2 research outputs found
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
Design of an intelligent embedded system for condition monitoring of an industrial robot
PhD ThesisIndustrial robots have long been used in production systems in order to improve
productivity, quality and safety in automated manufacturing processes. There are
significant implications for operator safety in the event of a robot malfunction or failure,
and 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.
Condition monitoring (CM) 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 reduce maintenance costs. So, the main
focus of this research is to design and develop an online, intelligent CM system based on
wireless embedded technology to detect and diagnose the most common faults in the
transmission systems (gears and bearings) of the industrial robot joints using vibration
signal analysis.
To this end an old, but operational, PUMA 560 robot was utilized to synthesize a number
of different transmission faults in one of the joints (3 - elbow), such as backlash between
the gear pair, gear tooth and bearing faults. A two-stage condition monitoring algorithm is
proposed for robot health assessment, incorporating fault detection and fault diagnosis.
Signal processing techniques play a significant role in building any condition monitoring
system, in order to determine fault-symptom relationships, and detect abnormalities in
robot health. Fault detection stage is based on time-domain signal analysis and a statistical
control chart (SCC) technique. For accurate fault diagnosis in the second stage, a novel
implementation of a time-frequency signal analysis technique based on the discrete wavelet
transform (DWT) is adopted. In this technique, vibration signals are decomposed into eight
levels of wavelet coefficients and statistical features, such as standard deviation, kurtosis
and skewness, are obtained at each level and analysed to extract the most salient feature
related to faults; the artificial neural network (ANN) is then used for fault classification. A
data acquisition system based on National Instruments (NI) software and hardware was
initially developed for preliminary robot vibration analysis and feature extraction. The
transmission faults induced in the robot can change the captured vibration spectra, and the
robot’s natural frequencies were established using experimental modal analysis, and also
the fundamental fault frequencies for the gear transmission and bearings were obtained and
utilized for preliminary robot condition monitoring.
In addition to simulation of different levels of backlash fault, gear tooth and bearing faults
which have not been previously investigated in industrial robots, with several levels of
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severity, were successfully simulated and detected in the robot’s joint transmission. The
vibration features extracted, which are related to the robot healthy state and different fault
types, using the data acquisition system were subsequently used in building the SCC and
ANN, which were trained using part of the measured data set that represents the robot
operating range. Another set of data, not used within the training stage, was then utilized
for validation. The results indicate the successful detection and diagnosis of faults using the
key extracted parameters. A wireless embedded system based on the ZigBee
communication protocol was designed for the application of the proposed CM algorithm in
real-time, using an Arduino DUE as the core of the wireless sensor unit attached on the
robot arm. A Texas Instruments digital signal processor (TMS320C6713 DSK board) was
used as the base station of the wireless system on which the robot’s fault diagnosis
algorithm is run. To implement the two stages of the proposed CM algorithm on the
designed embedded system, software based on the C programming language has been
developed. To demonstrate the reliability of the designed wireless CM system,
experimental validations were performed, and high reliability was shown in the detection
and diagnosis of several seeded faults in the robot.
Optimistically, the established wireless embedded system could be envisaged for fault
detection and diagnostics on any type of rotating machine, with the monitoring system
realized using vibration signal analysis. Furthermore, with some modifications to the
system’s hardware and software, different CM techniques such as acoustic emission (AE)
analysis or motor current signature analysis (MCSA), can be applied.Iraqi government, represented by the Ministry of Higher Education and
Scientific Research, the Iraqi Cultural Attaché in London, and the University of
Technology in Baghda