Fuzzy Nonlinear Regression Analysis Using Fuzzified Neural Networks for Fault Diagnosis of Chemical Plants

In systems such as chemical plants or circulatory systems, failure of piping, sensors or valves causes serious problems. These failures can be avoided by the increase in sensors and operators for condition monitoring. However, since adding sensors and operators leads to an increase in cost, it is difficult to realize. In this paper, a technique of diagnosing target systems based on a fuzzy nonlinear regression is proposed by using a fuzzified neural network that is trained with time-series data with reliability grades. Our proposed technique uses numerical data recorded by the existing monitoring system. Reliability grades are beforehand given to the recorded data by domain experts. The state of a target system is determined based on the fuzzy output from the trained fuzzified neural network. Our proposed technique makes us determine easily the state of the target systems. Our proposed technique is flexibly applicable to various types of systems by considering some parameters for failure determination of target systems.

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Predicting machine failure using recurrent neural network-gated recurrent unit (RNN-GRU) through time series data

Bulletin of Electrical Engineering and Informatics ◽

10.11591/eei.v10i2.2036 ◽

2021 ◽

Vol 10 (2) ◽

pp. 870-878

Author(s):

Zainuddin Z. ◽

P. Akhir E. A. ◽

Hasan M. H.

Keyword(s):

Neural Network ◽

Time Series ◽

Recurrent Neural Network ◽

Time Series Data ◽

Short Term Memory ◽

Learning Algorithm ◽

The State ◽

Series Data ◽

Machine Failure ◽

Gated Recurrent Unit

Time series data often involves big size environment that lead to high dimensionality problem. Many industries are generating time series data that continuously update each second. The arising of machine learning may help in managing the data. It can forecast future instance while handling large data issues. Forecasting is related to predicting task of an upcoming event to avoid any circumstances happen in current environment. It helps those sectors such as production to foresee the state of machine in line with saving the cost from sudden breakdown as unplanned machine failure can disrupt the operation and loss up to millions. Thus, this paper offers a deep learning algorithm named recurrent neural network-gated recurrent unit (RNN-GRU) to forecast the state of machines producing the time series data in an oil and gas sector. RNN-GRU is an affiliation of recurrent neural network (RNN) that can control consecutive data due to the existence of update and reset gates. The gates decided on the necessary information to be kept in the memory. RNN-GRU is a simpler structure of long short-term memory (RNN-LSTM) with 87% of accuracy on prediction.

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AN ARTIFICIAL NEURAL NETWORK BASED DEVELOPMENT OF CLOUD BURST FORECASTING MODEL BY USING TIME-SERIES DATA.

June-2020 - International Journal of Engineering Sciences & Research Technology ◽

10.29121/ijesrt.v9.i10.2020.14 ◽

2020 ◽

Vol 9 (10) ◽

pp. 143-155

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Prediction Accuracy ◽

Time Series Data ◽

Weather Forecasting ◽

Fitness Function ◽

Optimization Technique ◽

Numerical Data ◽

Series Data ◽

Artificial Neural

Over the last decades, weather forecasting models using the numerical data of various previous years have been improving steadily to provide a more predictable and accurate model. However, the atmosphere conditions are a highly chaotic system and always vary with time and weather forecasts are a major benefit for society and sustainable development. So, in this research we focus to develop a model using the optimized Artificial Neural Network (ANN) for prediction of cloud bursting in India and developed model in known as Cloud Burst Foresting (CBF) model for forecasting of rainfall or cloud burst based on the previous record of the bursting in different state. Here the concept of Particle Swarm Optimization (PSO) is used as an optimization technique in pre-processing step to separate the previous year rainfalls data into two categories such minimum and maximum rainfalls. Basically, PSO separate the cloud burst recorded data using a novel fitness function that help to train the CBF model accuracy and if training is better, then the prediction accuracy will be high. By utilizing the concept of optimized ANN, the prediction accuracy is high in terms of percentage of correct predication and with minimum percentage of incorrect prediction. At last, the performance of the model is calculated to validate the proposed CBF model and this shows that it is possible to use ANN as a machine learning technique in order to estimate future cloud burst forecast uncertainty from past forecasts data of bursting. The main constraint in the performance of our proposed CBF model seems to be the number of past forecasts available for training the ANN.

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Neural Network Techniques for Time Series Prediction: A Review

JOIV International Journal on Informatics Visualization ◽

10.30630/joiv.3.3.281 ◽

2019 ◽

Vol 3 (3) ◽

Author(s):

Muhammad Faheem Mushtaq ◽

Urooj Akram ◽

Muhammad Aamir ◽

Haseeb Ali ◽

Muhammad Zulqarnain

Keyword(s):

Neural Network ◽

Neural Networks ◽

Time Series ◽

Time Series Data ◽

Weather Prediction ◽

Time Series Prediction ◽

Series Data ◽

Prediction Problem ◽

Neural Network Models ◽

Physical Time

It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series. Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN.

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Neural networks approached for modelling river suspended sediment concentration due to tropical storms

Global NEST Journal ◽

10.30955/gnj.000628 ◽

2013 ◽

Vol 11 (4) ◽

pp. 457-466

Keyword(s):

Neural Network ◽

Neural Networks ◽

Suspended Sediment ◽

Suspended Sediment Concentration ◽

Time Series Data ◽

Water Discharge ◽

Sediment Concentration ◽

Series Data ◽

Generalized Regression Neural Network ◽

Event Based

Artificial neural networks are one of the advanced technologies employed in hydrology modelling. This paper investigates the potential of two algorithm networks, the feed forward backpropagation (BP) and generalized regression neural network (GRNN) in comparison with the classical regression for modelling the event-based suspended sediment concentration at Jiasian diversion weir in Southern Taiwan. For this study, the hourly time series data comprised of water discharge, turbidity and suspended sediment concentration during the storm events in the year of 2002 are taken into account in the models. The statistical performances comparison showed that both BP and GRNN are superior to the classical regression in the weir sediment modelling. Additionally, the turbidity was found to be a dominant input variable over the water discharge for suspended sediment concentration estimation. Statistically, both neural network models can be successfully applied for the event-based suspended sediment concentration modelling in the weir studied herein when few data are available.

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Towards Providing Effective Data-Driven Responses to Predict the Covid-19 in São Paulo and Brazil

Sensors ◽

10.3390/s21020540 ◽

2021 ◽

Vol 21 (2) ◽

pp. 540

Author(s):

Fabio Amaral ◽

Wallace Casaca ◽

Cassio M. Oishi ◽

José A. Cuminato

Keyword(s):

Learning Strategies ◽

Time Series Data ◽

Sao Paulo ◽

The State ◽

Data Driven ◽

Series Data ◽

São Paulo ◽

Time Data ◽

Unified Framework ◽

Infected People

São Paulo is the most populous state in Brazil, home to around 22% of the country’s population. The total number of Covid-19-infected people in São Paulo has reached more than 1 million, while its total death toll stands at 25% of all the country’s fatalities. Joining the Brazilian academia efforts in the fight against Covid-19, in this paper we describe a unified framework for monitoring and forecasting the Covid-19 progress in the state of São Paulo. More specifically, a freely available, online platform to collect and exploit Covid-19 time-series data is presented, supporting decision-makers while still allowing the general public to interact with data from different regions of the state. Moreover, a novel forecasting data-driven method has also been proposed, by combining the so-called Susceptible-Infectious-Recovered-Deceased model with machine learning strategies to better fit the mathematical model’s coefficients for predicting Infections, Recoveries, Deaths, and Viral Reproduction Numbers. We show that the obtained predictor is capable of dealing with badly conditioned data samples while still delivering accurate 10-day predictions. Our integrated computational system can be used for guiding government actions mainly in two basic aspects: real-time data assessment and dynamic predictions of Covid-19 curves for different regions of the state. We extend our analysis and investigation to inspect the virus spreading in Brazil in its regions. Finally, experiments involving the Covid-19 advance in other countries are also given.

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Remaining Useful Life Prediction Using Temporal Convolution with Attention

AI ◽

10.3390/ai2010005 ◽

2021 ◽

Vol 2 (1) ◽

pp. 48-70

Author(s):

Wei Ming Tan ◽

T. Hui Teo

Keyword(s):

Neural Network ◽

Time Series ◽

Time Series Data ◽

Remaining Useful Life ◽

Sensor Data ◽

Series Data ◽

Multiple Time ◽

Data Set ◽

Form Complex ◽

Useful Life

Prognostic techniques attempt to predict the Remaining Useful Life (RUL) of a subsystem or a component. Such techniques often use sensor data which are periodically measured and recorded into a time series data set. Such multivariate data sets form complex and non-linear inter-dependencies through recorded time steps and between sensors. Many current existing algorithms for prognostic purposes starts to explore Deep Neural Network (DNN) and its effectiveness in the field. Although Deep Learning (DL) techniques outperform the traditional prognostic algorithms, the networks are generally complex to deploy or train. This paper proposes a Multi-variable Time Series (MTS) focused approach to prognostics that implements a lightweight Convolutional Neural Network (CNN) with attention mechanism. The convolution filters work to extract the abstract temporal patterns from the multiple time series, while the attention mechanisms review the information across the time axis and select the relevant information. The results suggest that the proposed method not only produces a superior accuracy of RUL estimation but it also trains many folds faster than the reported works. The superiority of deploying the network is also demonstrated on a lightweight hardware platform by not just being much compact, but also more efficient for the resource restricted environment.

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