An optical fiber temperature distribution measurement system includes a temperature difference calculator configured to calculate a temperature difference between corresponding spatial resolution zones based on a first temperature distribution obtained by a return light from a first optical fiber part and a second temperature distribution obtained by a return light from a second optical fiber part, and an abnormality detector configured to calculate a temperature difference for evaluation for each spatial resolution zone, the temperature difference for evaluation being a sum of a temperature difference of each spatial resolution zone and a temperature difference of a spatial resolution zone adjacent thereto, and to determine that an abnormality has occurred in a roller near the spatial resolution zone when the calculated temperature difference for evaluation exceeds a reference value.

A method is provided for testing a reliability of a plurality of driven component variants (22, 24, 30), such as those to be used as power train elements in one or more wind turbines. The method includes conducting physical success run testing on a test bench (42) of a first subset of test specimens (50) provided for the component variants (22, 24, 30), and conducting virtual success run testing of a second subset of the test specimens (50). The virtual success run testing does not use test bench time or physical component samples, which reduces the costs typically needed to provide reliability results for specified operating time durations or other parameters at desired confidence levels for operators of driven components such as wind turbine operators, who want to avoid unscheduled downtime based on component failures.

A method is provided for testing a reliability of a plurality of driven component variants (22, 24, 30), such as those to be used as power train elements in one or more wind turbines. The method includes conducting physical success run testing on a test bench (42) of a first subset of test specimens (50) provided for the component variants (22, 24, 30), and conducting virtual success run testing of a second subset of the test specimens (50). The virtual success run testing does not use test bench time or physical component samples, which reduces the costs typically needed to provide reliability results for specified operating time durations or other parameters at desired confidence levels for operators of driven components such as wind turbine operators, who want to avoid unscheduled downtime based on component failures.

A robot for testing lower limb performance of a spacesuit includes a pressure maintaining box, an air circulation component, an air cooling unit, heat radiating hose components, and two mechanical legs. The air cooling unit is connected with the pressure maintaining box; the air circulation component is arranged in the pressure maintaining box; the mechanical legs are installed on the pressure maintaining box, and the heat radiating hose components are arranged in the mechanical legs; air in the pressure maintaining box is cooled through the air cooling unit and delivered into the heat radiating hose components through the air circulation component; each mechanical leg comprises a thigh, a knee joint component, a shank, an ankle joint component and a foot; the thigh is connected with the shank through the knee joint component; the shank is connected with the foot through the ankle joint component.

A robot for testing lower limb performance of a spacesuit includes a pressure maintaining box, an air circulation component, an air cooling unit, heat radiating hose components, and two mechanical legs. The air cooling unit is connected with the pressure maintaining box; the air circulation component is arranged in the pressure maintaining box; the mechanical legs are installed on the pressure maintaining box, and the heat radiating hose components are arranged in the mechanical legs; air in the pressure maintaining box is cooled through the air cooling unit and delivered into the heat radiating hose components through the air circulation component; each mechanical leg comprises a thigh, a knee joint component, a shank, an ankle joint component and a foot; the thigh is connected with the shank through the knee joint component; the shank is connected with the foot through the ankle joint component.

Predictive monitoring of a rotational machine of a cooling apparatus is provided by integrating a predictive vibration analyzer into an electronics rack being cooled by the cooling apparatus. The integrating includes associating at least one sensor of the predictive vibration analyzer with the rotational machine of the cooling apparatus. The analyzer includes a predetermined harmonics table of one or more base operational frequencies and associated rotational speed harmonics for the rotational machine indicative of one or more rotational faults, and the predictive vibration analyzer automatically evaluates vibration of the rotational machine during operation of the machine by analyzing vibration data therefor, and automatically ascertains, based on the vibration data, and the predetermined harmonics table, whether the rotational machine is predicted to possess a rotational fault of the one or more rotational faults.

A mechanical reliability testing platform for tri-post insulators in a GIL device includes a horizontal-GIL-arrangement-form fixed-tri-post-insulator mechanical reliability verification testing platform for a horizontal dynamic insertion and extraction test, and a turning-GIL-arrangement-form fixed-tri-post-insulator mechanical reliability verification testing platform for a vertical dynamic insertion and extraction test. A driving unit is employed to realize the insertion and extraction of the conducting rod of the sliding-tri-post-insulator GIL form unit at the contact holder, so as to simulate the reciprocating forces on the fixed tri-post insulator induced by the thermal expansion and contraction of the pipe during the actual operation of the GIL, and simulate the working condition of the fixed tri-post insulator under abnormal forces when the GIL experience foundation settlement.

A mechanical reliability testing platform for tri-post insulators in a GIL device includes a horizontal-GIL-arrangement-form fixed-tri-post-insulator mechanical reliability verification testing platform for a horizontal dynamic insertion and extraction test, and a turning-GIL-arrangement-form fixed-tri-post-insulator mechanical reliability verification testing platform for a vertical dynamic insertion and extraction test. A driving unit is employed to realize the insertion and extraction of the conducting rod of the sliding-tri-post-insulator GIL form unit at the contact holder, so as to simulate the reciprocating forces on the fixed tri-post insulator induced by the thermal expansion and contraction of the pipe during the actual operation of the GIL, and simulate the working condition of the fixed tri-post insulator under abnormal forces when the GIL experience foundation settlement.

A solenoid testing system for testing a solenoid is provided. The solenoid testing system includes a housing member having an inner surface and an outer surface. The solenoid is positioned on the outer surface. The solenoid testing system includes an armature adapted to move towards the solenoid upon generation of the magnetic field by the solenoid. The solenoid testing system includes at least one measuring device connected to the armature for measuring a value of a displacement parameter and a value of a load parameter associated with the armature. The solenoid testing system also includes a controller in communication with the measuring device. The controller is configured to determine an operational state of the solenoid, based on comparison of the value of the displacement parameter and the value of the load parameter with a first predefined set of values and a second predefined set of values, respectively.

A self-aware machine platform is implemented through analyzing operational data of machining tools to achieve machine tool damage assessment, prediction and planning in manufacturing shop floor. Machining processes are first identified by matching similar processes through an ICP algorithm. Machining processes are further clustered by Hotelling's T-squared statistics. Degradation of the machining tool is detected through a trend of the operational data within a cluster of machining processes by a monotonicity test, and the remaining useful life of the machining tool is predicted through a particle filter by extrapolating the trend under a first-order Markov process. In addition, process anomalies across machines are detected through a combination of outlier detection methods including SOMs, multivariate regression, and robust Mahalanobis distance. Warnings and recommendations are flexibly provided to manufacturing shop floor based on policy choice.