# Fault Diagnosis of Railway Point Machines Using the Locally Connected Autoencoder
## 1 Introduction  
With the tremendous deployment of different kinds of sensors and actuators, the Internet of Things (IoT) emerges as an advanced method to connect devices and collect the status data [1]. Aided by the use of a large amount of operation data, the data-driven fault diagnosis is considered as a modern technique in Industry 4.0 and has become a research hotspot in recent years [2; 3; 4; 5]. In the area of urban rail transit, the significant increase of the line mileage and the passenger throughput leads to a high capacity utilization of the existing infrastructure [6]. This kind of situation may cause more equipment failures and service disruptions, resulting in a great impact on traffic safety, property, and customer satisfaction. Among these infrastructure failures in urban rail transit systems, the vast majority of them are triggered by railway point machines [7]. The point machine is a critical component that is used to safely switch the train direction. To meet the demand of fault-tolerant operation ofrailway transportation and reduce the maintenance costs of metro operators, advanced fault diagnoses schemes of the point machines are of utmost need. The literature includes a wide range of methods for condition monitoring-based failure diagnosis of point machines, including sound analysis [8], gap measurement [9; 10], and electric current analysis [11; 12; 13]. Among these methods, electric current analysis is considered a straightforward and effective approach for failure diagnosis of the point machine, as the point machine is directly actuated by an electric motor [14]. The support vector machine is a representative method to solve the task [15]. The support vector classification method usually has two key steps: feature extraction and fault recognition. The feature extraction process, however, is typically manually designed--as the classifier is incapable of capturing information from raw signals--and typically much of the main effort involves incorporating expert knowledge into the system, which can be influenced by personal attitudes and not fully objective, like artificial intelligence. The problem of how to automatically extract features needs to be well addressed. In addition, one core characteristic of our data is that each current signal is a time series of correlated count data. The temporal characteristic should be taken into consideration to improve the accuracy of fault diagnosis of point machines [16]. Bearing these challenges in mind, in this paper, we propose a novel fault diagnosis scheme for railway point machines. First, a locally connected autoencoder is employed to obtain high-order feature representations. Thereby, instead of the labor-intensive hand-engineering feature extraction approach, effective features can automatically be captured from the raw electrical data. Different from other autoencoder-based methods, in the proposed autoencoder, \"locally\" means that the current signal is equally segmented into some subsequences. Then, these subsequences are fed sequentially to the input layer of the autoencoder. To enhance the temporal characteristic, inspired by the word2 vec method [17], adjacent subsequences are blended to obtain the features of the current subsequence. Second, after the local features are extracted using the locally connected autoencoder, a weighting strategy is proposed to weight average the features into their final representations. The reason for using this strategy is that the electric signals collected from the point machines may contain some noninformative information, which causes the unexpected redundancy and the high disturbance of the local features. By employing a weighting function, useful and robust features will be enhanced, whereas the less informative features will be reduced. Finally, different from the existing methods, which input the features into the softmax classifier for classification, we use the Local Outlier Factor (LOF) algorithm for outlier detection.