Temporal-logic-based Semantic Fault Diagnosis with Time-series Data from Industrial Internet of Things

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Gang Chen ◽  
Mei Liu ◽  
Zhaodan Kong
Keyword(s):  
Time Series ◽  
Fault Diagnosis ◽  
Internet Of Things ◽  
Temporal Logic ◽  
Time Series Data ◽  
Series Data ◽  
Industrial Internet Of Things ◽  
Industrial Internet

A Polishing Robot Force Control System Based on Time Series Data in Industrial Internet of Things

2021 ◽  
Vol 21 (2) ◽  
pp. 1-22
Author(s):  
Chen Zhang ◽  
Zhuo Tang ◽  
Kenli Li ◽  
Jianzhong Yang ◽  
Li Yang
Keyword(s):  
Time Series ◽  
Control System ◽  
Force Control ◽  
Time Series Data ◽  
Industrial Robot ◽  
Industrial Robots ◽  
Series Data ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Force Control System

Installing a six-dimensional force/torque sensor on an industrial arm for force feedback is a common robotic force control strategy. However, because of the high price of force/torque sensors and the closedness of an industrial robot control system, this method is not convenient for industrial mass production applications. Various types of data generated by industrial robots during the polishing process can be saved, transmitted, and applied, benefiting from the growth of the industrial internet of things (IIoT). Therefore, we propose a constant force control system that combines an industrial robot control system and industrial robot offline programming software for a polishing robot based on IIoT time series data. The system mainly consists of four parts, which can achieve constant force polishing of industrial robots in mass production. (1) Data collection module. Install a six-dimensional force/torque sensor at a manipulator and collect the robot data (current series data, etc.) and sensor data (force/torque series data). (2) Data analysis module. Establish a relationship model based on variant long short-term memory which we propose between current time series data of the polishing manipulator and data of the force sensor. (3) Data prediction module. A large number of sensorless polishing robots of the same type can utilize that model to predict force time series. (4) Trajectory optimization module. The polishing trajectories can be adjusted according to the prediction sequences. The experiments verified that the relational model we proposed has an accurate prediction, small error, and a manipulator taking advantage of this method has a better polishing effect.

Fault diagnosis based on feature clustering of time series data for loss and kick of drilling process

2021 ◽  
Vol 102 ◽  
pp. 24-33
Author(s):  
Zheng Zhang ◽  
Xuzhi Lai ◽  
Min Wu ◽  
Luefeng Chen ◽  
Chengda Lu ◽  
...  
Keyword(s):  
Time Series ◽  
Fault Diagnosis ◽  
Time Series Data ◽  
Series Data ◽  
Drilling Process ◽  
Feature Clustering

Fast and Accurate Deep Learning Framework for Secure Fault Diagnosis in the Industrial Internet of Things

2021 ◽  
pp. 1-1
Author(s):  
Youcef Djenouri ◽  
Asma Belhadi ◽  
Gautam Srivastava ◽  
Uttam Ghosh ◽  
Pushpita Chatterjee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Internet Of Things ◽  
Industrial Internet Of Things ◽  
Learning Framework ◽  
Industrial Internet
Sign in / Sign up

Export Citation Format

Share Document