Advanced Predictive Control Systems for Elevators Utilizing Intelligent Wavelet Techniques for Fault Signal Analysis

Omar W. Maaroof; Muhamad Azhar Abdilatef Alobaidy; Ruaa H. Ali Al-Mallah; Rashad A. Alsaigh

doi:10.18196/jrc.v6i3.25698

Authors

Omar W. Maaroof University of Mosul
Muhamad Azhar Abdilatef Alobaidy University of Mosul https://orcid.org/0000-0003-0997-3530
Ruaa H. Ali Al-Mallah Northern Technical University
Rashad A. Alsaigh University of Mosul

DOI:

https://doi.org/10.18196/jrc.v6i3.25698

Keywords:

CDWA, BMLP, Energy Distribution, Fault Elevator

Abstract

Elevators play a critical role in modern infrastructure, requiring robust predictive control systems to ensure safe and efficient operation. This research work investigates an advanced predictive control system for elevators, integrating intelligent techniques with Wavelet analysis for fault signal analysis. The study aims to enhance the maintenance strategies to minimize downtime and improve reliability. In recent works, the Discrete wavelet transforms with different types like, Daubechies and Symlet, were used for the purpose of decomposition. In this work, the Coiflets Discrete Wavelet Analysis (CDWA), which is classified as one of the well utilized methods for analog signals is applied for the recorded data that obtained from a simulated elevator model for the purpose of enabling the identification of subtle anomalies indicative of potential faults in elevator systems. Concurrently, AI-based intelligent techniques, represented in the use of Backpropagation Multi-Layer Perceptron (BMLP) neural network, are utilized to analyze the decomposed signals, predict impending faults, and recommend proactive maintenance actions. By combining Wavelet analysis with AI-based fault signal analysis, the proposed predictive control system offers a comprehensive approach to elevator maintenance, leading to increased operational efficiency, reduced maintenance costs, and most importantly enhanced safety. The mean square error (MSE) is used to measure the performance of the system, while the convolution matrix is used to assess the accuracy. The findings of this research contribute to the development of smarter and more reliable elevator systems, aligning with the growing demand for intelligent infrastructure in modern urban environments.

References

Y. Singh, K. Bagri, A. Jayakumar, and G. Rizzoni, “Fault Diagnostics for Oscillatory Failure Case in Aircraft Elevator Servos,” IFAC-PapersOnLine, vol. 56, no. 2, pp. 408-15, 2023.

M. Sato, R. Takase, and S. Suzuki, “Pilot assessment of fault-tolerant PID flight controller for elevator efficiency reduction via hardware-in-the-loop simulations,” IFAC-PapersOnLine, vol. 53, no. 2, pp. 14918-14923, 2020.

X. Wang, S. Wang, Z. Yang, and C. Zhang, “Active fault-tolerant control strategy of large civil aircraft under elevator failures,” Chinese Journal of Aeronautics, vol. 28, no. 6, pp. 1658-1666, 2015.

A. M. Taneva, A. P. Petrov, and M. G. Petrov, “Fault Detection Algorithm for Pneumatic Measuring and Sorting Station,” IFAC-PapersOnLine, vol. 58, no. 9, pp. 13-18, 2024.

L. Wang, R. QI, and B. Jiang, “Adaptive fault-tolerant control for non-minimum phase hypersonic vehicles based on adaptive dynamic programming,” Chinese Journal of Aeronautics, vol. 37, no. 3, pp. 290-311, 2024.

M. Atif, S. Azmat, F. Khan, F. R. Albogamy, and A. Khan, “AI-driven thermography-based fault diagnosis in single-phase induction motor,” Results in Engineering, vol. 24, p. 103493, 2024.

L. Yao, “Research on the Application of Intelligent Sensors Based on the Internet of Things in Fault Diagnosis of Mechanical and Electrical Equipment,” Measurement: Sensors, vol. 38, p. 101811, 2025.

Y. Guo, X. Li, J. Zhang, and Z. Cheng, “SDCGAN: A CycleGAN-Based Single-Domain Generalization Method for Mechanical Fault Diagnosis,” Reliability Engineering & System Safety, vol. 258, p. 110854, 2025.

K. Zhai et al., “Mechanical properties of the liner for strengthening steel pipe under the action of normal fault,” Journal of Pipeline Science and Engineering, vol. 5, no. 1, p. 100225, 2025.

Z. Xu, K. Zhao, J. Wang, and M. Bashir, “Physics-informed probabilistic deep network with interpretable mechanism for trustworthy mechanical fault diagnosis,” Advanced Engineering Informatics, vol. 62, p. 102806, 2024.

A. S. Willsky, “A survey of design methods for failure detection in dynamic systems,” Automatica, vol. 2, pp. 431-434, 1976.

F. Jian, Z. Huaguang, Z. Tieyan, and D. Liu, "Detection Algorithm and Application Based on Work Status Evaluator," 2006 6th World Congress on Intelligent Control and Automation, pp. 5479-5482, 2006.

S. Postalcioglu, K. Erkan, and E. D. Bolat, “Discrete wavelet analysis based fault detection,” WSEAS Transactions on Systems, vol. 5, no. 10, pp. 2391-2397, 2006.

Z. Wang, Y. Liu, and P. J. Griffin, "Neural net and expert system diagnose transformer faults," IEEE Computer Applications in Power, vol. 13, no. 1, pp. 50-55, 2000.

X. M. Jiang and Y. X. Wu, “Research on Extension-Fuzzy-Based Evaluation Used for Performance of Heat Exchanger,” Advanced Materials Research, vol. 566, pp. 244-247, 2012.

R. Li and Y. L. Ai, “Study on fault diagnosis of elevator based on neural network information fusion technology,” Computer Engineering and Applications, vol. 46, no. 14, pp. 231-234, 2010.

K. Hadad, M. Pourahmadi, and H. Majidi-Maraghi, “Fault diagnosis and classification based on wavelet transform and neural network,” Progress in Nuclear Energy, vol. 53, no. 1, pp. 41-47, 2011.

Y. Le, Y. Shifeng, L. Huanhuan, L. Zhicheng, and H. Xiaobing, "Research on Elevator Group Optimal Dispatch Based on Ant Colony Algorithm," 2020 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA), pp. 99-102, 2020.

L. Chen, S. Lan, and S. Jiang, “Elevators Fault Diagnosis Based on Artificial Intelligence,” Journal of Physics: Conference Series, vol. 1345, no. 4, p. 42024, 2019.

C. Chen, X. Ren, and G. Cheng, “Research on Distributed Fault Diagnosis Model of Elevator Based on PCA-LSTM,” Algorithms, vol. 17, no. 6, p. 250, 2024.

W. Pan, Y. Xiang, W. Gong, and H. Shen, “Risk Evaluation of Elevators Based on Fuzzy Theory and Machine Learning Algorithms,” Mathematics, vol. 12, no. 1, p.113, 2024.

C. Beldjaatit, T. Sebbagh, and H. Guentri, “Discrete Wavelet Transform and Energy Distribution for Effective Bearing Fault Detection and Analysis,” Przegląd Elektrotechniczny, vol. 99, no. 7, 2023.

R. Chen, X. Huang, L. Yang, X. Xu, X. Zhang, and Y. Zhang, “Intelligent fault diagnosis method of planetary gearboxes based on convolution neural network and discrete wavelet transform,” Computers in Industry, vol. 06, pp. 48-59, 2019.

K. V. S. M. Babu, “Efficient fault detection and categorization in electrical distribution systems using hessian locally linear embedding on measurement data,” Measurement: Energy, vol. 5, p. 100035, 2025.

L. Yao, “Research on the Application of Intelligent Sensors Based on the Internet of Things in Fault Diagnosis of Mechanical and Electrical Equipment,” Measurement: Sensors, vol. 38, p. 101811, 2025.

M. Al-Flaiyeh, “Influence of Using ACO Algorithm in STATCOM Performance,” NTU Journal of Engineering and Technology, vol. 3, no. 1, pp. 26-34, 2024.

K. Sawayama, T. Ishibashi, F. Jiang, and T. Tsuji, “Hydro-mechanical-electrical simulations of synthetic faults in two orthogonal directions with shear-induced anisotropy,” Journal of Rock Mechanics and Geotechnical Engineering, vol. 16, no. 11, pp. 4428-39, 2024.

H. Mbembati and H. Bakiri, “A novel approach for oil-based transformer fault identification in electrical secondary distribution networks,” Heliyon, vol. 15, no. 5, p. e26336, 2024.

C. Zhang and C. Zhang, “Intelligent Fault Diagnosis System of Electrical Equipment Based on Neural Network Algorithm,” Procedia Computer Science, vol. 247, pp. 485-492, 2024.

A. Elevators, "Traction elevator," Aarisi Elevators, 2024.

J. Vladić, R. Đ. ić, M. Kljajin, and M. Karakašić, “Modelling and Simulations of Elevator Dynamic Behaviour,” Technical Gazette, vol. 18, no. 3, pp. 423-434, 2011.

M. Fabietti et al., “SANTIA: a Matlab-based open-source toolbox for artifact detection and removal from extracellular neuronal signals,” Brain Informatics, vol. 8, pp. 1-19, 2021.

T. Zoppi, S. Gazzini, and A. Ceccarelli, “Anomaly-based error and intrusion detection in tabular data: No DNN outperforms tree-based classifiers,” Future Generation Computer Systems, vol. 160, pp. 951-65, 2024.

F. P. de Isidro-Gómez, J. L. Vilas, J. M. Carazo, and C. O. Sorzano, “Automatic detection of alignment errors in cryo-electron tomography,” Journal of structural biology, vol. 217, no. 1, p. 108153, 2025.

K. Awedat, G. Comert, M. Ayad, and A. Mrebit, “Advanced fault detection in photovoltaic panels using enhanced U-Net architectures,” Machine Learning with Applications, vol. 20, p. 100636, 2025.

H. Chae, J. E. Lee, and K. -Y. Oh, "Fault Diagnosis of Elevator Doors Using Control State Information," IEEE Access, vol. 10, pp. 7207-7222, 2022.

B. A. Hamad and A. Al-Naib, “Design and Implementation of a Dual-Axis Solar Tracking System STS,” NTU-JET, vol. 1, no. 1, pp. 35-43, 2021.

S. Z. Saeed, M. A. A. Alobaidy, and Z. M. Yosif, “Errors Detection Based on SDWT and BNN Applied for Position, Velocity and Acceleration Signals of a Wheeled Mobile Robot,” Journal of Robotics and Control (JRC), vol. 5, no. 5, pp. 1291-1298, 2024.

A. A. Jaber and R. Bicker, “Fault diagnosis of industrial robot gears based on discrete wavelet transform and artificial neural network,” International Journal of Prognostics and Health Management, vol. 7, no. 2, 2016.

M. A. Alobaidy, J. M. Abdul-Jabbar, and S. Z. Al-khayyt, “Slantlet transform used for faults diagnosis in robot arm,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 25, no. 1, pp. 281-90, 2022.

W. Liu, R. Zhu, W. Zhou, and J. Wang, “Research on feature extraction method for different levels of cracks and pitting in spur gear based on dynamic characteristic templates,” Measurement, vol. 228, p. 114335, 2024.

M. A. Sattari, G. H. Roshani, R. Hanus, and E. Nazemi, “Applicability of time-domain feature extraction methods and artificial intelligence in two-phase flow meters based on gamma-ray absorption technique,” Measurement, vol. 168, p. 108474, 2021.

M. A. A. Alobaidy, D. J. Abdul-Jabbar, and S. Z. Al-khayyt, “Faults diagnosis in robot systems: a review,” Al-Rafidain Engineering Journal (AREJ), vol. 25, no. 2, pp. 164-175, 2020.

M. S. Karthika, H. Rajaguru, and A. R. Nair, “Wavelet feature extraction and bio-inspired feature selection for the prognosis of lung cancer− A statistical framework analysis,” Measurement, vol. 238, p. 115330, 2024.

M. Uyar, S. Yildirim, and M. T. Gencoglu, “An effective wavelet-based feature extraction method for classification of power quality disturbance signals,” Electric power systems Research, vol. 78, no. 10, 1747-1755, 2008.

H. K. Nigam and H. M. Srivastava, “Filtering of audio signals using discrete wavelet transforms,” Mathematics, vol. 11, no. 19, p. 4117, 2023.

Y. Song, J. Cao, and Y. Hu, “In-process feature extraction of milling chatter based on second-order synchroextracting transform and fast kutrogram,” Mechanical Systems and Signal Processing, vol. 15, p. 111018, 2024.

V. Srivastava and R. K. Purwar, “A Five‐Level Wavelet Decomposition and Dimensional Reduction Approach for Feature Extraction and Classification of MR and CT Scan Images,” Applied Computational Intelligence and Soft Computing, vol. 2017, no. 1, pp. 1-9, 2017.

T. Cooklev and A. Nishihara, "Biorthogonal coiflets," IEEE Transactions on Signal Processing, vol. 47, no. 9, pp. 2582-2588, 1999.

T. Guo, T. Zhang, E. Lim, M. López-Benítez, F. Ma, and L. Yu, "A Review of Wavelet Analysis and Its Applications: Challenges and Opportunities," IEEE Access, vol. 10, pp. 58869-58903, 2022.

T. N. Babu, P. S. Ali, D. R. Prabha, V. N. Mohammed, R. S. Wahab, and S. Vijayalakshmi, “Fault diagnosis in bevel gearbox using coiflet wavelet and fault classification based on ANN including DNN,” Arabian Journal for Science and Engineering, vol. 47, no. 12, 15823-15849, 2022.

T. Guo, T. Zhang, E. Lim, M. López-Benítez, F. Ma, and L. Yu, "A Review of Wavelet Analysis and Its Applications: Challenges and Opportunities," IEEE Access, vol. 10, pp. 58869-58903, 2022.

L. M. Reissell, “Wavelet multiresolution representation of curves and surfaces,” Graphical Models and Image Processing, vol. 58, no. 3, 198-217, 1996.

M. Moradi, “Wavelet transform approach for denoising and decomposition of satellite-derived ocean color time-series: Selection of optimal mother wavelet,” Advances in Space Research, vol. 69, no. 7, pp. 2724-2744, 2022.

X. Chen, "Real Wavelet Transform-Based Phase Information Extraction Method: Theory and Demonstrations," IEEE Transactions on Industrial Electronics, vol. 56, no. 3, pp. 891-899, 2009.

M. K. Wali, M. Murugappan, R. B. Ahmad, and B. S. Zheng, "Development of Discrete Wavelet Transform (DWT) toolbox for signal processing applications," 2012 International Conference on Biomedical Engineering (ICoBE), pp. 211-216, 2012.

J. Yao, Y. Lin, X. Lin, and L. Yao, “Fatigue loads compressed editing by discrete wavelet transform and optimal wavelet parameters selection algorithm,” Engineering Fracture Mechanics, vol. 303, p. 110128, 2024.

M. A. A. Alobaidy and S. Z. Saeed, “A Comparative Study of Multi-Layer Perceptron and Jordan Recurrent Neural Networks for Signals Classification in a Robotic System,” Journal Européen des Systèmes Automatisés, vol. 56, no. 4, pp. 547-551, 2023.

H. R. Ansari, M. J. Zarei, S. Sabbaghi, and P. A. Keshavarz, “A new comprehensive model for relative viscosity of various nanofluids using feed-forward back-propagation MLP neural networks,” International Communications in Heat and Mass Transfer, vol. 91, p. 158-164, 2018.

E. Ashraf, A. E. Kabeel, Y. Elmashad, S. A. Ward, and W. M. Shaban, “Predicting solar distiller productivity using an AI Approach: Modified genetic algorithm with Multi-Layer Perceptron,” Solar Energy, vol. 263, p. 111964, 2023.

J. Zhang, X. Liu, Y. Wang, S. Zhang, T. Wang, and Z. Ji, “A novel explainable propagation-based fault diagnosis approach for Clean-In-Place by establishing Boolean network model,” Journal of Process Control, vol. 148, p. 103405, 2025.

P. Mohapatra, A. Ray, S. Jena, B. M. Padhiari, A. Kuanar, S. Nayak, and S. Mohanty, “Artificial neural network based prediction and optimization of centelloside content in Centella asiatica: A comparison between multilayer perceptron (MLP) and radial basis function (RBF) algorithms for soil and climatic parameter,” South African Journal of Botany, vol. 160, pp. 571-585, 2023.

N. Y. Abdullah, “Evaluation of Physiotherapy Exercise by Motion Capturing Based on Artificial Intelligence: A Review,” AL-Rafidain Engineering Journal (AREJ), vol. 28, no. 2, 2023.

T. Sathish et al., “Characteristics estimation of natural fibre reinforced plastic composites using deep multi-layer perceptron (MLP) technique,” Chemosphere, vol. 337, p. 139346, 2023.

Z. M. Yosif, B. S. Mahmood, and S. Z. Al-khayyt, “Assessment and review of the reactive mobile robot navigation,” Al-Rafidain Engineering Journal (AREJ), vol. 26, no. 2, pp. 340-355, 2021.

A. A. Jaber and R. Bicker, “Industrial robot backlash fault diagnosis based on discrete wavelet transform and artificial neural network,” American Journal of Mechanical Engineering, vol. 4, no. 1, pp. 21-31, 2016.

M. A. A. Alobaidy, Z. M. Yosif, and A. M. Alkababchi, “Age-Dependent Palm Print Recognition Using Convolutional Neural Network,” Revue d'Intelligence Artificielle, vol. 37, no. 3, p. 795, 2023.

A. H. Saber, A. A. Abdulla, G. Alwan, and R. A. Alsaigh, “Low-cost embedded intelligent system design for smart mobile robot controller,” Przeglad Elektrotechniczny, vol. 99, no. 3, 2023.