Article: Improving fault diagnosis in elevator systems with GAN-based synthetic data Journal: International Journal of Sensor Networks (IJSNET) 2025 Vol.47 No.1 pp.26 - 35 Abstract: Elevator maintenance and fault diagnosis are critical in ensuring reliable and safe operation. Elevator systems are complex electromechanical systems prone to various faults, such as sensor failures, motor malfunctions, and mechanical wear and tear. Detecting these faults promptly and accurately ensures elevators' safe and reliable operation. However, there is a lack of labelled data that may be used to train machine learning models, making it difficult to diagnose problems with elevators. This paper presents a novel approach for elevator fault diagnosis based on optimised generative adversarial networks (GANs). The proposed method employs a GAN model that generates synthetic data to augment the limited amount of labelled data and then trains a classifier on the augmented dataset. To improve the performance of the GAN, the authors introduce an optimisation algorithm that combines gradient ascent and descent, resulting in better-quality synthetic data. The efficiency of the system is evaluated using real-world elevator sensor data and compared its performance to traditional fault diagnosis methods. The results show that the proposed system can accurately diagnose faults with high accuracy and can potentially reduce maintenance costs and downtime. The proposed system provides a promising solution for elevator fault diagnosis, especially when labelled data is limited. Inderscience Publishers - linking academia, business and industry through research

Title: Improving fault diagnosis in elevator systems with GAN-based synthetic data

Authors: Xiaomei Lv; Zhibin Lu; Zhihao Huang; Zhanhao Wei

Addresses: School of Mechatronic Engineering, Guangdong Engineering Polytechnic, Guangzhou 510520, China ' Schindler (China) Elevator Co. Ltd., Shanghai 201815, China ' Schindler (China) Elevator Co. Ltd., Shanghai 201815, China ' Schindler (China) Elevator Co. Ltd., Shanghai 201815, China

Abstract: Elevator maintenance and fault diagnosis are critical in ensuring reliable and safe operation. Elevator systems are complex electromechanical systems prone to various faults, such as sensor failures, motor malfunctions, and mechanical wear and tear. Detecting these faults promptly and accurately ensures elevators' safe and reliable operation. However, there is a lack of labelled data that may be used to train machine learning models, making it difficult to diagnose problems with elevators. This paper presents a novel approach for elevator fault diagnosis based on optimised generative adversarial networks (GANs). The proposed method employs a GAN model that generates synthetic data to augment the limited amount of labelled data and then trains a classifier on the augmented dataset. To improve the performance of the GAN, the authors introduce an optimisation algorithm that combines gradient ascent and descent, resulting in better-quality synthetic data. The efficiency of the system is evaluated using real-world elevator sensor data and compared its performance to traditional fault diagnosis methods. The results show that the proposed system can accurately diagnose faults with high accuracy and can potentially reduce maintenance costs and downtime. The proposed system provides a promising solution for elevator fault diagnosis, especially when labelled data is limited.

Keywords: fault diagnosis; optimised generative adversarial networks; GANs; elevators; augmented dataset; and maintenance costs.

DOI: 10.1504/IJSNET.2025.143899

International Journal of Sensor Networks, 2025 Vol.47 No.1, pp.26 - 35

Received: 27 May 2024
Accepted: 12 Jun 2024
Published online: 13 Jan 2025 *

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Title: Improving fault diagnosis in elevator systems with GAN-based synthetic data

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