Fault diagnosis in robotic manipulators using artificial neural networks and fuzzy logic

Khireddine, Mohamed Salah; Chafaa, Kheireddine; Slimane, Noureddine; Boutarfa, Abdelhalim

doi:10.1109/wccais.2014.6916571

Cited by 7 publications

(5 citation statements)

References 16 publications

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“…The ANN is a technique that has the potential to model a complex system. In this case, neural network connections can be made between inputs and outputs so physical operators are not needed to get the desired outputs [5]. The basic construction of NN consists of an input layer, output layer, and a hidden layer or layers.…”

Section: Artificial Neural Network Techniquementioning

confidence: 99%

“…Then, the values of the fuzzified inputs are assessed through the control rules (IF then) and the control outputs are generated. After that converted back Fuzzy output to crisp values by one of the different defuzzification method [5].…”

Section: Fuzzy-logic Techniquementioning

confidence: 99%

“…Previously, there were many kinds of research published in detecting and isolating faults in manipulator robots. In 2014, Mohamed Salah and others used to design and simulate fuzzy logic and neural network for fault detection and isolation (FDI) in sensor and actuator in manipulator robot [5]. In 2015, Amaneh Salmani Rezazadeh and others used nonlinear adaptive fault detection estimator that abled to a two_ link robot manipulator [6].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Hybrid Neural-Fuzzy Network Based Fault Detection and IsolationSystem for DC Motor of Robot Manipulator

Jassim¹,

Issa²,

Jawad³

2019

ETJ

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Section: Artificial Neural Network Techniquementioning

confidence: 99%

Section: Fuzzy-logic Techniquementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Hybrid Neural-Fuzzy Network Based Fault Detection and IsolationSystem for DC Motor of Robot Manipulator

Jassim¹,

Issa²,

Jawad³

2019

ETJ

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“…However, the spectrum becomes complicated when there are too many types of faults. Khireddine et al [10] proposed to use the information of position, speed and torque of the joints of SCARA as the feature input of artificial neural network and compare the residuals of the results for fault diagnosis. However, this in practice requires the installation of sensors at the joints, which limits the operation of SCARA.…”

Section: Introducementioning

confidence: 99%

SCARA mechanical fault identification based on WPM-SE+BPNN method

Wang¹,

Sun²,

Bai³

et al. 2022

Meas. Sci. Technol.

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The Selective Compliance Assembly Robot Arm (SCARA) is becoming increasingly important in industrial manufacturing and its operating condition directly determines the safe operation of production lines. However, the complex structure, the variety of mechanical faults and unstable movements of SCARA make fault identification extremely difficult. Therefore, this study proposed identification of SCARA mechanical faults based on wavelet packet multi-segment entropy (WPM-SE) +Back Propagation Neural Network (BPNN). First, the original vibration signal was decomposed into several sub-node signals by wavelet packet transform (WPT) and its envelope spectrum was obtained by Hilbert transform. Then, the envelope spectrum was divided equally into multiple intervals along the time axis, and the energy of each interval was calculated. Afterwards, the feature information of the envelope spectrum was obtained from the energy of each interval, which is defined as multi-segment entropy (M-SE). Where an envelope spectrum obtains a M-SE, and the number of segmentation intervals determines the value of the M-SE. Finally, a feature vector composed of the values of the M-SE was used as the feature input data of the BPNN for mechanical fault identification in SCARA. The BPNN has been tested to achieve an average recognition accuracy of 99.67% for both single mechanical faults and multiple mechanical faults. The results show that the WPM-SE method can effectively extract the feature information of the vibration signal and achieve fast and accurate identification of mechanical faults in SCARA.

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“…Also, the network structure of SVM does not allow the training of large number of sample data. The answer to this challenging issue would be neural network [20]- [25].…”

Section: Introductionmentioning

confidence: 99%

Data Association-Based Fault Diagnosis of IMUs: Optimized DBN Design and Wheeled Robot Evaluation

et al. 2020

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Deep belief network (DBN) is now being recognized as a powerful and eminently practical tool for large scale data processing. The main characteristics of DBN are the feature extension from low-level content to high-level data association and the representation of joint distribution between original data and matched labels. For a wheeled robot with no other available location reference supports, the internally integrated inertial measurement units (IMUs) essentially requires the robot to be able to implement efficient fault diagnosis to locate and identify the faults, especially for the accumulated error caused by large drifts of gyroscopes. An optimized DBN based fault diagnosis design is proposed to deal with such faults with complexity and diversity. The highlights of the proposed DBN model lies in its combination of weight value optimization via an inexact LSA-GA (abbreviates 'inexact linear searching algorithm-genetic algorithm') and dynamic adjustment for hidden-layer neurons of constituent RBMs (abbreviates 'restricted Boltzmann machines'). The problems associated with DBN anatomy, bat algorithm (BA) description and fault diagnosis modeling are discussed in detail. The real robot platform experiments and dataset tests are conducted. The results indicate that, the optimized DBN design leads to a better fault classification with excellent generalization ability on given datasets, and the adjustable 'DBN structure' contributes to the data association extraction between multiples of fault categories. The proposed scheme may therefore be considered to provide preferred reference models for a class of data based fault diagnosis problems.

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Fault diagnosis in robotic manipulators using artificial neural networks and fuzzy logic

Cited by 7 publications

References 16 publications

A Hybrid Neural-Fuzzy Network Based Fault Detection and IsolationSystem for DC Motor of Robot Manipulator

A Hybrid Neural-Fuzzy Network Based Fault Detection and IsolationSystem for DC Motor of Robot Manipulator

SCARA mechanical fault identification based on WPM-SE+BPNN method

Data Association-Based Fault Diagnosis of IMUs: Optimized DBN Design and Wheeled Robot Evaluation

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