# Research on Distributed Fault Diagnosis Model of Elevator Based on PCA-LSTM
## 3 Distributed Elevator Fault Diagnosis System  
### Overview of Diagnostic Methods  
Elevator fault diagnosis represents an organic integration of sensor technology, embedded systems, network transmission, computer control, intelligent diagnostics, databases, and other cutting-edge technologies. The development of Distributed Elevator Fault Diagnosis Systems (DEFDS) offers us a technical pathway to address these complex challenges.  
In the realm of distributed fault diagnosis for elevator systems, the AllReduce algorithm plays a pivotal role in synchronizing fault data collected from a network of elevator units. This method involves equipping each elevator with sensors that continuously monitor a variety of operational parameters, such as velocity, door status, and motor-induced vibrations. Initially, these data points are processed locally at each elevator, where basic fault detection algorithms identify potential issues based on predefined thresholds and patterns.  
Following the identification of potential faults, summaries of these data points, including key statistical measures and diagnostic indicators, are shared across the network using the AllReduce algorithm. This algorithm efficiently aggregates data from all elevator units, eliminating the need for a centralized database and thereby providing each unit with a comprehensive view of fault data across the entire system. The aggregation process involves summing and averaging similar data points from all elevators, enabling the accurate and synchronous integration of fault information across the network.  
Figure 3: Structure flowchart.  
The fundamental concept of distributed elevator fault diagnosis is that each elevator functions as a work node, with each work node training a sub-model based on local data and communicating with other work nodes according to specific protocols (primarily concerning sub-model parameters or updates). This ensures that the training results from each work node can be effectively integrated to form a cohesive global machine learning model. The distributed deep learning framework for elevators is illustrated in Figure 4.  
After the model parameters from each elevator are received, they need to be aggregated to facilitate multi-machine collaborative learning. Aggregation is a concept unique to distributed machine learning due to the variety of distributed learning methods available, each with its own logic and approach to aggregation. The processes and contents of aggregation differ among various distributed machine learning algorithms.  
Most aggregation methods rely on the synchronization of all worker nodes and typically operate in a centralized mode similar to global summation, requiring a central node (e.g., a parameter server) to coordinate the aggregation process. However, this centralized approach has its drawbacks. Firstly, it can create a bottleneck at the central node, especially when network transmission costs are high or network connectivity is poor. Secondly, the centralized model demands greater system stability, as it depends on the central node to aggregate and distribute models reliably; a single error at the central node can compromise the entire task.  
To address these issues, decentralized, distributed machine learning methods have been explored. Figure 4 illustrates the comparison between centralized and decentralized network topologies. The decentralized approach offers greater autonomy to each working node, facilitating easier maintenance and scaling of the model. Specifically, each node can selectively communicate with only a select few other nodes based on specific needs.