Bandsaw diagnostics by neurocomputing-two are better than one!

Abstract

In industrial sawmills, bandsaws must work at a high production rate. Two major factors which limit cutting performance are cracking and instability of the saw blades. This paper describes the results from the development of a diagnostic system which monitors blade vibration and blade tension sensor data to estimate crack length using neurocomputing techniques, to help predict blade failure. It was found that a multi-layered feedforward artificial neural network with two hidden layers produces the most reliable results. The results indicate that this system should enable the detection of cracking in blades while in a running but unloaded (between cuts) state. This may help allow longer run times to be planned with confidence increasing production uptime and minimising maintenance.