Abnormal Status Detection of Catenary Based on TSNE Dimensionality
Reduction Method and IGWO-LSSVM Model
Authors: Lingzhi Yi , Guo Yu , Yahui Wang , Tengfei Dong , Huang Yu , Haixiang She 
Published: May 2023

Background:
Catenary is a crucial component of an electrified railroad's traction power supply system.
There is a considerable incidence of abnormal status and failures due to prolonged outside exposure.
Driving safety will be directly impacted if an abnormal status or failure occurs. Currently, catenary detection
vehicles are the most often utilized technique for gathering data and identifying faults based on manual experience.
However, this technology cannot meet the demands of prompt detection and correction of faults in
railways engineering due to its extremely low work efficiency.

Objective:
Based on the above, an abnormal status detection method of catenary based on the improved gray
wolf (IGWO) algorithm optimized the least squares support vector machine (LSSVM) with the t-distributed
stochastic neighbor embedding (TSNE) is proposed in this paper. In order to improve the accuracy of catenary
abnormal status detection and shorten the detection time.

Methods:
Firstly, the TSNE dimensionality reduction technology is used to reduce the original catenary data
to three-dimensional space. Then, in order to address the issue that the parameters of the LSSVM detection
model are hard to determine, the improved GWO algorithm is used to optimize the penalty factor and kernel
parameter in the LSSVM and establish the TSNE-IGWO-LSSVM catenary abnormal status detection model.
Finally, contrasting experimental results of different detection models. The T-distributed Stochastic Domain
Embedding (TSNE) is an improved nonlinear dimensionality reduction method based on the Stochastic
Neighbor Embedding (SNE). TSNE no longer adopts the distance invariance in linear dimensionality reduction
methods such as ISOMAP. TSNE is much better than the linear dimensionality reduction method in the
reduction degree of the original dimension. The GWO algorithm, which is frequently used in engineering research,
has the advantages of a simple model, great generalization capability, and good optimization performance.
The premature convergence is one of the remaining flaws. By applying a good point set to initialize
the gray wolf population and the nonlinear control parameters, the gray wolf algorithm is improved in this research.
The IGWO algorithm effectively makes up for the problem of balancing the local exploitation and
global search capabilities of GWO. Additionally, this IGWO algorithm performs the Cauchy variation operation
on the current generation optimal solution to improve population diversity, enlarge the search space, and
increase the likelihood of the algorithm escaping the local optimal solution in order to prevent the algorithm
from failing the local optimum. The Least Squares Support Vector Machine (LSSVM) is an improved version
of the Support Vector Machine (SVM), which replaces the original inequality constraint with a linear least
squares criterion for the loss function. The kernel parameters of the RBF function and the penalty factor, these
two parameters directly determine the detection effect of LSSVM. In this paper, the IGWO is utilized to adjust
and determine the LSSVM parameters in order to enhance the detection capacity of the LSSVM model.

Results::
In this paper, in order to minimize the experiment's bias, the training data and the test data are allocated
in a ratio of 4:1, the training data are set to 400 groups, and the test data are set to 100 groups. After
training the five models, the test data is used to validate and compare the detection capacity of the models. After
each of the five detection models was tested ten times, the TSNE-IGWO-LSSVM model is compared with
the IGWO-LSSVM model, the TSNE-FA-LSSVM model, the GWO-LSSVM model, and the GWO-ELM
model, the results show that the TSNE-IGWO-LSSVM model has the highest average detection accuracy of
97.1% and the shortest running time of 26.9s. For the root mean squared error (RMSE) and the root mean
squared error (RMSE), the TSNE-IGWO-LSSVM model is 0.17320 and 2.51% respectively, which is the best
among the five models, indicating that it not only has higher detection accuracy but also better convergence of
detection accuracy than the other models.

Conclusions:
With the thousands of miles of catenary and the complexity of the data, it is crucial to shorten
the running time in order to improve the efficiency and ease the burden of the processors. The experiments
demonstrate that the TSNE-IGWO-LSSVM detection model can detect the abnormal status of catenary more
accurately and quickly, providing a new method for the abnormal status detection of catenary, which has certain
application value and engineering significance in the era of fully electrified railways.