Industrial robots have long been used in production systems in order to improve productivity, quality and safety in automated manufacturing processes. An unforeseen robot stoppage due to different reasons has the potential to cause an interruption in the entire production line, resulting in economic and production losses. The majority of the previous research on industrial robots health monitoring is focused on monitoring of a limited number of faults, such as backlash in gears, but does not diagnose the other gear and bearing faults. Thus, the main aim of this research is to develop an intelligent condition monitoring system to diagnose the most common faults that could be progressed in the bearings of industrial robot joints, such as inner/outer race bearing faults, using vibration signal analysis. For accurate fault diagnosis, time-frequency signal analysis based on the discrete wavelet transform (DWT) is adopted to extract the most salient features related to faults, and the artificial neural network (ANN) is used for faults classification. A data acquisition system based on National Instruments (NI) software and hardware was developed for robot vibration analysis and feature extraction. An experimental investigation was accomplished using the PUMA 560 robot. Firstly, vibration signals are captured from the robot when it is moving one joint cyclically. Then, by utilising the wavelet transform, signals are decomposed into multi-band frequency levels starting from higher to lower frequencies. For each of these levels the standard deviation feature is computed and used to design, train and test the proposed neural network. The developed system has showed high reliability in diagnosing several seeded faults in the robot. _____________________ Alaa Abdulhady Jaber et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

The results from the first international comparison of profile and helix measurements between the national metrological institutes (NMIs) or their representatives of Germany, USA and UK are presented. The comparison was organized and coordinated by the UK National Gear Metrology Laboratory. The results from measurements on a 200 mm diameter helix master and a 200 mm diameter profile show that differences in slope error parameters are within the measurement uncertainty claimed by individual NMIs. The results should become the basis for the mutual acceptance of measurands for gears, in the calibration measurement capability list of EUROMET.

Machine healthy monitoring is a type of maintenance inspection technique by which an operational asset is monitored and the data obtained is analysed to detect signs of degradation, diagnose the causes of faults and thus reducing the maintenance costs. Vibration signals analysis was extensively used for machines fault detection and diagnosis in various industrial applications, as it respond immediately to manifest itself if any change is appeared in the monitored machine. However, recent developments in electronics and computing have opened new horizons in the area of condition monitoring and have shown their practicality in fault detection and diagnosis processes. The main aim of using wireless embedded systems is to allow data analysis to be carried out locally at field level and transmitting the results wirelessly to the base station, which as a result will help to overcome the need for wiring and provides an easy and cost-effective sensing technique to detect faults in machines. So, the main focuses of this research is to design and develop an online condition monitoring system based on wireless embedded technology that can be used to detect and diagnose the most common faults in the transmission systems (gears and bearings) of an industrial robot joints using vibration signal analysis.

For robots to perform many complex tasks there is a need for robust and stable force control. Linear, fixed‐gain controllers can only provide adequate performance when they are tuned to specific task requirements, but if the environmental stiffness at the robot/task interface is unknown and varies significantly, performance is degraded. This paper describes the design of two nonlinear, fuzzy force controllers, developed primarily using analytical methods, which overcome the problems of conventional control. Using simulation and an experimental robot, they are shown to perform well over a wide range of stiffness and both a quantitative and qualitative assessment of their performance compared with conventional force control is presented. © 2003 Wiley Periodicals, Inc.

The study was conducted to investigate the difference between Han Chinese and Caucasians on various parameters measured from responses to transcranial magnetic brain stimulation (TMS). Sixteen subjects were studied in each group. A circular coil at the vertex was used for stimulation, whilst recording surface electromyograms from right first dorsal interosseous. In the passive state, motor-evoked potential (MEP) threshold, MEP recruitment, short-interval intracortical inhibition (SICI) and intracortical facilitation were measured. The MEP threshold, recruitment and silent period were also measured in the active state. Chinese subjects showed significantly higher passive thresholds (P < 0.005), less inhibition of the motor response (SICI, P < 0.0005) and a shorter silent period (P < 0.05). Differences in SICI appeared to be a consequence of the differences in passive threshold and were not seen when active threshold was used to determine the conditioning stimulus intensity. Differences in silent period may also reflect differences in cortical excitability rather than inhibitory processes, as they were not seen when the silent-period duration was expressed as a function of MEP size, rather than TMS intensity. There appears to be a significant difference in some TMS parameters between Han Chinese and Caucasian subjects. This may reflect an underlying difference in cortical excitability.