface of a mechanical seal, the system including a loss of lubrication failure mode logic module configured to monitor data sensed by one or more sensors and diagnose conditions relating to a loss of lubrication within the sliding seal interface, and a plurality of other failure mode logic modules configured to monitor data sensed by the one or more sensors and diagnose conditions relating to specific types of mechanical failures known to occur in mechanical seal systems, the loss of lubrication failure mode logic module configured to determine which of the plurality of other failure mode logic modules are activated during the diagnosis of conditions related to a loss of lubrication within the sliding seal interface.

An inspection station for manufactured components includes a framework and a plurality of cameras. The framework has of a plurality of vertically stacked and spaced apart plates. Each plate defines a central orifice. The central orifices of each plate are aligned along an axis. The manufactured components are configured to freefall through the central orifices defined by the plates. The plurality of cameras is secured to the framework. Each camera is focused toward a region within the framework to capture images of the manufactured components. The plurality of cameras is arranged in an array that extends radially about the axis. At least one of the cameras is positioned at an angle above a horizontal plane that is perpendicular to the axis and intersects the region within the framework. At least one of the cameras is positioned at an angle below the horizontal plane.

An insulating glazing unit includes at least one spacer, which is shaped around the periphery to produce a spacer frame and delimits an inner region, a first glass pane, which is arranged on a pane contact surface of the spacer frame and a second glass pane, which is arranged on a second pane contact surface of the spacer frame, and the glass panes project beyond the spacer frame and an outer region is formed, which is filled, at least in some sections with a sealing element. At least one NFC transponder is arranged in the inner region, the NFC transponder includes an electronics unit, which is arranged on the inner surface of the spacer, and an antenna unit, which is arranged on the inner surface of one of the glass panes, and the electronics unit is electrically conductively connected to the antenna unit.

The present technology includes a method for detecting one or more anomalous operating characteristic of an industrial machine. The method can include collecting telematics data indicative of the industrial machine's performance, generating a histogram based on at least a portion of the collected telematics data, applying a histogram comparator engine to the histogram to determine whether the histogram indicates an anomalous operating characteristic, and if the histogram is determined to indicate an anomalous operating characteristic, presenting, to a user, information associated with the anomalous operating characteristic.

A low-powered sensor, a method for a low-powered sensor, and an apparatus are provided for monitoring a condition of a machine. Responsive to receiving a trigger signal during a setup phase, the low-powered sensor collects sensor data for a predetermined extended period of time. The low-powered sensor receives a setting of a delay based on a determined amount of time of the predetermined extended period of time, since the receiving of the trigger signal, for the machine to reach a steady operating condition within predetermined limits. Upon receiving the trigger signal after the receiving of the setting of the delay, the low-powered sensor waits a period of time of the set delay before collecting the sensor data for a predetermined amount of time that is shorter than the predetermined extended period of time, and provides the collected sensor data to a computing device for analysis.

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.

Systems, methods, and apparatus for prediction of maintenance service for machines. In one example, one or more sensors are configured to generate a sensor data stream during operation of a machine. An artificial neural network (ANN) is configured to receive the sensor data stream and predict a maintenance service for the machine based on the sensor data stream. For example, the ANN can be trained using the sensor data stream collected within a predetermined time period of a machine being newly-installed in an assembly line or other industrial automation facility. The machine can be considered to be operating in a normal condition during the predetermined time period such that the ANN can be trained to detect anomaly that deviates from the normal patterns of the sensor data stream. For example, the ANN can be a spiking neural network (SNN).

In accordance with various embodiments described herein are systems and methods for collecting and analyzing parameter and properties data from an operation and using the data to determining Automation Safety and Operations Performance (ASOP) metrics indicative of various performance aspects of a given operation. Some embodiments are systems and methods for determining one or more ASOP metrics or metrics selected from a group consisting of elements including but not limited to Alarm System Performance Metric, an Operator Loading Metric, a Controllability Performance Metric, a Proximity to Safety System Metric, a Demand on Safety Systems Metric, and a Control System Integrity Metric.

In accordance with various embodiments described herein are systems and methods for collecting and analyzing parameter and properties data from an operation and using the data to determining Automation Safety and Operations Performance (ASOP) metrics indicative of various performance aspects of a given operation. Some embodiments are systems and methods for determining one or more ASOP metrics or metrics selected from a group consisting of elements including but not limited to Alarm System Performance Metric, an Operator Loading Metric, a Controllability Performance Metric, a Proximity to Safety System Metric, a Demand on Safety Systems Metric, and a Control System Integrity Metric.

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. A processor may receive output signals from the speed sensor and may establish a position of the dry gas seal or an operating condition of the dry gas seal based on the signal from the speed sensor.