# Research on Distributed Fault Diagnosis Model of Elevator Based on PCA-LSTM
## 1 Introduction
[12] developed a neural network-based elevator fault diagnosis system optimized using a genetic algorithm, aiming to overcome the challenges associated with obtaining mathematical models of elevator systems and diagnosing operational dynamics. [PERSON] et al. [13] employed mathematical computing mechanisms to analyze the characteristics of a research object and successfully developed an elevator fault prediction model utilizing the optimized PSO-BP (Particle Swarm Optimization-Back Propagation) algorithm. Additionally, [PERSON] et al. [14] provided a comprehensive overview of a variety of AI algorithms for elevator fault diagnosis at both theoretical and application levels. These algorithms include Back Propagation (BP) neural networks, Radial Basis Function (RBF) networks, K-means clustering, and Support Vector Machines (SVMs). They also conducted an extensive literature review on the applications of these algorithms in elevator fault diagnosis, drawing insights from their extensive body of work. Most existing research on elevator fault diagnosis has primarily utilized machine learning techniques to identify faults in individual elevators. However, a significant portion of the literature has also explored the use of distributed methods, which are applied not only for fault detection but also for the precise localization of faults across elevator systems. This broader approach addresses both system-wide issues and specific component malfunctions, thereby offering a comprehensive perspective that is crucial for advancing the field. Numerous studies have focused on fault diagnosis using distributed algorithms. For instance, [PERSON] et al. successfully implemented a distributed deep learning model that utilizes geolocation by processing features as data through the hidden parameters of deep learning [15]. [PERSON] et al. designed a distributed sensor-fault detection and diagnosis system utilizing a machine learning approach in which the fault detection is executed directly within the sensor to enable immediate output generation after data collection [16]. Conversely, [PERSON] and other researchers have employed various machine learning methods to classify and identify faults in cascaded H-bridge multilevel inverters (CHMLI) across different environments, demonstrating the widespread applicability of these algorithms for fault detection and localization in the distributed genset CHMLI [17]. For the collected elevator operation signals, affected due to Gaussian noise, issues such as variable and high dimensionality, as well as numerous types of faults and uneven sample distribution, arise. In this study, we utilized PCA to reduce the dimensionality of data, building on the advantages highlighted in previous literature. Subsequently, based on the characteristics of the elevator samples, we designed an LSTM model to determine the causes of failures and predict failure parameters. This model analyzes failure data to proactively provide failure warnings and significantly enhance elevator safety. A large body of related literature confirms that distributed fault diagnosis techniques can effectively manage the problem of overloaded central processors in complex systems. Common and frequently occurring faults are addressed independently by installing local intelligence on each elevator. Simultaneously, neighboring elevators in a distributed system can leverage their resources for model integration. To facilitate the exchange of model parameters, it is necessary to install management intelligence on multiple elevators in order to enable fault diagnosis across multiple units. The main contributions of this paper are as follows: (1) the development of a fault diagnosis system based on the PCA-LSTM model for a single elevator and (2) the implementation of distributed communication among multiple elevators within the same network, in which the elevators act as nodes to exchange models with each other in order to optimize the fault diagnosis model.