Wireless Fault Detection System for an Industrial Robot Based on Statistical Control Chart

Industrial robots are now commonly used in production systems to improve productivity, quality and safety in manufacturing processes. Recent developments involve using robots cooperatively with production line operatives. Regardless of application, there are significant implications for operator safety in the event of a robot malfunction or failure, and the consequent downtime has a significant impact on productivity in manufacturing. 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 and thus reducing the maintenance costs. Developments in electronics and computing have opened new horizons in the area of condition monitoring. The aim of using wireless electronic 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 develop an online and wireless fault detection system for an industrial robot based on statistical control chart approach. An experimental investigation was accomplished using the PUMA 560 robot and vibration signal capturing was adopted, as it responds immediately to manifest itself if any change is appeared in the monitored machine, to extract features related to the robot health conditions. The results indicate the successful detection of faults at the early stages using the key extracted parameters

Design of a Wireless Sensor Node for Vibration Monitoring of Industrial Machinery

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

Development of a Condition Monitoring Algorithm for Industrial Robots based on Artificial Intelligence and Signal Processing Techniques

Signal processing plays a significant role in building any condition monitoring system. Many types of signals can be used for condition monitoring of machines, such as vibration signals, as in this research; and processing these signals in an appropriate way is crucial in extracting the most salient features related to different fault types. A number of signal processing techniques can fulfil this purpose, and the nature of the captured signal is a significant factor in the selection of the appropriate technique. This chapter starts with a discussion of the proposed robot condition monitoring algorithm. Then, a consideration of the signal processing techniques which can be applied in condition monitoring is carried out to identify their advantages and disadvantages, from which the time-domain and discrete wavelet transform signal analysis are selected

Force feedback in remote tele-manipulation

PhD ThesisIt is becoming increasingly necessary to carry out manual operations in environments which are hazardous to humans - using remote manipulator systems that can extend the operators reach. However, manual dexterity can become severely impaired due to the complex relationship that exists between the operator, the remote manipulator system and the task. Under such circumstances, the introduction of force feedback is considered a desirable feature, and is particularly important when attempting to carry out complex assembly operations. The dynamic interaction in the manmachine system can significantly influence performance, and in the past evaluation has been largely by comparative assessment. In this study, an experimental remote manipulator system, or tele-manipulator system, has been developed which consists of three electrically linked planar manipulator arms, each with three degrees of freedom. An articulated 'master' arm is used to control an identical 'slave' arm, and independently, a second kinematically and dynamically dissimilar slave arm. Fully resolved Generalized Control has been demonstrated using a high speed computer to carry out the necessary position and force transformations between dissimilar master and slave arms in realtime. Simulation of a one degree of freedom master-slave system has also been carried out, which includes a simple model of the human operator and a task based upon a rigid stop. The results show good agreement with parallel experimental tests, and have provided a firm foundation for developing a fully resolved position/position control scheme, and a unique way of backdriving the master arm. Preliminary tests were based on a peg-in-hole transfer task, and have identified the effect on performance of force reflection ratio. More recently a novel crank-turning task has been developed to investigate the interaction of system parameters on overall performance. The results obtained from these experimental studies, backed up by simulation, demonstrate the potential of computer augmented control of remote manipulator systems. The directions for future work include development of real-time control of tele-robotic systems and research into the overall man-machine interaction

Localization of Indoor Mobile Robot Using Monte Carlo Localization Algorithm (MCL)

One of the challenging issues in robotics is to give a mobile robot the ability to recognize its initial pose ( position and orientation) without any human help. In this paper, the components of a mobile robot will be described in addition to the specification of the sensor that will be used. Then, the map of the environment  will be defined since it is pre-defined and stored in the memory of the robot. After that, a localization algorithm has been designed, analysed and implemented to develop the ability of a mobile robot to  recognize its initial pose. Finally, the final results that have been taken practically will discussed. These result will be divided into two main sub-sections; the first section describes the particles distribution over the working environment and their position update over a number of iterations. Second section will shows the update in the importance weight values over a number of iterations and for three different number of particles. 

Fault Diagnosis of Industrial Robot Bearings Based on Discrete Wavelet Transform and Artificial Neural Network

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

Path Planning, Motion Control and Obstacle Detection of Indoor Mobile Robot

In this paper, A* path planning algorithm has been represented for a mobile robot to be able to follow a constructed path from its current position to a specified goal within its environment. To ensure that the mobile robot follow the constructed path by path planning algorithm, a motion control algorithm has been built. In the same time, to detect static obstacles and avoid collision with them, an obstacle detection algorithm has been used as a final algorithm that will be used as a part of the whole system to give the robot the ability to move from its initial known position to a specific goal in an optimum way.

Collins and Sivers asymmetries in muonproduction of pions and kaons off transversely polarised protons

Measurements of the Collins and Sivers asymmetries for charged pions and charged and neutral kaons produced in semi-inclusive deep-inelastic scattering of high energy muons off transversely polarised protons are presented. The results were obtained using all the available COMPASS proton data, which were taken in the years 2007 and 2010. The Collins asymmetries exhibit in the valence region a non-zero signal for pions and there are hints of non-zero signal also for kaons. The Sivers asymmetries are found to be positive for positive pions and kaons and compatible with zero otherwise. © 2015