Abstract
This paper intends to develop ANN (artificial neural network) based control charts. The (ANN) is a machine learning (ML) methodology that evolved and developed from the scheme of imitating the human brain. ANN has been explained by discussing the network topology and development parameters (number of nodes, number of hidden layers, learning rules, and activated function). Among many models that deal with combining factors and data-based supervised learning classifiers, ANN has the most significant impact on air quality as air quality has nonlinear and noisy data. The best activation of a new hybrid EWMA (HEWMA) control chart is proposed by mixing two EWMA control charts to efficiently monitor the process mean. The ANN-based HEWMA scheme was a promising procedure for the detection of air quality measurements. We compare the performance of the ANN-based HEWMA control chart and the EWMA control chart based on average run lengths when the data are contaminated with the measurement error. The results revealed that the higher the temperature, the better fitting shape we obtain from air quality parameters. The ANN-based HEWMA control chart deals with measurement errors more efficiently than the EWMA control chart.
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References
D.C. Montgomery, G.C. Runger and N.F. Hubele. Engineering Statistics. Wiley (2009).
Shewhart, W. A., & Deming, W. E. (1967). In Memoriam: Walter A. Shewhart, 1891–1967. The American Statistician, 21(2), 39–40. https://doi.org/10.1080/00031305.1967.10481808
H. Khan, S. Farooq, M. Aslam and M.A. Khan, Exponentially weighted moving average control charts for the process mean using exponential ratio type estimator. Journal of Probability and Statistics, 2018 (2018) 1–15. https://doi.org/10.1155/2018/9413939.
M. Aslam, S.R. Gadde, M.S. Aldosari and C.-H. Jun, A hybrid EWMA chart using coefficient of variation. International Journal of Quality & Reliability Management, 36(4) (2019) 587–600. https://doi.org/10.1108/IJQRM-12-2017-0285.
J. Zhang, Z. Li, B. Chen and Z. Wang. A new exponentially weighted moving average control chart for monitoring the coefficient of variation. Computers & Industrial Engineering. 78 (2014) 205–212.
Raza, S. M. M., Sial, M. H., Haider, M., & Butt, M. M. (2019). Hybrid exponentially weighted moving average (HEWMA) control chart based on exponential type estimator of mean. Journal of Reliability and Statistical Studies, 187–198. https://doi.org/10.13052/jrss2229-5666.12214
D.W. Apley and H. Cheol Lee, Design of exponentially weighted moving average control charts for autocorrelated processes with model uncertainty. Technometrics, 45(3) (2003) 187–198. https://doi.org/10.1198/004017003000000014.
Vişoiu, A. (2008). Neural network based model refinement. Revista Informatica Economică, 45(11).
Fernandes, P. O., & Teixeira, J. P. (2008). Applying the artificial neural network methodology for forecasting the tourism time series.
M. Riaz, S. Ahmad, T. Mahmood and N. Abbas, On Reassessment of the HWMA Chart for process monitoring. Processes, 10(6) (2022) 1129. https://doi.org/10.3390/pr10061129.
F. Ahmadzadeh, Change point detection with multivariate control charts by artificial neural network. The International Journal of Advanced Manufacturing Technology, 97(9–12) (2018) 3179–3190. https://doi.org/10.1007/s00170-009-2193-6.
Haider, A., & Muhammad Nadeem Hanif. (2009). Inflation forecasting in Pakistan using artificial neural networks. Pakistan Economic and Social Review.
J. Yu and L. Xi, A neural network ensemble-based model for on-line monitoring and diagnosis of out-of-control signals in multivariate manufacturing processes. Expert Systems with Applications, 36(1) (2009) 909–921. https://doi.org/10.1016/j.eswa.2007.10.003.
B. Abbasi, A neural network applied to estimate process capability of non-normal processes. Expert Systems with Applications, 36(2) (2009) 3093–3100. https://doi.org/10.1016/j.eswa.2008.01.042.
C. Zou and F. Tsung, Likelihood ratio-based distribution-free EWMA control charts. Journal of Quality Technology, 42(2) (2010) 174–196. https://doi.org/10.1080/00224065.2010.11917815.
N. Abbas, M. Riaz and R.J.M.M. Does, Enhancing the performance of EWMA charts. Quality and Reliability Engineering International, 27(6) (2011) 821–833. https://doi.org/10.1002/qre.1175.
N. Abbas, R.F. Zafar, M. Riaz and Z. Hussain, Progressive mean control chart for monitoring process location parameter. Quality and Reliability Engineering International, 29(3) (2013) 357–367. https://doi.org/10.1002/qre.1386.
Alireza Aliahmadi, Meisam Jafari-Eskandari, Azimeh Mozafari and Hamed Nozari. International Journal of Information, Business and Management, 5(2) (2013) 41–58.
SM Nimbale, & VB Ghute. (2016). A neural network based individual control chart. International Journal of Engineering Research.
S.M. Nimbale and V.B. Ghute, Monitoring process mean and variability using artificial neural networks. Nt. J. Sci. Res. in Mathematical and Statistical Sciences, 6 (2019) 1–6.
S. Tariq, M. Noor-ul-Amin, M. Aslam and M. Hanif, Design of hybrid EWMln- S2 control chart. Journal of Industrial and Production Engineering, 36(8) (2019) 554–562. https://doi.org/10.1080/21681015.2019.1702111.
F. Asif, S. Khan and M. Noor-ul-Amin, Hybrid exponentially weighted moving average control chart with measurement error. Iranian Journal of Science and Technology, Transactions A: Science, 44(3) (2020) 801–811. https://doi.org/10.1007/s40995-020-00879-3.
Minn, Y., & Hassan, A. (2021). Performance of EWMA and ANN-based schemes in detection of denial of service attack. In IOP Conference Series: Materials Science and Engineering, 1096(1), 012009. https://doi.org/10.1088/1757-899X/1096/1/012009
S. Rostami, R. Kalbasi, N. Sina and A.S. Goldanlou, Forecasting the thermal conductivity of a nanofluid using artificial neural networks. Journal of Thermal Analysis and Calorimetry, 145(4) (2021) 2095–2104. https://doi.org/10.1007/s10973-020-10183-2.
Salah Alaloul, W., & Hannan Qureshi, A. (2020). Data processing using artificial neural networks. In Dynamic Data Assimilation-Beating the Uncertainties. IntechOpen. https://doi.org/10.5772/intechopen.91935
S. Sukparungsee, Y. Areepong and R. Taboran, Exponentially weighted moving average—moving average charts for monitoring the process mean. PLOS ONE, 15(2) (2020) e0228208. https://doi.org/10.1371/journal.pone.0228208.
B. Wang, X. Gu, L. Ma and S. Yan, Temperature error correction based on BP neural network in meteorological wireless sensor network. International Journal of Sensor Networks, 23(4) (2017) 265. https://doi.org/10.1504/IJSNET.2017.083532.
D. Rosa Lakus, M. Pizzolato, F. de Medeiros Albano and P. Langer Menin, Shewhart, CUSUM and EWMA control charts: A comparative study on intermediate check of balances. J. Metrol. Soc India, 37(2) (2022) 453–464. https://doi.org/10.1007/s12647-021-00511-8.
L. Chen and S. Yang, A new p -control chart with measurement error correction. Quality and Reliability Engineering International, 39(1) (2023) 81–98. https://doi.org/10.1002/qre.3219.
Volodarskyi, Y., Kozyr, O., & Kosheva, L. (2022). Control charts based on principal components. In 2022 XXXII International Scientific Symposium Metrology and Metrology Assurance (MMA), 1–4.https://doi.org/10.1109/MMA55579.2022.9992823
Dyuthi Sanjeev, Implementation of machine learning algorithms for analysis and prediction of air quality, I. International Journal of Engineering Research & Technology (IJERT), 10(3) (2021) 2278–181.
E.S. Page, Continuous inspection schemes. Biometrika (2006). https://doi.org/10.2307/2333009.
S.W. Roberts, Control chart tests based on geometric moving averages. Technometrics (1959). https://doi.org/10.1080/00401706.1959.10489860.
A. Haq, A new hybrid exponentially weighted moving average control chart for monitoring process mean: Discussion. Quality and Reliability Engineering International, 33(7) (2017) 1629–1631. https://doi.org/10.1002/qre.2092.
T. Kohonen, An introduction to neural computing. Neural Networks, 1(1) (1988) 3–16. https://doi.org/10.1016/0893-6080(88)90020-2.
A.E. Smith, X -bar and R control chart interpretation using neural computing. International Journal of Production Research, 32(2) (1994) 309–320. https://doi.org/10.1080/00207549408956935.
N. Karunanithi, W.J. Grenney, D. Whitley and K. Bovee, Neural networks for river flow prediction. Journal of Computing in Civil Engineering, 8(2) (1994) 201–220. https://doi.org/10.1061/(ASCE)0887-3801(1994)8:2(201).
R, Y. E. (1999). Artificial Neural Networks.
T. Velasco and M.R. Rowe, Back propagation artificial neural networks for the analysis of quality control charts. Computers & Industrial Engineering, 25(1–4) (1993) 397–400. https://doi.org/10.1016/0360-8352(93)90305-H.
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Azmat, S., Sabir, Q.U.A., Tariq, S. et al. Monitoring Air Quality using the Neural Network based Control Chart. MAPAN 38, 885–893 (2023). https://doi.org/10.1007/s12647-023-00663-9
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DOI: https://doi.org/10.1007/s12647-023-00663-9