Fault detection and state estimation in robotic automatic control using machine learning

Natarajan, Rajesh; P, Santosh Reddy; Bose, S.; Gururaj, H L; Flammini, Francesco; Velmurugan, S.

doi:10.1016/j.array.2023.100298

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…This review uses figures and tables to present the output of STM, including the content of the topic itself, the relationship between topics and time, and the relationship between topics and regions, which is intuitive. (3). The discussion in this review can provide multi-perspective insights and guidance for the fault diagnosis of server robots.…”

Section: Discussionmentioning

confidence: 99%

“…Hence, one of the ideas, the data-driven intelligent maintenance methods, has gained attention. Natarajan et al [3] proposed an automatic control system with an Accelerated Gradient Descent based support vector machine and Gaussian filter for robot fault detection and state estimation. Based on the Internet of Things, Zhou et al [4] built a remote monitoring platform, realizing the two-way interaction between the robot and the platform.…”

Section: Introductionmentioning

confidence: 99%

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

Wang,

Dong,

Qian

et al. 2024

Meas. Sci. Technol.

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The market for service robots is expanding as labor costs continue to rise. Faced with intricate working environments, fault detection and diagnosis are crucial to ensure the proper functioning of service robots. The objective of this review is to systematically investigate the realm of service robots’ fault diagnosis through the application of Structural Topic Modeling (STM). A total of 289 papers were included, culminating in ten topics, including advanced algorithm application, data learning-based evaluation, automated equipment maintenance, actuator diagnosis for manipulator, non-parametric method, distributed diagnosis in multi-agent systems, signal-based anomaly analysis, integrating complex control framework, event knowledge assistance, mobile robot particle filtering method. These topics spanned service robot hardware and software failures, diverse service robot systems, and a range of advanced algorithms for fault detection in service robots. Asia-Pacific, Europe, and the Americas, recognized as three pivotal regions propelling the advancement of service robots, were employed as covariates in this review to investigate regional disparities. The review found that current research tends to favor the use of artificial intelligence algorithms to address service robots’ complex system faults and vast volumes of data. The topics of algorithms, data learning, automated maintenance, and signal analysis are advancing with the support of artificial intelligence, gaining increasing popularity as a burgeoning trend. Additionally, variations in research focus across different regions were found. The Asia-Pacific region tends to prioritize algorithm-related studies, while Europe and the Americas show a greater emphasis on robot safety issues. The integration of diverse technologies holds the potential to bring forth new opportunities for future service robot fault diagnosis. Simultaneously, regional standards about data, communication, and other aspects can streamline the development of methods for service robots’ fault diagnosis.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Wang,

Dong,

Qian

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

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Artificial Intelligence-Based Engineering Applications a Comprehensive Review of Application Areas, Impacts and Challenges

Aksoy,

Salman,

Ekrem

et al. 2024

Engineering Cyber-Physical Systems and Critical Infrastructures

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

Choi,

Park,

et al. 2023

Sensors

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This paper proposes fault diagnosis methods aimed at proactively preventing potential safety issues in robot systems, particularly human coexistence robots (HCRs) used in industrial environments. The data were collected from durability tests of the driving module for HCRs, gathering time-series vibration data until the module failed. In this study, to apply classification methods in the absence of post-failure data, the initial 50% of the collected data were designated as the normal section, and the data from the 10 h immediately preceding the failure were selected as the fault section. To generate additional data for the limited fault dataset, the Wasserstein generative adversarial networks with gradient penalty (WGAN-GP) model was utilized and residual connections were added to the generator to maintain the basic structure while preventing the loss of key features of the data. Considering that the performance of image encoding techniques varies depending on the dataset type, this study applied and compared five image encoding methods and four CNN models to facilitate the selection of the most suitable algorithm. The time-series data were converted into image data using image encoding techniques including recurrence plot, Gramian angular field, Markov transition field, spectrogram, and scalogram. These images were then applied to CNN models, including VGGNet, GoogleNet, ResNet, and DenseNet, to calculate the accuracy of fault diagnosis and compare the performance of each model. The experimental results demonstrated significant improvements in diagnostic accuracy when employing the WGAN-GP model to generate fault data, and among the image encoding techniques and convolutional neural network models, spectrogram and DenseNet exhibited superior performance, respectively.

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Fault detection and state estimation in robotic automatic control using machine learning

Cited by 4 publications

References 18 publications

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

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

Artificial Intelligence-Based Engineering Applications a Comprehensive Review of Application Areas, Impacts and Challenges

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

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