# Research on Distributed Fault Diagnosis Model of Elevator Based on PCA-LSTM
## 1 Introduction  
With the rapid urbanization and proliferation of high-rise buildings, the number of elevators in use has significantly increased. Consequently, elevators frequently face issues such as entrapment, overshooting the top floor, or experiencing slippage. These operational challenges not only cause considerable inconvenience but also pose potential hazards. Thus, there is a critical need for the development and research of advanced elevator failure prediction systems. By predicting failures in advance, it is possible to prevent accidents and minimize operational downtime, thereby enhancing the safety and reliability of elevator operations. The data from elevator operations, which are spatially distributed, are typically centralized in data centers. This centralization of data for fault analysis often overloads the central processors, resulting in slower detection times. Therefore, developing a distributed elevator fault prediction system capable of diagnosing faults across multiple elevators holds significant importance. A substantial body of research has been conducted both domestically and internationally on the prediction of elevator faults, predominantly utilizing mathematical or physical models of elevator equipment. [PERSON] et al. [1] employed Convolutional Neural Networks (CNNs) to automate fault diagnosis using data gathered from elevator vibration sensors, significantly reducing the time and effort required for fault information collection. Traditional expert systems, when faced with elevator faults, often exhibit low diagnostic accuracy due to challenges in data acquisition and limited learning capabilities. To address this, [PERSON] et al. [2] developed an elevator door system fault diagnosis model based on the XGBoost algorithm, which effectively identifies the presence and type of faults in the elevator door system from extensive diagnostic data. The fault diagnosis of critical mechanical components is essential for ensuring passenger safety and comfort. [PERSON] et al. [3]tackled the issues of noise and indirect signal acquisition by analyzing micro-impact signals resulting from mechanical damage, using real-time data from elevator operations to enable intelligent fault diagnosis. [PERSON] et al. [4] devised a deep convolutional forest algorithm to model elevator door fault diagnosis based on comprehensive elevator operation and maintenance data, employing CNNs for feature extraction and the deep forest algorithm as a classifier to pinpoint faults. Addressing the sample imbalance between elevator factory samples and fault samples, [PERSON] et al. [5] proposed an improved fault diagnosis method for imbalanced samples using the Aquila optimizer-enhanced Extreme Gradient Boosting Trees (XGBoost). [PERSON] et al. [6] considered the time and frequency domain characteristics of elevator car vibration faults and developed a simple mathematical model combining a radial basis function (RBF) neural network to predict elevator car vibration fault diagnosis outcomes. [PERSON] et al. [7] utilized fault tree analysis to pinpoint and rectify faults. [PERSON] et al. [8] applied a combination of the polar band algorithm (PBO) and CNN to detect faults in elevator bearings based on vibration signals during operation. [PERSON] et al. [9] introduced a multichannel, one-dimensional convolutional neural network for diagnosing abnormal elevator vibration faults, effectively leveraging elevator vibration data. Considering the sensitivity of traction wheel-bearing fault diagnosis to variable speed conditions and environmental noise, [PERSON] et al. [10] introduced an angular-domain resampling method for estimating traction wheel rotational speed and a traction wheel-bearing fault diagnosis method optimized using the Sparrow Search Algorithm (SSA). [PERSON] et al. [11] proposed an elevator fault identification and judgment method based on kernel entropy component analysis (KECA), performing dimensionality reduction and clustering analysis on various types of elevator fault data. Finally, [PERSON] et al.