Hierarchical Spatial-Temporal State Machine for Vehicle Instrument Cluster Manufacturing

Abstract

The vehicle instrument cluster is one of the most advanced and complicated electronic embedded control systems used in modern vehicles providing a driver with an interface to control and determine the status of the vehicle. In this paper, we develop a novel hybrid approach called Hierarchical Spatial-Temporal State Machine (HSTSM). The approach addresses a problem of spatial-temporal inference in complex dynamic systems. It is based on a memory-prediction framework and Deep Neural Networks (DNN) which is used for fault detection and isolation in automatic inspection and manufacturing of vehicle instrument cluster. The technique has been compared with existing methods namely rule-based, template-based, Bayesian, restricted Boltzmann machine and hierarchical temporal memory methods. Results show that the proposed approach can successfully diagnose and locate multiple classes of faults under real-time working conditions.