scholarly journals Multiple Working Mode Based Door Opening Control and Fault Detection of Mobile Modular and Reconfigurable Robot

2021 ◽  
Author(s):  
Saleh Ahmad Ali
Keyword(s):  
Fault Detection ◽  
Control Design ◽  
Torque Control ◽  
Experimental Results ◽  
Joint Torque ◽  
Detection Scheme ◽  
Door Opening ◽  
Position Errors ◽  
Internal Forces ◽  
Reconfigurable Robot

The study in this thesis addresses the problem of opening a door with a modular and reconfigurable robot (MRR) mounted on a wheeled mobile robot platform. The foremost issue with door opening problems is the prevention of occurrence of large internal forces that arise due to position errors or imprecise modeling of the robot or its environment, i.e. the door parameters, specifically. Unlike previous methods that relied on compliance control, making the control design rather complicated, this thesis presents a new concept that utilizes the multiple working modes of the MRR modules. The control design is significantly simplified by switching selected joints of the MRR to work in passive mode during door opening operation. As a result, the occurrence of large internal forces is prevented. Different control schemes are used for control of the joint modules in different working modes. For passive joint modules, a feedforward torque control approach is used to compensate the joint friction to ensure passive motion. For the active joint modules, a distributed control method, based on torque sensing, is used to facilitate the control of joint modules working under this mode. To enable autonomous door opening, an online door parameter estimation algorithm is proposed on the basis of the least squares method; and a path planning algorithm is developed on the basis of Hermite cubic spline functions, with consideration of motion constraints of the mobile MRR. The theory is validated using simulations and experimental results, as presented herein. A distributed fault detection scheme for MRR robots with joint torque sensing is also proposed in this thesis. The proposed scheme relies on filtering the joint torque command and comparing it with a filtered torque estimate that is derived from the nonlinear dynamic model of MRR with joint torque sensing. Common joint actuator faults are considered with fault detection being performed independently for each joint module. The proposed fault detection scheme for each module does not require motion states of any other module, making it an ideal modular approach for fault detection of modular robots. Experimental results have attested the effectiveness of the proposed fault detection scheme.

scholarly journals Multiple Working Mode Based Door Opening Control and Fault Detection of Mobile Modular and Reconfigurable Robot

2021 ◽  
Author(s):  
Saleh Ahmad Ali
Keyword(s):  
Fault Detection ◽  
Control Design ◽  
Torque Control ◽  
Experimental Results ◽  
Joint Torque ◽  
Detection Scheme ◽  
Door Opening ◽  
Position Errors ◽  
Internal Forces ◽  
Reconfigurable Robot

The study in this thesis addresses the problem of opening a door with a modular and reconfigurable robot (MRR) mounted on a wheeled mobile robot platform. The foremost issue with door opening problems is the prevention of occurrence of large internal forces that arise due to position errors or imprecise modeling of the robot or its environment, i.e. the door parameters, specifically. Unlike previous methods that relied on compliance control, making the control design rather complicated, this thesis presents a new concept that utilizes the multiple working modes of the MRR modules. The control design is significantly simplified by switching selected joints of the MRR to work in passive mode during door opening operation. As a result, the occurrence of large internal forces is prevented. Different control schemes are used for control of the joint modules in different working modes. For passive joint modules, a feedforward torque control approach is used to compensate the joint friction to ensure passive motion. For the active joint modules, a distributed control method, based on torque sensing, is used to facilitate the control of joint modules working under this mode. To enable autonomous door opening, an online door parameter estimation algorithm is proposed on the basis of the least squares method; and a path planning algorithm is developed on the basis of Hermite cubic spline functions, with consideration of motion constraints of the mobile MRR. The theory is validated using simulations and experimental results, as presented herein. A distributed fault detection scheme for MRR robots with joint torque sensing is also proposed in this thesis. The proposed scheme relies on filtering the joint torque command and comparing it with a filtered torque estimate that is derived from the nonlinear dynamic model of MRR with joint torque sensing. Common joint actuator faults are considered with fault detection being performed independently for each joint module. The proposed fault detection scheme for each module does not require motion states of any other module, making it an ideal modular approach for fault detection of modular robots. Experimental results have attested the effectiveness of the proposed fault detection scheme.

scholarly journals Power efficiency estimation based health monitoring and fault detection of modular and reconfigurable robot

2021 ◽  
Author(s):  
Jing Yaun
Keyword(s):  
Fault Detection ◽  
Health Monitoring ◽  
Power Efficiency ◽  
Joint Torque ◽  
Robot System ◽  
Operation Conditions ◽  
Efficiency Estimation ◽  
Joint Torque Sensor ◽  
Reconfigurable Robot ◽  
Critical Components

Power efficiency degradation of machines often provides intrinsic indication of problems associated with their operation conditions. Inspired by this observation, in this thesis work, a simple yet effective power efficiency estimation base health monitoring and fault detection technique is proposed for modular and reconfigurable robot with joint torque sensor. The design of the Ryerson modular and reconfigurable robot system is first introduced, which aims to achieve modularity and compactness of the robot modules. Critical components, such as the joint motor, motor driver, harmonic drive, sensors, and joint brake, have been selected according to the requirement. Power efficiency coefficients of each joint module are obtained using sensor measurements and used directly for health monitoring and fault detection. The proposed method has been experimentally tested on the developed modular and reconfigurable robot with joint torque sensing and a distributed control system. Experimental results have demonstrated the effectiveness of the proposed method.

scholarly journals Power efficiency estimation based health monitoring and fault detection of modular and reconfigurable robot

2021 ◽  
Author(s):  
Jing Yaun
Keyword(s):  
Fault Detection ◽  
Health Monitoring ◽  
Power Efficiency ◽  
Joint Torque ◽  
Robot System ◽  
Operation Conditions ◽  
Efficiency Estimation ◽  
Joint Torque Sensor ◽  
Reconfigurable Robot ◽  
Critical Components

Power efficiency degradation of machines often provides intrinsic indication of problems associated with their operation conditions. Inspired by this observation, in this thesis work, a simple yet effective power efficiency estimation base health monitoring and fault detection technique is proposed for modular and reconfigurable robot with joint torque sensor. The design of the Ryerson modular and reconfigurable robot system is first introduced, which aims to achieve modularity and compactness of the robot modules. Critical components, such as the joint motor, motor driver, harmonic drive, sensors, and joint brake, have been selected according to the requirement. Power efficiency coefficients of each joint module are obtained using sensor measurements and used directly for health monitoring and fault detection. The proposed method has been experimentally tested on the developed modular and reconfigurable robot with joint torque sensing and a distributed control system. Experimental results have demonstrated the effectiveness of the proposed method.

Multiple faults detection and isolation approach applied to a kraft recovery boiler

TAPPI Journal ◽  
2014 ◽  
Vol 13 (1) ◽  
pp. 33-41
Author(s):  
YVON THARRAULT ◽  
MOULOUD AMAZOUZ
Keyword(s):  
Fault Detection ◽  
Early Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Principal Component ◽  
Fault Isolation ◽  
Fault Detection And Isolation ◽  
Detection Scheme ◽  
Multiple Faults ◽  
Recovery Boiler

Recovery boilers play a key role in chemical pulp mills. Early detection of defects, such as water leaks, in a recovery boiler is critical to the prevention of explosions, which can occur when water reaches the molten smelt bed of the boiler. Early detection is difficult to achieve because of the complexity and the multitude of recovery boiler operating parameters. Multiple faults can occur in multiple components of the boiler simultaneously, and an efficient and robust fault isolation method is needed. In this paper, we present a new fault detection and isolation scheme for multiple faults. The proposed approach is based on principal component analysis (PCA), a popular fault detection technique. For fault detection, the Mahalanobis distance with an exponentially weighted moving average filter to reduce the false alarm rate is used. This filter is used to adapt the sensitivity of the fault detection scheme versus false alarm rate. For fault isolation, the reconstruction-based contribution is used. To avoid a combinatorial excess of faulty scenarios related to multiple faults, an iterative approach is used. This new method was validated using real data from a pulp and paper mill in Canada. The results demonstrate that the proposed method can effectively detect sensor faults and water leakage.

scholarly journals Distributed global actuator fault-detection scheme for a class of linear multi-agent systems with disturbances

2020 ◽  
Vol 53 (2) ◽  
pp. 4202-4207
Author(s):  
Anass Taoufik ◽  
Michael Defoort ◽  
Mohamed Djemai ◽  
Krishna Busawon ◽  
Juan Diego Sánchez-Torres
Keyword(s):  
Fault Detection ◽  
Actuator Fault ◽  
Multi Agent Systems ◽  
Detection Scheme ◽  
Agent Systems ◽  
Multi Agent

scholarly journals Deep Learning Based Pavement Inspection Using Self-Reconfigurable Robot

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2595
Author(s):  
Balakrishnan Ramalingam ◽  
Abdullah Aamir Hayat ◽  
Mohan Rajesh Elara ◽  
Braulio Félix Gómez ◽  
Lim Yi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
High Accuracy ◽  
Experimental Results ◽  
Mobile Mapping ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Reconfigurable Robot ◽  
Nvidia Gpu ◽  
Inspection Task

The pavement inspection task, which mainly includes crack and garbage detection, is essential and carried out frequently. The human-based or dedicated system approach for inspection can be easily carried out by integrating with the pavement sweeping machines. This work proposes a deep learning-based pavement inspection framework for self-reconfigurable robot named Panthera. Semantic segmentation framework SegNet was adopted to segment the pavement region from other objects. Deep Convolutional Neural Network (DCNN) based object detection is used to detect and localize pavement defects and garbage. Furthermore, Mobile Mapping System (MMS) was adopted for the geotagging of the defects. The proposed system was implemented and tested with the Panthera robot having NVIDIA GPU cards. The experimental results showed that the proposed technique identifies the pavement defects and litters or garbage detection with high accuracy. The experimental results on the crack and garbage detection are presented. It is found that the proposed technique is suitable for deployment in real-time for garbage detection and, eventually, sweeping or cleaning tasks.

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