Slantlet transform used for faults diagnosis in robot arm

Alobaidy, Muhamad Azhar Abdilatef; Abdul–Jabbar, Jassim M.; Al-Khayyt, Saad Zaghlul Saeed

doi:10.11591/ijeecs.v25.i1.pp281-290

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…Signal acquisition and reconstruction A hard cut replaces irrelevant coefficients with 0 after projecting the wavelet basis onto the non-sparse signal. The spare vector coefficients are multiplied by the random Gaussian-sensing matrix [33]. To rebuild the voice signal, optimization functions are used.…”

Section: Orthogonal Modulation Using Cs-dtcwtmentioning

confidence: 99%

FPGA implementation of DTCWT architecture's high-speed DA structure for OFDM-based transceiver with CS

Sindgi,

Mahadevaswamy

2024

Bulletin EEI

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Communication systems at millimeter-wave (mm-wave) frequencies with high propagation losses use radio frequency (RF) budget analysis. RF system gains and losses ensure the receiver can recover the broadcast signal. Modern communication systems use compressive sensing (CS) and discrete wavelet transform (DWT). Hardware implementation is hard. Fieldprogrammable gate arrays (FPGA) adaptability, configurability, and processing speed make them popular. More mm-wave transceivers use FPGAs and advanced signal processing. FPGA-based mm-wave transceivers use compressed sensing and dual-tree complex wavelet transform (DTCWT). RF budget analysis recovers receiver signals. Energy and data efficiency transceivers have baseband processors, transmitters, and receivers. RF-to-mm-wave transmitter. Receiver demodulation and baseband conversion. CS and DTCWT processing modules boost baseband signal processing 5 Gbps Xilinx virtex-6 FPGAs. The system retrieves the signal while conserving power, according to simulations and testing. This study found that FPGA-based mm-wave transceivers can use advanced signal processing in future high-speed communication systems.

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Section: Orthogonal Modulation Using Cs-dtcwtmentioning

confidence: 99%

FPGA implementation of DTCWT architecture's high-speed DA structure for OFDM-based transceiver with CS

Sindgi,

Mahadevaswamy

2024

Bulletin EEI

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“…The signals are considered to be non-healthy if the gain of the disturbance block is not zero. The discrete wavelet transform (DWT) is used in this paper as a feature extraction method [17,18] representing the first error diagnosis stage. 8-level of DWT is dependent [19].…”

Section: Figure 2 Non-planar Robot Armmentioning

confidence: 99%

A Comparative Study of Multi-Layer Perceptron and Jordan Recurrent Neural Networks for Signals Classification in a Robotic System

Alobaidy,

Saeed

2023

JESA

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The artificial neural network (ANN) technique, one of the most sophisticated and advanced technologies of the present day, is leveraged across various fields due to its precision and superior performance. One such field that significantly relies on ANN is the detection of errors and classification of signals, especially in non-linear dynamic systems such as robotic systems. Mechanical and electrical faults at the robot's joints often manifest as disturbances, causing vibration at the robot arm's tip. This paper explores the offline detection and diagnosis of these tip acceleration values using eight levels of discrete wavelet transform (DWT). The output from the DWT serves as the input for the signal classification process. A comparative analysis between two neural network types, multilayer perceptron and the Jordan recurrent neural network, is undertaken to achieve signal classification. Signals from various non-healthy scenarios, along with the healthy case, are utilized to train the neural networks. The errors under consideration occur either individually at the robot's joints or at different joints simultaneously. The results indicate that, when applied to the same set of offline recorded data, the multi-layer perceptron neural network demonstrates 100% accuracy, while the Jordan recurrent neural network achieves 97% accuracy in data classification. Thus, for error classification in the robot arm, the multi-layer perceptron neural network outperforms the Jordan neural network.

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Review on Fault Diagnosis and Fault-Tolerant Control Scheme for Robotic Manipulators: Recent Advances in Ai, Machine Learning, and Digital Twin

Quamar,

Nasir

2024

Preprint

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Slantlet transform used for faults diagnosis in robot arm

Cited by 3 publications

References 16 publications

FPGA implementation of DTCWT architecture's high-speed DA structure for OFDM-based transceiver with CS

FPGA implementation of DTCWT architecture's high-speed DA structure for OFDM-based transceiver with CS

A Comparative Study of Multi-Layer Perceptron and Jordan Recurrent Neural Networks for Signals Classification in a Robotic System

Review on Fault Diagnosis and Fault-Tolerant Control Scheme for Robotic Manipulators: Recent Advances in Ai, Machine Learning, and Digital Twin

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