Data-driven system prognostics and diagnostics for SMT component placement machines

Abstract

Prognostic-based maintenance analyses past events and predicts the future state of a machine based on the understanding of the degradation function of the machine’s components. Diagnostics-based maintenance tests equipment according to a fixed routine for a machine’s proper functioning and reliability. Current Surface-mount Technology (SMT) machines are not equipped with self-prognostic and diagnostic functions. In this paper, a system prognostic and diagnostic method is proposed, implemented in software, for estimating a machine’s health condition and faulty components of a SMT component placement machine outfitted with machine logs that consist of take-up count, miss count and time information. At each execution period the method processes features extracted from the machine logs to obtain a set of parity parameters, which are further used to analyse the machine. The prognostic algorithm computes the health status indicator of the component placement machine. The computed final status indicator is compared to a threshold value to check the system’s health condition. The diagnostic algorithm predicts and identifies the faulty pick-up nozzles and faulty input trays. The proposed algorithms minimise the effects of faulty components on production lines and assist to produce optimal maintenance decisions and reliability functions for equipment.