# 1D CNN Based Detection and Localisation of Defective Droppers in Railway Catenary
###### Abstract  
Defective droppers pose a significant threat to the performance of the contact between the train pantograph and railway catenary. In this paper, the impact of damaged droppers on the performance of pantograph-catenary interaction behaviour is analysed, and the impact of varying degrees of damage to each dropper is labelled. To improve the classification accuracy when both the damage degree and position are considered, a model integrating multiple 1D CNNs is proposed. Approaches including randomly searching the optimal hyper-parameters and K-fold cross-validation are used to prevent overfitting and to ensure model performance regardless of the training data subset selected. Compared with a conventional 1D CNN, the classification performance of the integrated method is demonstrated using the metrics accuracy, F1-score, precision and recall. It is concluded that, through the use of the integrated 1D CNN, damaged droppers can be detected and localised based on the pantograph-catenary contact force. Hence, intelligent catenary inspection can be enhanced. catenary condition monitoring; deep learning; fault classification; 1D CNN; pantograph-catenary interaction +
Footnote †: journal: Applied sciences  
In recent years, finite element (FE)-based catenary models have dominated research focusing on pantograph-catenary dynamics [9; 26]. The method approximates the catenary by discretising the system into smaller physics-based elements, which provides excellent accuracy and consistency [21; 24; 26; 27]. When contact force is the main interest, the pantograph is commonly modelled by a lumped-mass model for simulations [9; 26]. However, to ensure the veracity of the pantograph-catenary models, the simulation framework is required to be validated by the simulation standard EN50318 [28]. Either from experimental data or simulation results, dropper defects have been identified as being critical to the pantograph-catenary interaction performance [3; 5; 16; 20]. The contact force signal is effective for finding fault information through static-based or signal-processing techniques. For example, in [7], contact force signals with defective droppers were obtained from an FE catenary model. The results were then subjected to time and frequency analysis techniques. In the time domain, the variance of different faults was obviously found when investigating peaks at some specific locations (local peaks). This study identified the feature of defective droppers, but there are few studies focused on damage classification. Additionally, the data used in [7] do not contain any noise, so they do not reflect real-world influences. Hence, further research on the detection and localisation of defective droppers is worthy of investigation. A convolutional neural network (CNN) as a standard deep learning technique has been effectively used in many research domains (fault classification, linear regression, speech recognition, etc.) [29; 30; 31; 32]. Generally, a CNN consists of CNN layers and multilayer perceptron (MLP) layers [33]. The CNN layer is used to process the data and extract the feature, and the MLP layers can perform classification according to the identified features. Considering the application of CNN for the inspection of the catenary system, some studies focusing on the image (2-dimensional signal) have been carried out [10; 11; 12; 13; 14; 15; 16]. Among them, Ref. [16] tackled the classification problem of defective droppers, where photos of droppers were captured by roof-mounted cameras on vehicles. According to the CNN method, the features of healthy and defective droppers have been identified and classified. However, as mentioned in this paper, it is difficult to classify damage that is not visually obvious. An alternative to the image-based approach is to make use of contact force. Contact force has the advantage that it can reflect the pantograph-catenary contact quality straightforwardly, no matter the shape of the dropper.