5 research outputs found
Diagnosis of rotor fault using neuro-fuzzy inference system
The three-phase induction machines (IM) is large importance and are being widely used as electromechanical system device regarding for their robustness, reliability, and simple design with well developed technologies. This work presents a reliable method for diagnosis and detection of rotor broken bars faults in induction machine. The detection faults are based on monitoring of the current signal. Also the calculation of the value of relative energy for each level of signal decomposition using package wavelet, which will be useful as data input of adaptive Neuro-Fuzzy inference system (ANFIS). In this method, fuzzy logic is used to make decisions about the machine state. The adaptive Neuro-Fuzzy inference system is able to identify the IM bearing state with high precision. This technique is applied under the MATLAB®.Keywords: Induction Machine; Diagnosis; Detection; Neuro-Fuzzy inference system
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
Detecção de Falhas em Internos de Válvulas de Controle Atuando em Múltiplas Regiões de Operação
Muitas técnicas de monitoramento de processos usando abordagens estatísticas assumem que os dados possuem uma distribuição normal. Além disso, várias destas técnicas requerem que a operação da planta se mantenha na mesma região, resultando na geração de um número elevado de falsos alarmes caso não sejam satisfeitas. Neste trabalho, uma técnica robusta de agrupamento de dados é utilizada para tratar plantas operando em múltiplos pontos de operação. A metodologia é aplicada à detecção de falhas em internos
de válvulas de controle, que pertence a esta classe de problema. Além disto, a falha considerada é de difícil detecção dada à dificuldade de instalar sensores para tal. Para avaliação da metodologia estudada, foi utilizado o benchmark DAMADICS. A técnica de agrupamento apresentada tem a capacidade de lidar com um determinado percentual de dados espúrios que podem surgir, inclusive em situações transitórias. Essa característica otimiza a etapa de pré-tratamento dos dados. Uma comparação com o método tradicional (sem agrupamento) é realizado destacando suas características e superioridade
Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals
Vibration measurement and monitoring are essential in a wide variety of applications. Vibration measurements are critical for diagnosing industrial machinery malfunctions because they provide information about the condition of the rotating equipment. Vibration analysis is considered the most effective method for predictive maintenance because it is used to troubleshoot instantaneous faults as well as periodic maintenance. Numerous studies conducted in this vein have been published in a variety of outlets. This review documents data-driven and recently published deep learning techniques for vibration-based condition monitoring. Numerous studies were obtained from two reputable indexing databases, Web of Science and Scopus. Following a thorough review, 59 studies were selected for synthesis. The selected studies are then systematically discussed to provide researchers with an in-depth view of deep learning-based fault diagnosis methods based on vibration signals. Additionally, a few remarks regarding future research directions are made, including graph-based neural networks, physics-informed ML, and a transformer convolutional network-based fault diagnosis method
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
ii
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