AI-based vehicle health management system

Business needs

Modern vehicles are complex machines that are costly to fix and maintain. For instance, warranty claims, recalls, maintenance, and vehicle downtime costs reduce commercial fleet and customer satisfaction.

The main business needs were:

• to allow users to compare and evaluate different anomaly detection models on unsupervised data;
• to enable faster selection of anomaly detection model for every new client dataset with a few labeled anomalies;
• to develop a robust data augmentation framework to help recognize unknown types of anomalies;
• to find a development team with good communication skills ready to start fast;
• to strengthen the in-house development team with skilled ML developers and Data Scientists.

Provided solution

• Using Deep Learning technologies, the client’s solution can determine the vehicle’s health conditions and then flag any deviations from the expected behavior. This VHM solution can also predict when a vehicle’s health condition could lead to failure and help identify the cause of the problem.
• Unlike other similar VHM solutions that can only operate in the cloud, this one can operate onboard vehicles in real-time, assisting fleet owners to predict vehicle health issues.
• CHI Software Data Scientist is currently working on implementing and testing Deep Learning models for unsupervised anomaly detection and auto-ml solutions based on synthetically generated anomalous data along with research on related state-of-the-art models and novel ideas in this field.
• The tasks of the CHI Software’s Machine Learning Engineer include obtaining models from Data Scientists, running them on Big Data, and parallelizing calculations on the cloud.

Technologies:

Python, Microsoft Azure, Databricks, PySpark, Pytorch, Keras,Horovod, Matplotlib, Pandas, NumPy, Sklearn, Seaborn, Plotly, Scipy