Author(s)

SaiNan Wang¹, Xian Zhou^1*, YunTao Yang²

Affiliation(s)

¹Hunan Electrical College of Technology, Xiangtan 411101, Hunan, China.

²School of Physics & Electronics, Hunan University, Changsha 410082, Hunan, China.

Corresponding Author

Xian Zhou

ABSTRACT

To address the fault diagnosis of elevator unbalanced loads, this study proposes a fault diagnosis method based on a parameter-optimized support vector machine (SVM). By establishing a dynamic model of the elevator system, fault features are extracted, and an improved particle swarm optimization algorithm is applied to optimize the key parameters of the SVM, thereby constructing an efficient fault diagnosis model. Experimental results indicate that the proposed method significantly outperforms traditional diagnostic approaches in terms of fault classification accuracy, and can effectively identify the unbalanced load state of elevators. The research outcome offers a new technical solution for elevator fault diagnosis and holds significant engineering application value for ensuring the safe operation of elevators.

KEYWORDS

Elevator fault diagnosis; Unbalanced load; Particle swarm optimization algorithm; Fault feature extraction

CITE THIS PAPER

SaiNan Wang,  Xian Zhou, YunTao Yang. Fault diagnosis of elevator unbalanced load based on parameter-optimized SVM. World Journal of Engineering Research. 2025, 3(4): 72-86. DOI: https://doi.org/10.61784/wjer3054.

REFERENCES

[1] Akbar S, Vaimann T, Asad B, et al. State-of-the-art techniques for fault diagnosis in electrical machines: advancements and future directions. Energies, 2023, 16(17): 6345.

[2] Cort é s P, Larraneta J, Onieva L. Genetic algorithm for controllers in elevator groups: analysis and simulation during lunchpeak traffic. Applied Soft Computing, 2004, 4(2): 159-174.

[3] Xiao Z, Cheng Z, Li Y. A review of fault diagnosis methods based on machine learning patterns. 2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing), 2021: 1-4.

[4] Fernandez J R, Cortes P. A survey of elevator group control systems for vertical transportation: A look at recent literature. IEEE Control Systems Magazine, 2015, 35(4): 38-55.

[5] Hadian M, Saryazdi S M E, Mohammadzadeh A, et al. Application of artificial intelligence in modeling, control, and fault diagnosis. Applications of Artificial Intelligence in Process Systems Engineering. Elsevier, 2021: 255-323.

[6] Lin N. Research on Wear Detection of Elevator Traction Wheel Based on Machine Vision Technology. Micro Special Motor, 2022, 50(3): 26-30.

[7] Li Z H, Chai Z, Zhao C H. Elevator Anomaly Based on Multi-channel Convolution Vibration Fault Diagnosis. Control Engineering of China, 2023, 30(3): 427-433.

[8] Zhang Y, Shem R, Li Z Z. Team workload: review and conceptualization. International Journal of Industrial Ergonomics, 2023, 95: 103452.

[9] Wang C Q. Research on Elevator Safety Evaluation and Management in M City. Beijing: School of Engineering Science, Chinese Academy of Sciences, 2020.

[10] Zhu X L, Li K, Zhang C S, et al. Elevator Boot Fault Diagnosis Method Based on Gabor Wavelet Transform and Multi-kernel Support Vector Machine. Computer Science and Technology, 2020, 47(12): 258-261.

[11] Li Z H, Chai Z, Zhao C H. Elevator Fault Diagnosis Based on Multi-channel Convolutional Neural Network. Control Engineering of China, 2023, 30(3): 427-433.

[12] Lee S Y, Cho I P, Hong C P. Contactless Elevator Button Control System Based on Weighted k-NN Algorithm for AI Edge Computing Environment. Journal of Web Engineering, 2022, 21(2): 443-457.

[13] Hua Y C. Research on Fault Analysis and Inspection of Elevator Brakes. Mechanical and Electrical Engineering Technology, 2024, 53(6): 257-260.

[14] Koehler J, Ottiger D. An AI-based Approach to Destination Control in Elevators. AI Magazine, 2002, 23(3): 59-59.

[15] Al-Kodmany K. Elevator Technology Improvements: A Snapshot. Encyclopedia, 2023, 3(2): 530-548.

[16] Niknam T, Amiri B. An Efficient Hybrid Approach Based on PSO, ACO and k-means for Cluster Analysis. Applied Soft Computing, 2010, 10(1): 183-197.

[17] Mishra S, Bhende C N, Panigrahi B K. Detection and Classification of Power Quality Disturbances Using S-Transform and Probabilistic Neural Network. IEEE Transactions on Power Delivery, 2007, 23(1): 280-287.

[18] Ferranti L, Wan Y M, Keviczky T. Fault-tolerant Reference Generation for Model Predictive Control with Active Diagnosis of Elevator Jamming Faults. International Journal of Robust and Nonlinear Control, 2019, 29(16): 5412-5428.