We have 5000 vehicles of different classes (trucks, small cars, large cars) with 100 sensors in each car measuring fuel consumption, distance traveled, average speed etc for some time period $t$ that can vary between each data post. At random time-deltas, all 100 sensor values are sent to a data center.
I want to build an anomaly detection algorithm to calculate the confidence we have that each post received has fields within reasonable values.
After doing some research, I have concluded that a good solution to model this type of data would be to derive the multivariate Gaussian distribution from a vector of all 100 sensors as well as a feature for the class of the car (small car, truck, etc).
The reason I believe this to be good is because some of the sensor data will be highly correlated which is exactly what I want to capture.
Has anyone solved a similar problem like this? Is anyone aware of better alternatives?