Anomaly Detection for Escalators Based on Feature Optimization and Improved GMM Algorithm

Escalators, as vital components of modern urban transportation and commercial buildings, necessitate stable operation. However, due to prolonged usage and environmental influences, key components of escalators are prone to failures, leading to operational anomalies. Traditional periodic inspections and manual maintenance methods suffer from low efficiency, high cost, and poor real-time performance. To address these issues, this paper proposes an unsupervised escalator anomaly detection method based on feature optimization and an improved Gaussian Mixture Model (GMM) algorithm.The method initially employs the Pareto principle for feature selection, enhancing computational efficiency and generalization capabilities. Subsequently, to overcome the limitations of traditional GMMs in determining the number of clusters and their application to anomaly detection, a bidirectional Fuzzy C-Means-Expectation Maximization (FCM-EM) algorithm is proposed. This algorithm determines the optimal number of clusters and optimizes the model’s fit to the data, thereby enabling accurate identification of anomalous states in escalators.Experimental results demonstrate that the proposed method achieves high diagnostic accuracy across four escalator fault modes. Furthermore, comparative experiments show that the proposed method outperforms the comparison methods, providing reliable technical support for intelligent maintenance and safe operation of escalators.