Deep residual neural-network-based robot joint fault diagnosis method

Abstract

A data driven method-based robot joint fault diagnosis method using deep residual neural network (DRNN) is proposed, where Resnet-based fault diagnosis method is introduced. The proposed method mainly deals with kinds of fault types, such as gain error, offset error and malfunction for both sensors and actuators, respectively. First, a deep residual network fault diagnosis model is derived by stacking small convolution cores and increasing the core size. meanwhile, the gaussian white noise is injected into the fault data set to verify the noise immunity for the proposed deep residual network. Furthermore, a simulation is conducted, where different fault diagnosis methods including support vector machine (SVM), artificial neural network (ANN), convolutional neural network (CNN), long-term memory network (LTMN) and deep residual neural network (DRNN) are compared, and the simulation results show the accuracy of fault diagnosis for robot system using DRNN is higher, meanwhile, DRNN needs less model training time. Visualization analysis proved the feasibility and effectiveness of the proposed method for robot joint sensor and actuator fault diagnosis using DRNN method.

Structural topic modeling-based systematic review for service robots’ fault diagnosis: content, popularity and regional differences

“…The figure results provided by STM and the discussion of this review can provide a helpful impact on the future development of service robots. The results showed that increasing research is biased toward the application of intelligent algorithms [35,36,47,48,50,52]. The emergence of technologies such as neural networks and deep learning has improved the diagnostic efficiency and accuracy of service robots.…”

“…Service robots are better adapted to complex environments and meet the essential work needs of human-machine collaborative interaction. Meanwhile, regional differences have brought new requirements to service robots' fault diagnosis, creating more unique service robots [35,76,80,81,87,92]. The main contributions of this review can be concluded as follows: (1).…”

“…Pan et al [35] proposed a Resent-based deep residual neural network algorithm for diagnosing robot joint and sensor faults. Compared with traditional fault diagnosis methods, the Resent-based diagnosis method has high accuracy, short training time, and high practicability.…”

“…In terms of the application of network algorithms, compared to the other two regions, the Asia-Pacific region has employed a more diverse range of network algorithms in robot fault detection. These include Convolutional Neural Networks [76][77][78][79], Back Propagation Neural Networks [80], Deep Residual Networks [35] and Generative Adversarial Networks [81]. Besides, abnormal diagnosis of signals (topic 7) is also a preferred research content in the Asia-Pacific region.…”

Fault Diagnosis Method for Human Coexistence Robots Based on Convolutional Neural Networks Using Time-Series Data Generation and Image Encoding

“…Therefore, even if robots have the same specifications and perform identical tasks in the same space, the types and characteristics of faults may differ. Hence, ongoing research is primarily directed at diagnosing faults in rotating components like the driving module [ 7 , 8 ].…”