# Fault Diagnosis of Railway Point Machines Using the Locally Connected Autoencoder
## 2 Related Works  
Fault diagnosis of point machines has been widely addressed in urban rail transit. Traditionally, condition monitoring systems are applied to rail transit applications to ensure driving safety. In these systems, a simple threshold is designed to detect abnormal behavior, resulting in poor performance of the detection accuracy. To improve the condition monitoring system, much effort has been targeted towards capturing the fault patterns. [PERSON] [19] investigated various point machines and explored core measurements for fault diagnosis. In this work, the electrical signals are considered to be the foremost feature to study to infer the health condition of the point machine. [PERSON] et al. [13] presented an early detection method in the measurement of the current signals. Using the similarity measure of edit distance, the one-class support vector machine is employed to seize the changes. Using the electric current as a parameter, [PERSON] et al. [7] proposed a wavelet transform-based feature extraction scheme by which an accurate health prediction was obtained. Aided by in-field current data, the authors of [14] proposed a classification method to detect the replacement conditions. [PERSON] et al. [11] focused on the aging effect of the point machine. This task is regarded as a binary-class classification problem. Similarly, the support vector data description was applied. Different from the current data-based approach, the authors of [8] put forward a data mining scheme that employs audio data of the point machine. The scheme is capable of extracting mel-frequency cepstrum coefficients to reduced feature dimensions. The fault detection accuracy is reported to exceed 94.1%. The point machines' gap was investigated in [10]. The proposed edge detection algorithm has the advantage of overcoming the local intensity variation in gap images, which can effectively show the health condition of the point machines. All the research discussed above are classification approaches, where the training process requires prelabeled samples. These researches mentioned above can be categorized as the classification approach, where the training process is necessary with prelabeled samples. However, the lack of available labeled data necessary for the training of the models used for anomaly detection techniques is usually a major obstacle to apply such models. [PERSON] et al. [12] pioneered a diagnosis method using dynamic time warping to manage the variation of the current signals without training steps. Inspired by the authors of [12], we attempt to solve the fault detection problem in a non-training way. In this paper, the LOF algorithm is employed for fault detection. Autoencoders have attracted attention in the field of fault diagnosis as replacements for any handcrafted features and expert knowledge incorporated in the traditional fault analysis approaches. [PERSON] et al. [20] presented a review on handcrafted based feature extraction algorithms and autoencoders. In this paper, a fault analysis was performed based on five different fault datasets and it was demonstrated that the autoencoder methods performed best. A two-layer encoder was proposed in [21], which was regularized by the contractive regularization and validated on artificially constructed datasets. Using bearing and gearbox fault datasets, the authors of [4] showed that the autoencoder yields better classification performances than the support vector machine based methods. The authors of [22] used a parse autoencoder to obtain useful representations of different sensor signals. The obtained representations were fed to train a deep belief network for fault classification. To address for unobserved working conditions, the authors of [23] developed a cross-domain stacked denoising autoencoder. The maximum mean discrepancy was calculated to achieve effective marginal distribution adaptation. [PERSON] et al. [24] proposed a multilevel denoising autoencoder approach.