Advanced Data Analytics in Intelligent Industry: Theory and Practice

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Industrial Systems".

Deadline for manuscript submissions: closed (10 December 2023) | Viewed by 18014

Special Issue Editors


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Guest Editor
School of Automation, China University of Geosciences, No. 388, Lumo Road, Wuhan, China
Interests: process monitoring; fault diagnosis; machine learning
Special Issues, Collections and Topics in MDPI journals
School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
Interests: data driven fault diagnosis; testability design; reliability; electrical/electronic systems
School of Automation, China University of Geosciences, Wuhan 430074, China
Interests: advanced alarm monitoring; process data analytics; data mining for complex industrial processes
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Automation, Shanghai Jiao Tong University, Shanghai, China
Interests: fault detection and diagnosis; high-speed trains; data mining and analytics; machine learning; quantum computation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the gaining momentum of the big data movement and the emergence of Industry 4.0, a massive amount of process data has been archived by the distributed control system. By translating the historical data into process information, data-driven control models can be established without first principles knowledge, such that complex systems can also be operated safely, efficiently, and economically. Hence, they have been extensively studied and implemented by the process control community in recent decades. The purpose of this Special Issue is to discuss recent advances in data-driven intelligent control methods for industrial applications, especially process monitoring and isolation, fault diagnosis and tolerance, quality prediction and soft sensing, etc. Furthermore, new problems and future research directions in data-driven intelligent industry are also explored in this Special Issue. Through this Special Issue, the theory and application of intelligent industry can be enriched, and the development of intelligent manufacturing and Industry 4.0 can be promoted.

Potential topics include, but are not limited to, the following:

  • Data-driven industrial process monitoring.
  • Fault diagnosis and tolerant control.
  • Advanced alarm management.
  • Soft sensing and quality prediction.
  • Iterative learning control.
  • Distributed optimization control.
  • System identification and application.

Prof. Dr. Wanke Yu
Dr. Yang Li
Dr. Wenkai Hu
Dr. Hongtian Chen
Guest Editors

Manuscript Submission Information

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Keywords

  • industrial applications
  • process control
  • data analytics methods
  • machine learning

Published Papers (13 papers)

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Research

18 pages, 1027 KiB  
Article
Exponential Local Fisher Discriminant Analysis with Sparse Variables Selection: A Novel Fault Diagnosis Scheme for Industry Application
by Zhengping Ding, Yingcheng Xu and Kai Zhong
Machines 2023, 11(12), 1066; https://doi.org/10.3390/machines11121066 - 01 Dec 2023
Cited by 1 | Viewed by 899
Abstract
Local Fisher discriminant analysis (LFDA) has been widely applied to dimensionality reduction and fault classification fields. However, it often suffers from small sample size (SSS) problem and incorporates all process variables without emphasizing the key faulty ones, thus leading to degraded fault diagnosis [...] Read more.
Local Fisher discriminant analysis (LFDA) has been widely applied to dimensionality reduction and fault classification fields. However, it often suffers from small sample size (SSS) problem and incorporates all process variables without emphasizing the key faulty ones, thus leading to degraded fault diagnosis performance and poor model interpretability. To this end, this paper develops the sparse variables selection based exponential local Fisher discriminant analysis (SELFDA) model, which can overcome the two limitations of basic LFDA concurrently. First, the responsible faulty variables are identified automatically through the least absolute shrinkage and selection operator, and the current optimization problem are subsequently recast as an iterative convex optimization problem and solved by the minimization-maximization method. After that, the matrix exponential strategy is implemented on LFDA, it can essentially overcome the SSS problem by ensuring that the within-class scatter matrix is always full-rank, thus more practical in real industrial practices, and the margin between different categories is enlarged due to the distance diffusion mapping, which is benefit for the enhancement of classification accuracy. Finally, the Tennessee Eastman process and a real-world diesel working process are employed to validate the proposed SELFDA method, experimental results prove that the SELFDA framework is more excellent than the other approaches. Full article
(This article belongs to the Special Issue Advanced Data Analytics in Intelligent Industry: Theory and Practice)
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15 pages, 3062 KiB  
Article
An Improved Denoising Method for Fault Vibration Signals of Wind Turbine Gearbox Bearings
by Chaohai Zhang, Xu Zhang, Zufeng Xu, Wei Dai and Jie Lu
Machines 2023, 11(11), 1004; https://doi.org/10.3390/machines11111004 - 01 Nov 2023
Viewed by 913
Abstract
Vibration monitoring (VM) is an important tool for fault diagnosis in key components of wind turbine gearboxes (WTGs). However, due to the influence of white noise and random interference, it is difficult to realize high-quality denoising of WTG-VM signals. To overcome this limitation, [...] Read more.
Vibration monitoring (VM) is an important tool for fault diagnosis in key components of wind turbine gearboxes (WTGs). However, due to the influence of white noise and random interference, it is difficult to realize high-quality denoising of WTG-VM signals. To overcome this limitation, a novel joint denoising method for fault WTG-VM signals is proposed in this article, which we have named EWTKC-SVD. First, the empirical wavelet transform (EWT) boundary exploration method is used to optimize frequency band allocation and obtain the multiple intrinsic mode functions (IMFs). Second, the sensitive IMFs are selected according to the calculated correlation coefficient and kurtosis index, avoiding IMF redundancy. Finally, the fault WTG-VM signals are obtained using SVD denoising. Using this approach, the proposed method realizes high-quality denoising of WTG-VM signals. Furthermore, it also effectively solves the existing problems of conventional methods, namely, inefficient IMF selection, high noise, false frequencies, mode mixing, and end effect. Finally, the effectiveness, superiority, and reliability of the proposed method are proved using simulation and practical case results. Full article
(This article belongs to the Special Issue Advanced Data Analytics in Intelligent Industry: Theory and Practice)
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19 pages, 6480 KiB  
Article
Fault Diagnosis of Autonomous Underwater Vehicle with Missing Data Based on Multi-Channel Full Convolutional Neural Network
by Yunkai Wu, Aodong Wang, Yang Zhou, Zhiyu Zhu and Qingjun Zeng
Machines 2023, 11(10), 960; https://doi.org/10.3390/machines11100960 - 14 Oct 2023
Viewed by 910
Abstract
The fault feature extraction and diagnosis of autonomous underwater vehicles (AUVs) in complex environments pose significant challenges due to the intricate nature of the signals that reflect the AUVs’ states in the deep ocean. In this paper, an analytical model-free fault diagnosis algorithm [...] Read more.
The fault feature extraction and diagnosis of autonomous underwater vehicles (AUVs) in complex environments pose significant challenges due to the intricate nature of the signals that reflect the AUVs’ states in the deep ocean. In this paper, an analytical model-free fault diagnosis algorithm based on a multi-channel full convolutional neural network (MC-FCNN) is introduced to establish patterns between AUV states and potential fault types using multi-sensor signals. Firstly, the AUV raw dataset undergoes random forest multiple imputation by chained equations (RF-MICE) to serve as the input of the convolution neural network. Next, signal features are extracted through the full convolution channel, which can be fused as multilayer perceptron (MLP) input and Softmax classifier for fault identification. Finally, to validate the effectiveness of the proposed MC-FCNN model, fault diagnosis experiments are conducted using the dataset sourced from the Zhejiang University Laboratory with missing data. The experimental results demonstrate that, even with 60% of the data missing, the proposed RF-MICE with MC-FCNN model can still achieve an ideal fault identification. Full article
(This article belongs to the Special Issue Advanced Data Analytics in Intelligent Industry: Theory and Practice)
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29 pages, 10330 KiB  
Article
Enhanced Integrator with Drift Elimination for Accurate Flux Estimation in Sensorless Controlled Interior PMSM for High-Performance Full Speed Range Hybrid Electric Vehicles Applications
by Sadiq Ur Rahman and Chaoying Xia
Machines 2023, 11(7), 769; https://doi.org/10.3390/machines11070769 - 24 Jul 2023
Viewed by 1114
Abstract
Interior Permanent Magnet Synchronous Motor (IPMSM) motion-sensorless speed control necessitates precise knowledge of rotor flux, speed, and position. Due to numerous non-ideal aspects, such as converter nonlinearities, detection errors, integral initial value, and parameter mismatches, the conventional first-order integrator’s estimated rotor flux experiences [...] Read more.
Interior Permanent Magnet Synchronous Motor (IPMSM) motion-sensorless speed control necessitates precise knowledge of rotor flux, speed, and position. Due to numerous non-ideal aspects, such as converter nonlinearities, detection errors, integral initial value, and parameter mismatches, the conventional first-order integrator’s estimated rotor flux experiences a DC offset (Doff). Low-pass filters (LPF) with a constant cut-off frequency yield accurate estimates only in the medium- and high-speed range; however, at the low-speed area, both magnitude and phase estimates are inaccurate. The presented technique resolves the aforementioned issue for a broad speed range. In order to achieve precise flux estimation, this article presents an improved technique of flux estimator with two distinct drift mitigation strategies for the motion-sensorless field-oriented control (FOC) system of IPMSM. Using the orthogonality of the α- and β-axes, the proposed drift elimination system can estimate drift in different situations while maintaining a high level of dynamic performance. The stator flux linkage (SFL) computation in the synchronous coordinate is established from the estimation of the rotating shaft’s permanent magnetic flux linkage orientation and the statistical equations model of the SFL. By comparing the calculated SFL vector to the SFL vector derived from the stator winding voltage and currents integral model with a drift PI compensation loop, a feedback loop is formed to neutralize integral drift, and the rotational speed and position of an IPMSM is estimated utilizing the vector product of the two flux linkages in a phase-locked loop. Theoretical interpretation is presented, and Matlab Simulink simulations, as well as experimental outcomes, consistently demonstrate that the suggested estimation techniques can eliminate the phenomenon of flux drift. Full article
(This article belongs to the Special Issue Advanced Data Analytics in Intelligent Industry: Theory and Practice)
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17 pages, 849 KiB  
Article
Industrial Process Monitoring Based on Parallel Global-Local Preserving Projection with Mutual Information
by Tianshu Wu, Hongpeng Yin, Zhimin Yang, Jie Yao, Yan Qin and Peng Wu
Machines 2023, 11(6), 602; https://doi.org/10.3390/machines11060602 - 01 Jun 2023
Viewed by 841
Abstract
This paper proposes a parallel monitoring method for plant-wide processes by integrating mutual information and Bayesian inference into a global-local preserving projections (GLPP)-based multi-block framework. Unlike traditional multivariate statistic process monitoring (MSPM) methods, the proposed MI-PGLPP method transforms plant-wide monitoring into several sub-block [...] Read more.
This paper proposes a parallel monitoring method for plant-wide processes by integrating mutual information and Bayesian inference into a global-local preserving projections (GLPP)-based multi-block framework. Unlike traditional multivariate statistic process monitoring (MSPM) methods, the proposed MI-PGLPP method transforms plant-wide monitoring into several sub-block monitoringtasks by fully taking advantage of a parallel distributed framework. First, the original datasets of the process are divided into a group of data blocks by quantifying the mutual information of process variables. The block indexes of new data are generated automatically. Second, each data block is modeled by the GLPP method. The variable information and local structure are well preserved during the whole projection. Third, Bayesian inference is introduced to generate final statistics of the process by the probability framework. To illustrate the algorithm performance, a detailed case study is performed on the Tennessee Eastman process. Compared with the principle component analysis and GLPP-based method, the proposed MI-PGLPP provides higher FDRs and superior performance for plant-wide process monitoring. Full article
(This article belongs to the Special Issue Advanced Data Analytics in Intelligent Industry: Theory and Practice)
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14 pages, 1089 KiB  
Communication
Distributed Adaptive Consensus Tracking Control for Second-Order Nonlinear Heterogeneous Multi-Agent Systems with Input Quantization
by Linxing Xu and Yang Li
Machines 2023, 11(5), 524; https://doi.org/10.3390/machines11050524 - 01 May 2023
Cited by 1 | Viewed by 935
Abstract
In this paper, the problem of distributed adaptive consensus tracking control for second-order nonlinear heterogeneous multi-agent systems (MASs) with input quantization is considered. A distributed output feedback control scheme based on a K-filter is developed to suppress the influences of unknown disturbances and [...] Read more.
In this paper, the problem of distributed adaptive consensus tracking control for second-order nonlinear heterogeneous multi-agent systems (MASs) with input quantization is considered. A distributed output feedback control scheme based on a K-filter is developed to suppress the influences of unknown disturbances and input quantization. In contrast to existing approaches, an additional design parameter is introduced into the controller design to ensure that the subsystem tracking error converges to an arbitrarily small residual set. Through Lyapunov stability analysis, it can be proved that the proposed control scheme can achieve distributed consensus tracking control of second-order nonlinear heterogeneous MASs. In addition, all signals in the closed-loop system are shown to be globally uniformly bounded. Finally, a practical example demonstrates the effectiveness of the proposed control method. Full article
(This article belongs to the Special Issue Advanced Data Analytics in Intelligent Industry: Theory and Practice)
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21 pages, 2495 KiB  
Article
Deep PCA-Based Incipient Fault Diagnosis and Diagnosability Analysis of High-Speed Railway Traction System via FNR Enhancement
by Yunkai Wu, Xiangqian Liu and Yang Zhou
Machines 2023, 11(4), 475; https://doi.org/10.3390/machines11040475 - 13 Apr 2023
Cited by 14 | Viewed by 1543
Abstract
In recent years, the data-driven based FDD (Fault Detection and Diagnosis) of high-speed train electric traction systems has made rapid progress, as the safe operation of traction system is closely related to the reliability and stability of high-speed trains. The internal complexity and [...] Read more.
In recent years, the data-driven based FDD (Fault Detection and Diagnosis) of high-speed train electric traction systems has made rapid progress, as the safe operation of traction system is closely related to the reliability and stability of high-speed trains. The internal complexity and external complexity of the environment mean that fault diagnosis of high-speed train traction system faces great challenges. In this paper, a wavelet transform-based FNR (Fault to Noise Ratio) enhancement is realised to highlight incipient fault information and a Deep PCA (Principal Component Analysis)-based diagnosability analysis framework is proposed. First, a scheme for FNR enhancement-based fault data preprocessing with selection of the intelligent decomposition levels and optimal noise threshold is proposed. Second, fault information enhancement technology based on continuous wavelet transform is proposed from the perspective of energy. Further, a Deep-PCA based incipient fault detectability and isolatability analysis are provided via geometric descriptions. Finally, experiments on the TDCS-FIB (Traction Drive Control System–Fault Injection Benchmark) platform fully demonstrate the effectiveness of the method proposed in this paper. Full article
(This article belongs to the Special Issue Advanced Data Analytics in Intelligent Industry: Theory and Practice)
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15 pages, 7784 KiB  
Article
Research on the Optimal Control Strategy for the Maximum Torque per Ampere of Brushless Doubly Fed Machines
by Nannan Wang and Chaoying Xia
Machines 2023, 11(4), 422; https://doi.org/10.3390/machines11040422 - 25 Mar 2023
Viewed by 1160
Abstract
This paper presents an optimization strategy for a brushless doubly fed motor (BDFM) to achieve the maximum torque per ampere (MTPA). This method resolves the issue of high stator currents in slip frequency vector feedback linearization control (SFV-FLC) during both no-load and light-load [...] Read more.
This paper presents an optimization strategy for a brushless doubly fed motor (BDFM) to achieve the maximum torque per ampere (MTPA). This method resolves the issue of high stator currents in slip frequency vector feedback linearization control (SFV-FLC) during both no-load and light-load conditions. Firstly, the paper establishes a reduced-order state-space (SS) model of the BDFM in arbitrary rotating reference coordinates. Secondly, the expression of BDFM is obtained after the control motor rotor field orientation. To ensure a minimal stator current at a specific torque, this paper constructs an auxiliary function based on Lagrange’s theorem, which forces the control motor stator current derivative to be zero, resulting in the MTPA criterion. Finally, the superiority of the MTPA optimization algorithm proposed in the paper is validated through simulation experiments. Full article
(This article belongs to the Special Issue Advanced Data Analytics in Intelligent Industry: Theory and Practice)
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22 pages, 8481 KiB  
Article
Research and Analysis of the Characteristics of the Brushless Doubly-Fed Machine with High-Performance Decoupling Control
by Chaoying Xia and Nannan Wang
Machines 2023, 11(2), 313; https://doi.org/10.3390/machines11020313 - 20 Feb 2023
Cited by 2 | Viewed by 1281
Abstract
The paper presents the state-space (SS) model of the brushless double-fed machine (BDFM) by taking the negative conjugate (NC) transformation of the power machine’s correlation variable when the current source of the control machine is supplied in the m-t reference frame. Based on [...] Read more.
The paper presents the state-space (SS) model of the brushless double-fed machine (BDFM) by taking the negative conjugate (NC) transformation of the power machine’s correlation variable when the current source of the control machine is supplied in the m-t reference frame. Based on this, the testing method of machine parameters is given, and the SS model described by five parameters is obtained. The derivation process and realization method of the slip-frequency vector feedback linearization control (SFV-FLC) strategy are given. Then, researching the SS equation in the m-t reference frame by the control machine rotor field-oriented, the relationship between the maximum and minimum output torque and the control machine rotor flux amplitude and slip velocity, the machine parameters are given considering power supply constraints. Finally, the validity of the theoretical analysis is verified by simulation and experiment, and the feasibility of the decoupling control method are also demonstrated. Full article
(This article belongs to the Special Issue Advanced Data Analytics in Intelligent Industry: Theory and Practice)
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13 pages, 2930 KiB  
Article
Semi−Supervised Hybrid Modeling of the Yeast Fermentation Process
by Meng Zhao, Shunyi Zhao and Fei Liu
Machines 2023, 11(1), 63; https://doi.org/10.3390/machines11010063 - 04 Jan 2023
Cited by 2 | Viewed by 1387
Abstract
This study focuses on modeling the yeast fermentation process using the hybrid modeling method. To improve the prediction accuracy of the model and reduce the model training time, this paper presents a semi−supervised hybrid modeling method based on an extreme learning machine for [...] Read more.
This study focuses on modeling the yeast fermentation process using the hybrid modeling method. To improve the prediction accuracy of the model and reduce the model training time, this paper presents a semi−supervised hybrid modeling method based on an extreme learning machine for the yeast fermentation process. The hybrid model is composed of the mechanism model and the residual model. The residual model is built from the residuals between the real yeast fermentation process and the mechanism model. The residual model is used in parallel with the mechanism model. Considering that the residuals might be related to the inaccurate parameters or structure of the process, the mechanism model output is taken as unlabeled data, and the suitable inputs are selected based on Pearson’s maximum correlation and minimum redundancy criterion (RRPC). Meanwhile, an extreme learning machine is employed to improve the model’s training speed while maintaining the model’s prediction accuracy. Consequently, the proposal proved its efficacy through simulation. Full article
(This article belongs to the Special Issue Advanced Data Analytics in Intelligent Industry: Theory and Practice)
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18 pages, 4431 KiB  
Article
A Robust and Efficient UAV Path Planning Approach for Tracking Agile Targets in Complex Environments
by Shunfeng Cui, Yiyang Chen and Xinlin Li
Machines 2022, 10(10), 931; https://doi.org/10.3390/machines10100931 - 13 Oct 2022
Cited by 7 | Viewed by 1736
Abstract
The research into the tracking methods of unmanned aerial vehicles (UAVs) for agile targets is multi-disciplinary, with important application scenarios. Using a quadrotor as an example, in this paper, we mainly researched the tracking-related modeling and application verification of agile targets. We propose [...] Read more.
The research into the tracking methods of unmanned aerial vehicles (UAVs) for agile targets is multi-disciplinary, with important application scenarios. Using a quadrotor as an example, in this paper, we mainly researched the tracking-related modeling and application verification of agile targets. We propose a robust and efficient UAV path planning approach for tracking agile targets aggressively and safely. This approach comprehensively takes into account the historical observations of the tracking target and the surrounding environment of the location. It reliably predicts a short time horizon position of the moving target with respect to the dynamic constraints. Firstly, via leveraging the Bernstein basis polynomial and combining obstacle distribution information around the target, the prediction module evaluated the future movement of the target, presuming that it endeavored to stay away from the obstacles. Then, a target-informed dynamic searching method was embraced as the front end, which heuristically searched for a safe tracking trajectory. Secondly, the back-end optimizer ameliorated it into a spatial–temporal optimal and collision-free trajectory. Finally, the tracking trajectory planner generated smooth, dynamically feasible, and collision-free polynomial trajectories in milliseconds, which is consequently reasonable for online target tracking with a restricted detecting range. Statistical analysis, simulation, and benchmark comparisons show that the proposed method has at least 40% superior accuracy compared to the leading methods in the field and advanced capabilities for tracking agile targets. Full article
(This article belongs to the Special Issue Advanced Data Analytics in Intelligent Industry: Theory and Practice)
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18 pages, 14438 KiB  
Article
Multi-Layered Graph Convolutional Network-Based Industrial Fault Diagnosis with Multiple Relation Characterization Capability
by Yuanxin Wang, Cunhua Pan, Jian Zhang, Ming Gao, Haifeng Zhang and Kai Zhong
Machines 2022, 10(10), 873; https://doi.org/10.3390/machines10100873 - 28 Sep 2022
Cited by 1 | Viewed by 1620
Abstract
Fault diagnosis of industrial equipments is extremely important for the safety requirements of modern production processes. Lately, deep learning (DL) has been the mainstream fault diagnosis tool due to its powerful representational ability in learning and flexibility. However, most of the existing DL-based [...] Read more.
Fault diagnosis of industrial equipments is extremely important for the safety requirements of modern production processes. Lately, deep learning (DL) has been the mainstream fault diagnosis tool due to its powerful representational ability in learning and flexibility. However, most of the existing DL-based methods may suffer from two drawbacks: Firstly, only one metric is used to construct networks, thus multiple kinds of potential relationships between nodes are not explored. Secondly, there are few studies on how to obtain better node embedding by aggregating the features of different neighbors. To compensate for these deficiencies, an advantageous intelligent diagnosis scheme termed AE-MSGCN is proposed, which employs graph convolutional networks (GCNs) on multi-layer networks in an innovative manner. In detail, AE is carried out to extract deep representation features in process measurement and then combined with different metrics (i.e., K-nearest neighbors, cosine similarity, path graph) to construct the multi-layer networks for better multiple interaction characterization among nodes. After that, intra-layer convolutional and inter-layer convolutional methods are adopted for aggregating extensive neighbouring information to enrich the representation of nodes and diagnosis performance. Finally, a benchmark platform and a real-world case both verify that the proposed AE-MSGCN is more effective and practical than the existing state-of-the-art methods. Full article
(This article belongs to the Special Issue Advanced Data Analytics in Intelligent Industry: Theory and Practice)
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25 pages, 2944 KiB  
Article
A Novel Sliding Mode Momentum Observer for Collaborative Robot Collision Detection
by Shike Long, Xuanju Dang, Shanlin Sun, Yongjun Wang and Mingzhen Gui
Machines 2022, 10(9), 818; https://doi.org/10.3390/machines10090818 - 17 Sep 2022
Cited by 4 | Viewed by 2105
Abstract
Safety during physical human–robot interaction is the most basic requirement for robots. Collision detection without additional sensors is an economically feasible way to ensure it. In contrast, current collision detection approaches have an unavoidable trade-off between sensitivity to collisions, signal smoothness, and immunity [...] Read more.
Safety during physical human–robot interaction is the most basic requirement for robots. Collision detection without additional sensors is an economically feasible way to ensure it. In contrast, current collision detection approaches have an unavoidable trade-off between sensitivity to collisions, signal smoothness, and immunity to measurement noise. In this paper, we present a novel sliding mode momentum observer (NSOMO) for detecting collisions between robots and humans, including dynamic and quasistatic collisions. The collision detection method starts with a dynamic model of the robot and derives a generalized momentum-based state equation. Then a new reaching law is devised, based on which NSOMO is constructed by fusing momentum, achieving higher bandwidth and noise immunity of observation. Finally, a time-varying dynamic threshold (TVDT) model is designed to distinguish between collision signals and the estimated lumped disturbance. Its coefficients are obtained through offline data recognition. The TVDT with NSOMO enables fast and reliable collision detection and allows collision position assessment. Simulation experiments and hardware tests of the 7-DOF collaborative robot are implemented to illustrate this proposed method’s effectiveness. Full article
(This article belongs to the Special Issue Advanced Data Analytics in Intelligent Industry: Theory and Practice)
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