Detection of rail squats from axle box acceleration: Optimization of a machine learning algorithm
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Typ
Examensarbete för masterexamen
Program
Sound and vibration (MPSOV), MSc
Publicerad
2020
Författare
Carlvik, Frida
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
The detection of rail surface defects, such as squats, is vital to maintaining the structural
health of railway tracks. Squats are a type of rail surface defect which results
in large contact forces between the wheel and rail. Squats are self-sustaining; the
increase in impact forces results in further rail deterioration. Squats are currently
detected through various methods such as ultrasonic measurements, eddy currents,
and human inspection. A promising alternative method of squat detection is the use
of axle box acceleration (ABA) measurements. Even light squats are visible in the
acceleration signal. The application of ABA data to automatically detect squats is
an area of current research.
Using data and knowledge from a previous study of acoustic squat detection on the
German railway, the thesis aimed to optimize a squat detection algorithm based on
machine learning. Measured and simulated axle-box acceleration data were supplied
from the previous research. The thesis investigated different methods of preprocessing
acceleration data to improve the machine learning algorithm. Two algorithms
were used- logistic regression and neural networks. The different methods of preprocessing
data were spectrogram images, scalogram images, time-averaged wavelet
power, and scale-averaged wavelet power.
To test the results, the data was divided into a training and a testing set. Furthermore,
leave-one-out validation was conducted for the measured squats. Finally, the
trained algorithm was tested on two 250 m test sequences of railway track. Issues
were found with distinguishing insulated rail joints from squats. Furthermore, a
higher success rate often led to a higher rate of false alarms. In these cases, the
algorithm failed to generalize to new data. The final algorithm and method of
preprocessing with scalogram images found 100% classification of medium to large
squats and 87% classification of small squats. The algorithm found a total of 4 false
alarms on the two test sequences, one of which was an insulated rail joint.
Although the final optimization did not find increased success in identifying small
squats in comparison to the previous study, the use of ABA to identify squats was
consolidated. Areas of further research are training the algorithm on tracks with
varying track dynamics as well as testing other algorithms such as convolutional
neural networks.
Beskrivning
Ämne/nyckelord
rail surface defects, squats, axle box acceleration, machine learning, wavelet transform, scalogram, logistic regression, neural network