scholarly journals Predictive Process Adjustment by Detecting System Status of Vacuum Gripper in Real Time during Pick-Up Operations

Processes ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 634
Author(s):  
Sujeong Baek ◽  
Dong Oh Kim
Keyword(s):  
Real Time ◽  
Manufacturing Systems ◽  
Air Pressure ◽  
Economic Losses ◽  
Stable Operation ◽  
System Operation ◽  
Process Adjustment ◽  
Manufacturing Errors ◽  
Predictive Process ◽  
Considerable Loss

In manufacturing systems, pick-up operations by vacuum grippers may fail owing to manufacturing errors in an object’s surface that are within the allowable tolerance limits. In such situations, manual interference is required to resume system operation, which results in considerable loss of time as well as economic losses. Although vacuum grippers have many advantages and are widely used in the industry, it is highly difficult to directly monitor the current machine status and provide appropriate recovery feedback for stable operation. Therefore, this paper proposes a method to detect the success or failure of a suction operation in advance by analyzing the amount of outlet air pressure in the Venturi line. This was achieved by installing an air pressure sensor on the Venturi line to predict whether the current suction action will be successful. Through empirical experiments, it was found that downward movements in the z-axis of the vacuum gripper can easily rectify a faulty gripper suction operation. Real-time monitoring results verified that predictive process adjustment of the pick-up operation can be performed by modifying the z-position of the vacuum gripper.

scholarly journals System integration for predictive process adjustment and cloud computing-based real-time condition monitoring of vibration sensor signals in automated storage and retrieval systems

2021 ◽  
Author(s):  
Sujeong Baek
Keyword(s):  
Cloud Computing ◽  
Fault Detection ◽  
System Integration ◽  
Manufacturing Systems ◽  
Fault Analysis ◽  
Storage And Retrieval ◽  
Process Adjustment ◽  
Predictive Process ◽  
Sensor Signals ◽  
Automated Storage

AbstractAs automation and digitalization are being increasingly implemented in industrial applications, manufacturing systems comprising several functions are becoming more complex. Consequently, fault analysis (e.g., fault detection, diagnosis, and prediction) has attracted increased research attention. Investigations involving fault analysis are usually performed using real-time, online, or automated techniques for fault detection or alarming. Conversely, recovery of faulty states to their healthy forms is usually performed manually under offline conditions. However, the development of intelligent systems requires that appropriate feedback be provided automatically, to facilitate faulty-state recovery without the need for manual operator intervention and/or decision-making. To this end, this paper proposes a system integration technique for predictive process adjustment that determines appropriate recovery actions and performs them automatically by analyzing relevant sensor signals pertaining to the current situation of a manufacturing unit via cloud computing and machine learning. The proposed system corresponds to an automated predictive process adjustment module of an automated storage and retrieval system (ASRS). The said integrated module collects and analyzes the temperature and vibration signals of a product transporter using an internet-of-things-based programmable logic controller and cloud computing to identify the current states of the ASRS system. Upon detection of faulty states, the control program identifies corresponding process control variables and controls them to recover the system to its previous no-fault state. The proposed system will facilitate automatic prognostics and health management in complex manufacturing systems by providing automatic fault diagnosis and predictive recovery feedback.

scholarly journals A Predictive Noise Correction Methodology for Manufacturing Systems Datasets

2020 ◽  
Author(s):  
Omogbai Oleghe
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Process Monitoring ◽  
Manufacturing Process ◽  
Manufacturing Systems ◽  
Missing Values ◽  
Feature Reduction ◽  
Streaming Data ◽  
Learning Performance ◽  
Predictive Process

Abstract In manufacturing systems, datasets intended for data driven decisions are majorly generated from time-sequenced sensor readings. Industrial sensors are prone to transmit inaccurate readings due to various reasons, which results in noisy datasets. Noisy datasets inhibit machine learning and knowledge discovery. This article reports a methodology that was developed to rectify a very noisy dataset of a multi-stage continuous manufacturing process, with multi-feature output. In the methodology, erroneous values are first swapped with missing values. Then, feature reduction modelling is used as a precursor of prediction learning to improve machine learning performance. Afterwards, a classifier model is built to predict replacement values for the missing values. Finally, predicted values are inputted to replace missing values in the dataset. With many attributes having erroneous values, the values replacement is done one attribute at a time. The flow direction and stages in the manufacturing process are used in partitioning the dataset to improve learning performance. With the methodology, important system relationships are identified and the dataset is learnt for predictive process monitoring. There is a paucity of this type of methodology for dealing with noisy datasets in manufacturing systems. In the future, the plan is to inject the prediction model into streaming data to enable real-time erroneous value correction and real-time predictive process monitoring at the same time.

scholarly journals Real-Time Monitoring and Fault Diagnosis of a Low Power Hub Motor Using Feedforward Neural Network

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Mehmet Şimşir ◽  
Raif Bayır ◽  
Yılmaz Uyaroğlu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Low Power ◽  
Real Time ◽  
Economic Losses ◽  
Real Time Monitoring ◽  
System Operation ◽  
Compact Structure ◽  
Neural Network Approach ◽  
Data Set

Low power hub motors are widely used in electromechanical systems such as electrical bicycles and solar vehicles due to their robustness and compact structure. Such systems driven by hub motors (in wheel motors) encounter previously defined and undefined faults under operation. It may inevitably lead to the interruption of the electromechanical system operation; hence, economic losses take place at certain times. Therefore, in order to maintain system operation sustainability, the motor should be precisely monitored and the faults are diagnosed considering various significant motor parameters. In this study, the artificial feedforward backpropagation neural network approach is proposed to real-time monitor and diagnose the faults of the hub motor by measuring seven main system parameters. So as to construct a necessary model, we trained the model, using a data set consisting of 4160 samples where each has 7 parameters, by the MATLAB environment until the best model is obtained. The results are encouraging and meaningful for the specific motor and the developed model may be applicable to other types of hub motors. The prosperous model of the whole system was embedded into Arduino Due microcontroller card and the mobile real-time monitoring and fault diagnosis system prototype for hub motor was designed and manufactured.

scholarly journals Power-Based Non-Intrusive Condition Monitoring for Terminal Device in Smart Grid

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3635 ◽  
Author(s):  
Guoming Zhang ◽  
Xiaoyu Ji ◽  
Yanjie Li ◽  
Wenyuan Xu
Keyword(s):  
Smart Grid ◽  
Power Consumption ◽  
Real Time ◽  
Condition Monitoring ◽  
Resource Constraints ◽  
Detection System ◽  
Stable Operation ◽  
Detection Accuracy ◽  
Monitoring Method ◽  
Dsp Processors

As a critical component in the smart grid, the Distribution Terminal Unit (DTU) dynamically adjusts the running status of the entire smart grid based on the collected electrical parameters to ensure the safe and stable operation of the smart grid. However, as a real-time embedded device, DTU has not only resource constraints but also specific requirements on real-time performance, thus, the traditional anomaly detection method cannot be deployed. To detect the tamper of the program running on DTU, we proposed a power-based non-intrusive condition monitoring method that collects and analyzes the power consumption of DTU using power sensors and machine learning (ML) techniques, the feasibility of this approach is that the power consumption is closely related to the executing code in CPUs, that is when the execution code is tampered with, the power consumption changes accordingly. To validate this idea, we set up a testbed based on DTU and simulated four types of imperceptible attacks that change the code running in ARM and DSP processors, respectively. We generate representative features and select lightweight ML algorithms to detect these attacks. We finally implemented the detection system on the windows and ubuntu platform and validated its effectiveness. The results show that the detection accuracy is up to 99.98% in a non-intrusive and lightweight way.

scholarly journals Research on Multi-Timescale Coordinated Method for Source-Grid-Load with Uncertain Renewable Energy Considering Demand Response

2021 ◽  
Vol 13 (6) ◽  
pp. 3400
Author(s):  
Jia Ning ◽  
Sipeng Hao ◽  
Aidong Zeng ◽  
Bin Chen ◽  
Yi Tang
Keyword(s):  
Renewable Energy ◽  
Real Time ◽  
Demand Response ◽  
System Operation ◽  
Real Time Scheduling ◽  
High Penetration ◽  
Time Scheduling ◽  
Grid Load ◽  
Grid Side ◽  
Scheduling Models

The high penetration of renewable energy brings great challenges to power system operation and scheduling. In this paper, a multi-timescale coordinated method for source-grid-load is proposed. First, the multi-timescale characteristics of wind forecasting power and demand response (DR) resources are described, and the coordinated framework of source-grid-load is presented under multi-timescale. Next, economic scheduling models of source-grid-load based on multi-timescale DR under network constraints are established in the process of day-ahead scheduling, intraday scheduling, and real-time scheduling. The loads are classified into three types in terms of different timescale. The security constraints of grid side and time-varying DR potential are considered. Three-stage stochastic programming is employed to schedule resources of source side and load side in day-ahead, intraday, and real-time markets. The simulations are performed in a modified Institute of Electrical and Electronics Engineers (IEEE) 24-node system, which shows a notable reduction in total cost of source-grid-load scheduling and an increase in wind accommodation, and their results are proposed and discussed against under merely two timescales, which demonstrates the superiority of the proposed multi-timescale models in terms of cost and demand response quantity reduction.

Investigation of Synchronous Accuracy of Dual Arm Motion of Industrial Robot

2012 ◽  
Vol 516 ◽  
pp. 234-239 ◽  
Author(s):  
Wei Wu ◽  
Toshiki Hirogaki ◽  
Eiichi Aoyama
Keyword(s):  
Rotational Motion ◽  
Manufacturing Systems ◽  
Industrial Robot ◽  
Circular Path ◽  
Stable Operation ◽  
Motion Error ◽  
Ball Rolling ◽  
Arm Motion ◽  
Five Axis ◽  
Dual Arm

Recently, new needs have emerged to control not only linear motion but also rotational motion in high-accuracy manufacturing fields. Many five-axis-controlled machining centres are therefore in use. However, one problem has been the difficulty of creating flexible manufacturing systems with methods based on the use of these machine tools. On the other hand, the industrial dual-arm robot has gained attention as a new way to achieve accurate linear and rotational motion in an attempt to control a working plate like a machine tool table. In the present report, a cooperating dual-arm motion is demonstrated to make it feasible to perform stable operation control, such as controlling the working plate to keep a ball rolling around a circular path on it. As a result, we investigated the influence of each axis motion error on a ball-rolling path.

A novel real-time method for HVAC system operation to improve indoor environmental quality in meeting rooms

2018 ◽  
Vol 144 ◽  
pp. 365-385 ◽  
Author(s):  
Wenzhuo Li ◽  
Choongwan Koo ◽  
Seung Hyun Cha ◽  
Taehoon Hong ◽  
Jeongyoon Oh
Keyword(s):  
Real Time ◽  
Environmental Quality ◽  
Indoor Environmental Quality ◽  
Hvac System ◽  
System Operation

Real-Time Maintenance Policy in Manufacturing Systems With Intermediate Buffers

2015 ◽  
Author(s):  
Xi Gu ◽  
Xiaoning Jin ◽  
Jun Ni
Keyword(s):  
Real Time ◽  
Manufacturing Systems ◽  
Maintenance Policy ◽  
Discrete Time Markov Chain ◽  
Markov Decision ◽  
Maintenance Decision ◽  
Maintenance Policies ◽  
Time Buffer ◽  
Buffer Capacities ◽  
One Machine

Real-time maintenance decision making in large manufacturing system is complex because it requires the integration of different information, including the degradation states of machines, as well as inventories in the intermediate buffers. In this paper, by using a discrete time Markov chain (DTMC) model, we consider the real-time maintenance policies in manufacturing systems consisting of multiple machines and intermediate buffers. The optimal policy is investigated by using a Markov Decision Process (MDP) approach. This policy is compared with a baseline policy, where the maintenance decision on one machine only depends on its degradation state. The result shows how the structures of the policies are affected by the buffer capacities and real-time buffer levels.

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