A Machine Learning-based Algorithm for Automated Detection of
  Frequency-based Events in Recorded Time Series of Sensor Data
Bahareh Medghalchi Andreas Vogel
Automated event detection has emerged as one of the fundamental practices to
monitor the behavior of technical systems by means of sensor data. In the
automotive industry, these methods are in high demand for tracing events in
time series data. For assessing the active vehicle safety systems, a diverse
range of driving scenarios is conducted. These scenarios involve the recording
of the vehicle's behavior using external sensors, enabling the evaluation of
operational performance. In such setting, automated detection methods not only
accelerate but also standardize and objectify the evaluation by avoiding
subjective, human-based appraisals in the data inspection. This work proposes a
novel event detection method that allows to identify frequency-based events in
time series data. To this aim, the time series data is mapped to
representations in the time-frequency domain, known as scalograms. After
filtering scalograms to enhance relevant parts of the signal, an object
detection model is trained to detect the desired event objects in the
scalograms. For the analysis of unseen time series data, events can be detected
in their scalograms with the trained object detection model and are thereafter
mapped back to the time series data to mark the corresponding time interval.
The algorithm, evaluated on unseen datasets, achieves a precision rate of 0.97
in event detection, providing sharp time interval boundaries whose accurate
indication by human visual inspection is challenging. Incorporating this method
into the vehicle development process enhances the accuracy and reliability of
event detection, which holds major importance for rapid testing analysis.