A Data-Driven Soft Sensor Based on Multilayer Perceptron Neural Network With a Double LASSO Approach

2020 ◽  
Vol 69 (7) ◽  
pp. 3972-3979 ◽  
Author(s):  
Yajun Fan ◽  
Bo Tao ◽  
Ying Zheng ◽  
Shi-Shang Jang
Keyword(s):  
Neural Network ◽  
Multilayer Perceptron ◽  
Soft Sensor ◽  
Data Driven

A Data-Driven, Statistical Feature-Based, Neural Network Method for Rotary Seal Prognostics

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
Madhumitha Ramachandran ◽  
Zahed Siddique
Keyword(s):  
Neural Network ◽  
Multilayer Perceptron ◽  
Accelerated Aging ◽  
Testing Procedure ◽  
Health Condition ◽  
Performance Degradation ◽  
Friction Torque ◽  
Data Driven ◽  
Rotary Seal ◽  
Unseen Data

Failure of the rotary seal is one of the foremost causes of breakdown in rotary machinery, and such a failure can be catastrophic, resulting in costly downtime and large expenses. Assessing the performance degradation of the rotary seal is very important for maintenance decision-making. Although significant progress has been made over the last 5 years to understand the degradation of seals using experimental verification and numerical simulation, there is a research gap on the data-driven-based tools and methods to assess the health condition of rotary seals. In this paper, we propose a data-driven-based performance degradation assessment approach to classify the running/health condition of rotary seals, which was not considered in the previous studies. Statistical time domain features are extracted from friction torque-based degradation signals collected from a rotary setup. Wrapper-based feature selection approach is used to select relevant features, with multilayer perceptron neural network utilized as a classification technique. To validate the proposed methodology, an accelerated aging and testing procedure is developed to capture the performance of rotary seals. The study findings indicate that multilayer perceptron (MLP) classifier built using features related to the amplitude of torque signal has a better classification accuracy for unseen data when compared with logistic regression and random forest classifiers.

scholarly journals Data-Driven Stability Assessment of Multilayer Long Short-Term Memory Networks

2021 ◽  
Vol 11 (4) ◽  
pp. 1829
Author(s):  
Davide Grande ◽  
Catherine A. Harris ◽  
Giles Thomas ◽  
Enrico Anderlini
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Short Term Memory ◽  
Moving Average ◽  
Machine Learning Algorithms ◽  
Physical Models ◽  
Data Driven ◽  
The Stability ◽  
And Control ◽  
Nonlinear Autoregressive

Recurrent Neural Networks (RNNs) are increasingly being used for model identification, forecasting and control. When identifying physical models with unknown mathematical knowledge of the system, Nonlinear AutoRegressive models with eXogenous inputs (NARX) or Nonlinear AutoRegressive Moving-Average models with eXogenous inputs (NARMAX) methods are typically used. In the context of data-driven control, machine learning algorithms are proven to have comparable performances to advanced control techniques, but lack the properties of the traditional stability theory. This paper illustrates a method to prove a posteriori the stability of a generic neural network, showing its application to the state-of-the-art RNN architecture. The presented method relies on identifying the poles associated with the network designed starting from the input/output data. Providing a framework to guarantee the stability of any neural network architecture combined with the generalisability properties and applicability to different fields can significantly broaden their use in dynamic systems modelling and control.

scholarly journals Real Time Multipurpose Smart Waste Classification Model for Efficient Recycling in Smart Cities Using Multilayer Convolutional Neural Network and Perceptron

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4916
Author(s):  
Ali Usman Gondal ◽  
Muhammad Imran Sadiq ◽  
Tariq Ali ◽  
Muhammad Irfan ◽  
Ahmad Shaf ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Multilayer Perceptron ◽  
Smart Cities ◽  
Classification Model ◽  
Rapid Urbanization ◽  
Metal Waste ◽  
Waste Classification ◽  
The City

Urbanization is a big concern for both developed and developing countries in recent years. People shift themselves and their families to urban areas for the sake of better education and a modern lifestyle. Due to rapid urbanization, cities are facing huge challenges, one of which is waste management, as the volume of waste is directly proportional to the people living in the city. The municipalities and the city administrations use the traditional wastage classification techniques which are manual, very slow, inefficient and costly. Therefore, automatic waste classification and management is essential for the cities that are being urbanized for the better recycling of waste. Better recycling of waste gives the opportunity to reduce the amount of waste sent to landfills by reducing the need to collect new raw material. In this paper, the idea of a real-time smart waste classification model is presented that uses a hybrid approach to classify waste into various classes. Two machine learning models, a multilayer perceptron and multilayer convolutional neural network (ML-CNN), are implemented. The multilayer perceptron is used to provide binary classification, i.e., metal or non-metal waste, and the CNN identifies the class of non-metal waste. A camera is placed in front of the waste conveyor belt, which takes a picture of the waste and classifies it. Upon successful classification, an automatic hand hammer is used to push the waste into the assigned labeled bucket. Experiments were carried out in a real-time environment with image segmentation. The training, testing, and validation accuracy of the purposed model was 0.99% under different training batches with different input features.

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