A method and apparatus for testing rings cut from pipes for use in making subsea pipelines are described. The method for determining the whether a test ring is correctly assembled in a test chamber for testing pipes for use in making subsea pipelines comprises: mounting a test ring in a pressure chamber such that the ends of the test ring forms seals with opposing surfaces of the chamber to isolate the inside of the test ring from the outside; providing means for measuring the displacement of the test ring; providing means for measuring a force applied to the inner surface of the test ring; applying a force to the inner surface of the test ring; and using the displacement measurement and force measurements to determine whether the test ring is correctly mounted in the pressure chamber.

A system and method are described for predicting road friction using data from a fleet of connected vehicles. Each fleet vehicle includes a communication arrangement for reporting, to a back end system, floating car data sets, including a position of the vehicle, time data and data regarding determined road friction influencing parameters and a determined road friction associated with that position. The data sets are collected and aggregated in a central database, over a predetermined time period. A neural network computer is trained and validated, using aggregated data sets, to create a model to predict future road friction for a specific road network position or segment associated with that specific position. Once trained and validated, the neural network computer, upon receiving the same type of new, up to date, input data, uses the model to predict future road friction for that specific position or segment of the road network.

A system and method are described for predicting road friction using data from a fleet of connected vehicles. Each fleet vehicle includes a communication arrangement for reporting, to a back end system, floating car data sets, including a position of the vehicle, time data and data regarding determined road friction influencing parameters and a determined road friction associated with that position. The data sets are collected and aggregated in a central database, over a predetermined time period. A neural network computer is trained and validated, using aggregated data sets, to create a model to predict future road friction for a specific road network position or segment associated with that specific position. Once trained and validated, the neural network computer, upon receiving the same type of new, up to date, input data, uses the model to predict future road friction for that specific position or segment of the road network.

Disclosed herein is a comb sensor for measuring combing resistance, wherein the comb sensor comprises: a comb or brush comprising a handle, a head connected to the handle, and teeth connected to the head; and a bend sensor located on at least one of the teeth, to measure the bending of the one of the teeth. The present invention provides improved sensitivity and/or detailed measurement result.

Disclosed herein is a comb sensor for measuring combing resistance, wherein the comb sensor comprises: a comb or brush comprising a handle, a head connected to the handle, and teeth connected to the head; and a bend sensor located on at least one of the teeth, to measure the bending of the one of the teeth. The present invention provides improved sensitivity and/or detailed measurement result.

The invention disclosed a friction coefficient measuring device under the condition of material periodic deformation. The measuring device is mainly composed of a torque sensor, a sleeve system, an upper sample, a lower sample, a flexible fixture, an air bearing, a clamping system, a tension-compression sensor, a support rod, and a support table. A working module of a friction-wear testing machine is connected with the torque sensor and then connected with the sleeve system to fix the upper sample. A rotating module of the friction-wear testing machine is used for connecting the flexible fixture to fix the lower sample. The tension-compression sensor can accurately measure the radial force between the upper sample and the lower sample. The torque sensor can measure the load torque values of the upper sample and lower sample during testing. According to the curve of the load torque value and radial load value change between the upper sample and lower sample, the friction coefficient between the flexible bearing and flexible gear is obtained.

The invention disclosed a friction coefficient measuring device under the condition of material periodic deformation. The measuring device is mainly composed of a torque sensor, a sleeve system, an upper sample, a lower sample, a flexible fixture, an air bearing, a clamping system, a tension-compression sensor, a support rod, and a support table. A working module of a friction-wear testing machine is connected with the torque sensor and then connected with the sleeve system to fix the upper sample. A rotating module of the friction-wear testing machine is used for connecting the flexible fixture to fix the lower sample. The tension-compression sensor can accurately measure the radial force between the upper sample and the lower sample. The torque sensor can measure the load torque values of the upper sample and lower sample during testing. According to the curve of the load torque value and radial load value change between the upper sample and lower sample, the friction coefficient between the flexible bearing and flexible gear is obtained.

Detecting a breakover event during a friction test includes obtaining a hookload measurement for each of a series of time windows and generating a linear model and a nonlinear model from the plurality of hookload measurements. During run time, from the nonlinear model, an inflection point is identified from the nonlinear model, where the inflection point is determined to have occurred at a particular time window. A hookload value associated with the linear model, and a hookload value associated with the nonlinear model is determined for the particular time window. A breakover event is determined to have occurred at the particular time when the hookload value associated with the linear model at the particular time window exceeds the hookload value associated with the nonlinear model at the particular time window.

Detecting a breakover event during a friction test includes obtaining a hookload measurement for each of a series of time windows and generating a linear model and a nonlinear model from the plurality of hookload measurements. During run time, from the nonlinear model, an inflection point is identified from the nonlinear model, where the inflection point is determined to have occurred at a particular time window. A hookload value associated with the linear model, and a hookload value associated with the nonlinear model is determined for the particular time window. A breakover event is determined to have occurred at the particular time when the hookload value associated with the linear model at the particular time window exceeds the hookload value associated with the nonlinear model at the particular time window.

A charge potential distributed over a vehicle body resulting from the contact, separation, and friction between a tire and a road surface is detected by a detecting unit provided with a sensing electrode that is disposed on the external surface of the vehicle body, a reference electrode that is disposed apart from the external surface of the vehicle body with a space therebetween, and a sensor amplifier that senses a potential between the sensing electrode and the reference electrode as a signal and amplifies the signal. And the amplitude of the charge potential detected by the detecting unit is monitored by a data processing unit, thereby making it possible to accurately identify not only the state of the road surface but also an internal pressure state of the tire, a wear state of the tire, and the like during vehicular travel.