Autoregressive modelling for rolling element bearing fault diagnosis

In this study, time series analysis and pattern recognition analysis are used effectively for the purposes of rolling bearing fault diagnosis. The main part of the suggested methodology is the autoregressive (AR) modelling of the measured vibration signals. This study suggests the use of a linear AR model applied to the signals after they are stationarized. The obtained coefficients of the AR model are further used to form pattern vectors which are in turn subjected to pattern recognition for differentiating among different faults and different fault sizes. This study explores the behavior of the AR coefficients and their changes with the introduction and the growth of different faults. The idea is to gain more understanding about the process of AR modelling for roller element bearing signatures and the relation of the coefficients to the vibratory behavior of the bearings and their condition

A new methodology for fault detection in rolling element bearings using singular spectrum analysis

In this study, a new methodology for fault detection in rolling element bearings is proposed, which is based on singular spectrum analysis (SSA). The main idea of the methodology is to build a baseline space from the feature vectors corresponding to the healthy bearing condition. This baseline space is made from the directions of the first three principal components, which are obtained from the decomposition stage of the singular spectrum analysis. Then, the lagged version of any new signal corresponding to a measured (possibly damaged) condition is projected onto this baseline space in order to assess its similarity to the baseline condition. The Euclidean norms of these projections are used to form three-dimensional feature vectors. The category of a new signal vector is determined on the basis of the Mahalanobis distance (MD) of its feature vector to the baseline ones. The methodology is validated using datasets acquired from two different test-rigs. From the results obtained for the correct classification rate, it is shown that this methodology performs very well. The suggested methodology also has simple steps and is easy to apply

A novel methodology to predict monthly municipal water demand based on weather variables scenario

This study provides a novel methodology to predict monthly water demand based on several weather variables scenarios by using combined techniques including discrete wavelet transform, principal component analysis, and particle swarm optimisation. To our knowledge, the adopted approach is the first technique to be proposed and applied in the water demand prediction. Compared to traditional methods, the developed methodology is superior in terms of predictive accuracy and runtime. Water consumption coupled with weather variables of the Melbourne City, from 2006 to 2015, were obtained from the South East Water retail company. The results showed that using data pre-processing techniques can significantly improve the quality of data and to select the best model input scenario. Additionally, it was noticed that the particle swarm optimisation algorithm accurately predicts the constants of the suggested model. Furthermore, the results confirmed that the proposed methodology accurately estimated the monthly data of municipal water demand based on a range of statistical criteria

A Novel Methodology for Prediction Urban Water Demand by Wavelet Denoising and Adaptive Neuro-Fuzzy Inference System Approach

Accurate and reliable urban water demand prediction is imperative for providing the basis to design, operate, and manage water system, especially under the scarcity of the natural water resources. A new methodology combining discrete wavelet transform (DWT) with an adaptive neuro-fuzzy inference system (ANFIS) is proposed to predict monthly urban water demand based on several intervals of historical water consumption. This ANFIS model is evaluated against a hybrid crow search algorithm and artificial neural network (CSA-ANN), since these methods have been successfully used recently to tackle a range of engineering optimization problems. The study outcomes reveal that 1) data preprocessing is essential for denoising raw time series and choosing the model inputs to render the highest model performance; 2) both methodologies, ANFIS and CSA-ANN, are statistically equivalent and capable of accurately predicting monthly urban water demand with high accuracy based on several statistical metric measures such as coefficient of efficiency (0.974, 0.971, respectively). This study could help policymakers to manage extensions of urban water system in response to the increasing demand with low risk related to a decision

Urban Water Demand Prediction for a City that Suffers from Climate Change and Population Growth: Gauteng Province case study

The proper management of municipal water system is essential to sustain cities and support water security of societies. Urban water estimating has always been a challenging task for managers of water utilities and policymakers. This paper applies a novel methodology that includes data pre-processing and Artificial Neural Network (ANN) optimized with Backtracking Search Algorithm (BSA-ANN) to estimate monthly water demand in relation to previous water consumption. Historical data of monthly water consumption in the Gauteng Province, South Africa, for the period 2007–2016, were selected for the creation and evaluation of the methodology. Data pre-processing techniques played a crucial role in the enhancing of the quality of the data before creating the prediction model. The BSA-ANN model yielded the best result with a root mean square error and a coefficient of efficiency of 0.0099 mega liters and 0.979, respectively. Also, it proved more efficient and reliable than the Crow Search Algorithm (CSA-ANN), based on the scale of error. Overall, this paper presents a new application for the hybrid model BSA-ANN that can be successfully used to predict water demand with high accuracy, in a city that heavily suffers from the impact of climate change and population growth

Prediction and Forecasting of Maximum Weather Temperature Using a Linear Autoregressive Model

This paper investigates the autoregressive (AR) model performance in prediction and forecasting the monthly maximum temperature. The temperature recordings are collected over 12 years (i.e., 144 monthly readings). All the data are stationaries, which is converted to be stationary, via obtaining the normal logarithm values. The recordings are then divided into 70% training and 30% testing sample. The training sample is used for determining the structure of the AR model while the testing sample is used for validating the obtained model in forecasting performance. A wide range of model order is selected and the most suitable order is selected in terms of the highest modelling accuracy. The study shows that the monthly maximum temperature can accurately be predicted and forecasted using the AR model

Forecasting of Air Maximum Temperature on Monthly Basis Using Singular Spectrum Analysis and Linear Autoregressive Model

In this research, the singular spectrum analysis technique is combined with a linear autoregressive model for the purpose of prediction and forecasting of monthly maximum air temperature. The temperature time series is decomposed into three components and the trend component is subjected for modelling. The performance of modelling for both prediction and forecasting is evaluated via various model fitness function. The results show that the current method presents an excellent performance in expecting the maximum air temperature in future based on previous recordings

Assessing the Potential of Hybrid-Based Metaheuristic Algorithms Integrated with ANNs for Accurate Reference Evapotranspiration Forecasting

Evapotranspiration (ETo) is one of the most important processes in the hydrologic cycle, with specific application to sustainable water resource management. As such, this study aims to evaluate the predictive ability of a novel method for monthly ETo estimation, using a hybrid model comprising data pre-processing and an artificial neural network (ANN), integrated with the hybrid particle swarm optimisation–grey wolf optimiser algorithm (PSOGWO). Monthly data from Al-Kut City, Iraq, over the period 1990 to 2020, were used for model training, testing, and validation. The predictive accuracy of the proposed model was compared with other cutting-edge algorithms, including the slime mould algorithm (SMA), the marine predators algorithm (MPA), and the constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithm (CPSOCGSA). A number of graphical methods and statistical criteria were used to evaluate the models, including root mean squared error (RMSE), Nash–Sutcliffe model efficiency (NSE), coefficient of determination (R2), maximum absolute error (MAE), and normalised mean standard error (NMSE). The results revealed that all the models are efficient, with high simulation levels. The PSOGWO–ANN model is slightly better than the other approaches, with an R2 = 0.977, MAE = 0.1445, and RMSE = 0.078. Due to its high predictive accuracy and low error, the proposed hybrid model can be considered a promising technique

Hybridised Artificial Neural Network model with Slime Mould Algorithm: A novel methodology for prediction urban stochastic water demand

Urban water demand prediction based on climate change is always challenging for water utilities because of the uncertainty which results from a sudden rise in water demand due to stochastic patterns of climatic factors. For this purpose, a novel combined methodology including, firstly, data pre-processing techniques were employed to decompose the time series of water and climatic factors by using Empirical Mode Decomposition and identifying the best model input via tolerance to avoid multi-collinearity. Second, the Artificial Neural Network (ANN) model was optimised by an up-to-date Slime Mould Algorithm (SMA-ANN) to predict the medium term of the stochastic signal of monthly urban water demand. Ten climatic factors over 16 years were used to simulate the stochastic signal of water demand. The results reveal that SMA outperforms Multi-Verse Optimiser and Backtracking Search Algorithm based on error scale. The performance of the hybrid model SMA-ANN is better than ANN (stand-alone) based on the range of statistical criteria. Generally, this methodology yields accurate results with a coefficient of determination of 0.9 and a mean absolute relative error of 0.001. This study can assist local water managers to efficiently manage the present water system and plan extensions to accommodate the increasing water demand

Updated Moving Forecasting Model of Air Maximum Temperature

In the current study, a moving forecasting model is used for the purpose of forecasting maximum air temperature. A number of recordings are used for building the AR model and next, to forecasting some temperature values ahead. Then the AR model coefficients are updating due to shifting the training sample by adding new temperature values in order to involve the change in temperature time series behaviour. The current work shows a high performance all over the temperature time series, which considered in the analysis