A Generalized Multisensor Real-Time Tool Condition–Monitoring Approach Using Deep Recurrent Neural Network

2019 ◽  
Vol 3 (2) ◽  
pp. 20190020 ◽  
Author(s):  
M. Hassan ◽  
A. Sadek ◽  
M. H. Attia
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Condition Monitoring ◽  
Recurrent Neural Network ◽  
Tool Condition Monitoring ◽  
Tool Condition ◽  
Deep Recurrent Neural Network

Tool Condition Monitoring Using Artificial Neural Network Models

2022 ◽  
pp. 400-426
Author(s):  
Srinivasa P. Pai ◽  
Nagabhushana T. N.
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Condition Monitoring ◽  
Medium Carbon Steel ◽  
Tool Condition Monitoring ◽  
Training Time ◽  
Tool Condition ◽  
Cell Structures ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Tool wear is a major factor that affects the productivity of any machining operation and needs to be controlled for achieving automation. It affects the surface finish, tolerances, dimensions of the workpiece, increases machine down time, and sometimes performance of machine tool and personnel are affected. This chapter deals with the application of artificial neural network (ANN) models for tool condition monitoring (TCM) in milling operations. The data required for training and testing the models studied and developed are from live experiments conducted in a machine shop on a widely used steel, medium carbon steel (En 8) using uncoated carbide inserts. Acoustic emission data and surface roughness data has been used in model development. The goal is for developing an optimal ANN model, in terms of compact architecture, least training time, and its ability to generalize well on unseen (test) data. Growing cell structures (GCS) network has been found to achieve these requirements.

scholarly journals A Deep Recurrent Neural Network for Non-Intrusive Load Monitoring Based on Multi-Feature Input Space and Post-Processing

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2195
Author(s):  
Hasan Rafiq ◽  
Xiaohan Shi ◽  
Hengxu Zhang ◽  
Huimin Li ◽  
Manesh Kumar Ochani
Keyword(s):  
Neural Network ◽  
Power Consumption ◽  
Real Time ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Estimation Accuracy ◽  
Post Processing ◽  
Input Space ◽  
Deep Recurrent Neural Network ◽  
Load Monitoring

Non-intrusive load monitoring (NILM) is a process of estimating operational states and power consumption of individual appliances, which if implemented in real-time, can provide actionable feedback in terms of energy usage and personalized recommendations to consumers. Intelligent disaggregation algorithms such as deep neural networks can fulfill this objective if they possess high estimation accuracy and lowest generalization error. In order to achieve these two goals, this paper presents a disaggregation algorithm based on a deep recurrent neural network using multi-feature input space and post-processing. First, the mutual information method was used to select electrical parameters that had the most influence on the power consumption of each target appliance. Second, selected steady-state parameters based multi-feature input space (MFS) was used to train the 4-layered bidirectional long short-term memory (LSTM) model for each target appliance. Finally, a post-processing technique was used at the disaggregation stage to eliminate irrelevant predicted sequences, enhancing the classification and estimation accuracy of the algorithm. A comprehensive evaluation was conducted on 1-Hz sampled UKDALE and ECO datasets in a noised scenario with seen and unseen test cases. Performance evaluation showed that the MFS-LSTM algorithm is computationally efficient, scalable, and possesses better estimation accuracy in a noised scenario, and generalized to unseen loads as compared to benchmark algorithms. Presented results proved that the proposed algorithm fulfills practical application requirements and can be deployed in real-time.

Open Platform for the Integration of Monitoring Applications

2002 ◽  
Author(s):  
Andreas Kahmen ◽  
Manfred Weck
Keyword(s):  
Control System ◽  
Machine Tool ◽  
Real Time ◽  
Condition Monitoring ◽  
Tool Condition Monitoring ◽  
Integration Platform ◽  
Open Platform ◽  
Tool Condition ◽  
Monitoring Applications ◽  
Monitoring Software

Process and machine tool condition monitoring are the keys to an increasing degree of automation and consequently to an increasing productivity in manufacturing. The realisation of monitoring functionality demands an extension of the control system. The prerequisite for these extensions are open interfaces in the NC-kernel. Nowadays controls with open NC-kernel interfaces are available on the market. However these interfaces are vendor specific solutions that do not allow the reuse of monitoring software in different controls. To overcome these limitations a platform with vendor neutral open real-time interface for the integration of monitoring functionality into the NC-kernel is presented in this paper. Additionally two realisations of the integration platform for different target systems are described.

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