Parametric model-based anomaly detection for locomotive subsystems

Locomotives are complex electromechanical systems. Continuously monitoring the health state of locomotives is critical in modern cost-effective maintenance strategy. A typical locomotive is equipped with the capability to generate fault messages or incidents based on logical rules in the control system. In the mean time, sensor readings and operational state variables are also collected. The goal is to detect faults early to provide lead-time for maintenance actions and trip planning based on the collected fault log and parametric data. In this paper, we present a model-based anomaly detection strategy. In this method, the inputs-outputs relationship of a locomotive subsystem is modeled using a neural network model based on normal operational data. The residuals between measurements and model outputs are calculated. A reasoning module based on these multiple residuals is used to generate an overall health indicator of the subsystem at each instance of times, which is further used to determine whether the subsystem is abnormal. Statistical testing, Gaussian mixture model and support vector machine are used to generate this healthy index and their performances are compared. We demonstrate the effectiveness of the anomaly detection strategy using real-world operational data from locomotives.