The Permanent Magnet Synchronous Motor (PMSM) is the power source maintaining the stable and efficient operation of various pieces of equipment; hence, its reliability is crucial to the safety of public equipment. Convolutional Neural Network (CNN) models face challenges in extracting features from PMSM current data. A new Discrete Wavelet Transform Convolutional Neural Networks (DW-CNN) feature with fusion weight updating Long Short-Term Memory (LSTM) anomaly detection is proposed in this paper. This approach combines Discrete Wavelet Transform (DWT) with high and low-frequency separation processing and LSTM. The anomaly detection method adopts DWT and CNN by separating high and low-frequency processing. Moreover, this method combines the hybrid attention mechanism to extract the multi-current signal features and detects anomalies based on weight updating the LSTM network. Experiments on the motor bearing real fault dataset and the PMSM stator fault dataset prove the method’s strong capability in fusing current features and detecting anomalies.

To address the challenges of weak early-stage loosening fault signals and strong environmental noise interference in escalator drive mainframe anchor bolts, which hinder effective fault feature extraction, this paper proposes an improved Residual Convolutional Denoising Autoencoder (RCDAE) for signal denoising in high-intensity noise environments. The model combines DMS (Dynamically Multimodal Synergistic) loss function, the gated residual mechanism, and CNN–Transformer. The experimental results demonstrate that the proposed model achieves an average accuracy of 93.88% under noise intensities ranging from 10 dB to −10 dB, representing a 2.65% improvement over the baseline model without the improved RCDAE (91.23%). At the same time, in order to verify the generalization performance of the model, the CWRU bearing data set is used to conduct experiments under the same conditions. The experimental results show that the accuracy of the proposed model is 1.30% higher than that of the baseline model without improved RCDAE, validating the method’s significant advantages in noise suppression and feature representation. This study provides an effective solution for loosening fault diagnosis of escalator drive mainframe anchor bolts.

In the rapid development of social economy and science and technology, Chinese public transportation and large-scale shopping malls, stations, airports in the construction promotion, start to use escalator in a large number, as a transportation vehicle of the new era construction development, although it has solved the problems of the human flow of the traditional urban construction and development, but also a lot of safety accidents have emerged. According to the practice investigation and research, there are many types of escalator safety accidents at home and abroad, which are reflected in the safety protection device, synchronous safety, brake safety and other aspects, which directly endanger people's life, property and health. Therefore, on the basis of understanding the theoretical research status of escalator technology, and according to the overall design of automatic safety performance detection of escalator, we propose an escalator braking performance test method, and then mainly study the safety and reliability of escalator equipment, in order to provide an effective basis for the construction and development of a new era.