Application of Deep Learning Networks to Design Quality Control Process in the Motor Oil Industry

Document Type : Original Article

Authors

1 Ph.D.Candidate in Industrial Engineering, Department of Industrial Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.

2 Associate Professor,Department of Industrial Engineering ,Qazvin Branch, Islamic Azad University, Qazvin, Iran.

3 Associate Professor, Department of Industrial Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.

Abstract

Introduction: In light of recent advancements in the modern world, multivariate-multistage quality control patterns are increasingly recognized as vital and indispensable in manufacturing industries. This study delves into the significance and necessity of multivariate-multistage quality control in manufacturing, specifically focusing on motor oil production. As a foundational factor, motor oil quality considerably influences engine performance, lifespan, customer satisfaction, and market positioning. This research employs deep learning algorithms for monitoring and fault detection in quality components. The primary rationale for opting for deep learning algorithms over conventional statistical methods is the non-normal distribution of data and the large sample sizes, which can lead to inaccurate estimations and unstable analyses. Conversely, the unique capabilities of deep learning algorithms in handling complex data and extracting meaningful features from extensive motor oil production data justify their selection.
Methods: To bolster accuracy and effective quality control, a combination of deep learning algorithms is utilized, including Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNN), and hybrid models such as LSTM-CNN, as well as Residual Networks (ResNet) with Dense Networks (DenseNet). The LSTM-CNN algorithm is applied to control numerical quality variables and identify temporal and sequential patterns in the data. Meanwhile, ResNet-DenseNet manages and analyzes visual data with non-uniform and intricate distributions. By integrating LSTM networks, CNNs, and residual connections, these algorithms excel at extracting meaningful features and capturing complex relationships within the data. This enhances performance and efficiency in quality control processes and facilitates intelligent decision-making. Such an approach is adept at uncovering latent patterns and intricate relationships between variables and quality attributes, enhancing quality control procedures and intelligent decision-making.
Results and discussion: The amalgamation of these algorithmic capabilities enhances the efficacy of quality control processes, outperforming single-algorithm approaches. Additionally, the Bee Colony Clonal Algorithm (BCC) is employed to fine-tune the parameters of the LSTM-CNN and ResNet-DenseNet algorithms. This hybrid approach harnesses the Artificial Bee Colony (ABC) and Genetic Algorithm (GA) strengths, markedly improving the performance of deep learning algorithms in quality control and reducing the time required to achieve desired outcomes. To illustrate the practical applicability of the proposed algorithms, a case study in the motor oil production industry is examined. The proposed LSTM-CNN hybrid algorithm in fault detection demonstrated superior results compared to standalone CNN and LSTM algorithms, achieving performance improvements of approximately 15% and 8%, respectively. Furthermore, the proposed ResNet-DenseNet hybrid algorithm exhibited higher accuracy in visual components, enhancing performance by approximately 10% and 15% compared to ResNet and DenseNet algorithms, respectively.
 Conclusions: From both academic and practical standpoints, this research scrutinizes deep learning algorithms' influence on enhancing motor oil quality and efficiency. Advanced data analysis methods, particularly hybrid deep learning algorithms, are employed to identify quality patterns in production data.

Keywords

Main Subjects


  1. Abbasi, R., Jamipour, M. & Ghasemlou, M. (2021).Analyzing the Factors Causing Customer Dissatisfaction with Food Ordering Applications. Business Management Perspective, 20(47) 111-136.
  2. Adalat, M.H., Azmi, R. & Bagherinejad, J. (2020).An Enhanced Lstm Method to Improve the Accuracy of the Business Process Prediction. The Journal of Industrial Management Perspective, 10(3), 71-97.
  3. Alla, S. & Adari, S.K. (2019). Beginning Anomaly Detection Using Python-Based Deep Learning. 2019: Springer.
  4. Alshamlan, H.M., Badr, G.H. & Alohali, Y.A. (2015).Genetic Bee Colony (Gbc) Algorithm: A New Gene Selection Method for Microarray Cancer Classification. Computational biology and chemistry, 56, 49-60.
  5. Asgharizadeh,E. Moghadam,M.R.S. Safari,H.&Neshan, (2019).The Effects of Customers’ Decision Making with Different Risk Prefrences on Warranty Providers: Agent Based Modeling. The Journal of Industrial Management Perspective, 9(1), 31-59.
  6. Atashgar, K. (2015). Monitoring Multivariate Environments Using Artificial Neural Network Approach: An Overview.
  7. Azar, A. & Mohammadlou, M.A. (2011). Designing a Service Quality Model in the Supply Chain: Explaining the Concept of Two-Way Service Quality. Business Management Perspective, 9(4),9-24.
  8. Baghbanpourasl, A., Lughofer, E., Meyer-Heye, P., Zörrer, H. &Eitzinger, C. (2019). Virtual Quality Control Using Bidirectional Lstm Networks and Gradient Boosting. IEEE 17th International Conference on Industrial Informatics (INDIN). 2019. IEEE.
  9. Bai, H., Tang, B., Cheng, T. & Liu, H. (2022).High Impedance Fault Detection Method in Distribution Network Based on Improved Emanuel Model and Densenet. Energy Reports, 8, 982-987.
  10. Bersimis,S.,Psarakis,S.& Panaretos, J. (2007). Multivariate Statistical Process Control Charts: An Overview. Quality and Reliability Engineering International, 23(5), 517-543.
  11. Chen,L.,Li, S., Bai, Q., Yang, J., Jiang, S. & Miao, Y. (2021).Review of Image Classification Algorithms Based on Convolutional Neural Networks, Remote Sensing, 13(22), 4712.
  12. Chen, S., Yu, J. & Wang, S. (2020).One-Dimensional Convolutional Auto-Encoder-Based Feature Learning for Fault Diagnosis of Multivariate Processes. Journal of Process Control, 87 54-67.
  13. Chen, Z., Yeo, C.K., Lee, B.S. & Lau, C.T. (2018). Autoencoder-Based Network Anomaly Detection. in 2018 Wireless Telecommunications Symposium (WTS).
  14. Dounias, G., Tselentis, G. & Moustakis, V. (2001).Machine Learning Based Feature Extraction for Quality Control in a Production Line. Integrated Computer-Aided Engineering, 8(4) 325-336.
  15. Esmaeili,M.,Olfat,L., Amiri, M. & Vanani, I.R. (2023).Classification and Allocation of Suppliers to Customers in Resilince Supply Chains Using Machine Learning. The Journal of Industrial Management Perspective, 13(3), 39-70.
  16. Gan, Y., Yang, J., & Lai, W. (2019) Video Object Forgery Detection Algorithm Base donVgg-11 Convolutional Neural Network. International Conference on Intelligent Computing, Automation and Systems (ICICAS).
  17. Guh,R.-S.&Shiue,Y.-R. (2008).AnEffective Application of Decision Tree Learning for on-Line Detection of Mean Shifts in Multivariate Control Charts. Computers & Industrial Engineering, 55(2), 475-493.
  18. Guo, B., Li, L. & Luo, Y. (2018). A New Method for Automatic Seismic Fault Detection Using Convolutional Neural Network. in 2018 SEG International Exposition and Annual Meeting. OnePetro.
  19. Harrou, F., Sun, Y., Hering, A.S. &Madakyaru, M. (2020) Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches: Theory and Practical Applications. 2020: Elsevier.
  20. Hsu,J.-Y.,Wang,Y.-F.,Lin, K.-C. & Chen, M.-Y. (2020). Wind Turbine Fault Diagnosis and Predictive Maintenance through Statistical Process Control and Machine Learning. Ieee Access, 8, 23427-23439.
  21. Huang,G., Liu, Z., Van Der Maaten, L. & Weinberger, K.Q. (2017). Densely Connected Convolutional Networks. in Proceedings of the IEEE conference on computer vision and pattern recognition.
  22. Jin, X., Fan, J. &Chow, T.W. (2018). Fault Detection for Rolling-Element Bearings Using Multivariate Statistical Process Control Methods. IEEE Transactions on Instrumentation Measurement, 68(9), 3128-3136.
  23. Jin, X., Fan, J. &Chow, T.W.S. (2019). Fault Detection for Rolling-Element Bearings Using Multivariate Statistical Process Control Methods. IEEE Transactions on Instrumentation and Measurement, 68(9), 3128-3136.
  24. Masuda,Y., Kaneko, H., & Funatsu, K. (2014). Multivariate Statistical Process Control Method Including Soft Sensors for Both Early and Accurate Fault Detection. Industrial & Engineering Chemistry Research, 53(20), 8553-8564.
  25. Moeinzadeh, B., Houshmand, A.A. & Niaki, S.T.A. (2001).On the Performance of a Multivariate Control Chart in Multistage Environment. International Journal of Engineering, 14(1), 49-64.
  26. Niaki,S.T.A. & Davoodi, M. (2009). Designing a Multivariate–Multistage Quality Control System Using Artificial Neural Networks. International Journal of Production Research, 47(1), 251-271.
  27. Odom, G.J., Newhart, K.B., Cath, T.Y. & Hering, A.S. (2018).Multistate Multivariate Statistical Process Control. Applied stochastic models in business industry, 34(6), 880-892.
  28. Pustokhin, D., Pustokhina, I., Dinh, P., Phan, S., Nhu, N., Joshi, G.P. & K, Shankar. (2020).An Effective Deep Residual Network Based Class Attention Layer with Bidirectional Lstm for Diagnosis and Classification of Covid-19. Journal of Applied Statistics, 50, 1-18.
  29. Qureshi, K.M., Lup, A.N.K., Khan, S., Abnisa, F. & Daud, W.M.A.W. (2018). A Technical Review on Semi-Continuous and Continuous Pyrolysis Process of Biomass to Bio-Oil. Journal of Analytical Applied Pyrolysis, 131 52-75.
  30. Rezai Dolatabadi, H., Zaineli, Z.&Shekarchizadeh, Z. (2011).Inves tigating the Impact of Competitive Intelligence in Creating a Competitive Advantage. Business Management Perspective, 10(6),9-25.
  31. Santos-Fernández, E. (2012) Multivariate Statistical Quality Control Using R. Vol. 14. 2012: Springer Science & Business Media.
  32. Shin, T. (2020).Towards Data Science. Retrieved from Meduim: https://towardsdatascience.com/understanding-the-confusionmatrix-and-how-to-implement-it-in-python-319202e0fe4d.
  33. Silva, A.F., Sarraguça, M.C., Fonteyne, M., Vercruysse, J., De Leersnyder, F., Vanhoorne, V., Bostijn, N., Verstraeten, M., Vervaet, C.,Remon,J.P.,DeBeer,T. & Lopes, J.A. (2017).Multivariate Statistical Process Control of a Continuous Pharmaceutical Twin-Screw Granulation and Fluid Bed Drying Process. International Journal of Pharmaceutics, 528(1), 242-252.
  34. Song, H., Xu, Q., Yang, H. & Fang, J. (2017). Interpreting out-of-Control Signals Using Instance-Based Bayesian Classifier in Multivariate Statistical Process Control. Communications in Statistics - Simulation and Computation. 46(1), 53-77.
  35. Song, H., Xu, Q., Yang, H. & Fang, J. (2017).Interpreting out-of-Control Signals Using Instance-Based Bayesian Classifier in Multivariate Statistical Process Control. Communications in Statistics-Simulation Computational biology and chemistry, 46(1), 53-77.
  36. Wang, L., Zhang, Z., Xu, J. &Liu, R. (2018).Wind Turbine Blade Breakage Monitoring with Deep Autoencoders. IEEE Transactions on Smart Grid, 9(4), 2824-2833.
  37. Wang,T., Chen, Y., Qiao, M. &Snoussi, H. (2018).A Fast and Robust Convolutional Neural Network-Based Defect Detection Model in Product Quality Control. The International Journal of Advanced Manufacturing Technology, 94 3465-3471.
  38. Yu, H., Miao, X. &Wang, H. (2022). Bearing Fault Reconstruction Diagnosis Method Based on Resnet-152 with Multi-Scale Stacked Receptive Field. Sensors (Basel). 22(5),1705.
  39. Yu,J., Zheng, X.& Wang, S.J.Q. (2019). Stacked Denoising Auto encoder‐Based Feature Learning for out‐of‐Control Source Recognition in Multivariate Manufacturing Process. Quality and Reliability Engineering International, 35(1), 204-223.
  40. Zheng, X. & Yu, J. (2019). Multivariate Process Monitoring and Fault Identification Using Convolutional Neural Networks. in Proceeding of the 24th International Conference on Industrial Engineering and Engineering Management Singapore: Springer Singapore.