System and method for unsupervised anomaly prediction
Some embodiments are associated with a system and method for deep learning unsupervised anomaly prediction in Internet of Things (IoT) sensor networks or manufacturing execution systems. The system and method use an unsupervised predictive GAN model with multi-layer perceptrons (MLP) as generator and discriminator.
1. A computer-implemented vehicle performance prediction system, comprising:
a vehicle onboard computer coupled to a plurality of interior and exterior sensors mounted on a vehicle and internal mechanical components for obtaining information related to external surroundings, interior environment, and components conditions;
a data service coupled to the vehicle onboard computer and configured to receive time series data from the plurality of interior and exterior sensors;
a model training and deployment service coupled to the vehicle onboard computer and configured to receives and stores the pre-processed data from the data service in a distributed database and generate a risk score or a probability for next failure occurring within a time period, wherein the time period ranges from one day to thirty days; and
a user interface service coupled to the vehicle onboard computer and configured to present the risk score or the probability for the next failure occurring within the time period in the vehicle on a dashboard along with supporting evidence;
wherein the data service comprises a data pre-processing service configured to characterize the sensors' time series data by a state, wherein the states include at least one of the following: a failed state which characterizes situations that are problematic or inoperational; a warning state which characterizes situations that lead up to a failure; and a normal state which characterizes all other situations; and
wherein the data pre-processing service is configured to remove the time series data characterized as the failed state as part of a data preparation step.
2. The system of claim 1 , wherein the data pre-processing service uses a script to characterize the sensors' time series data.
3. The system of claim 1 , wherein the model training and deployment service comprises a remaining useful life (RUL) calculator configured to calculate how much time is left before a next failure.
4. The system of claim 3 , wherein the RUL calculator employs an unsupervised predictive GAN model with multi-layer perceptrons (MLP) as generator and discriminator (MLP-MLP GAN).
5. The system of claim 4 , wherein the unsupervised predictive GAN model is trained with the pre-processed data in the normal or the warning state to generate the risk score or the probability of next failure occurring within the time period.