Acoustic Condition Monitoring: Signal Analysis for Large Machinery Halls

Abstract

Maintenance and condition monitoring of machinery is an essential part of almost every industrial branch. An auspicious approach for machine surveillance is to use an acoustic sound based condition monitoring (ASCM) system. With such a system complex equipping and wiring single machines with sensors gets obsolete. ASCM has seen intensive research for monitoring single machines. In this work we consider aspects for the application of ASCM for monitoring entire machine halls. In contrast to the application of ASCM for single machines, the sound scene offers larger variations for normal operational conditions, making the detection of faulty states more challenging. We present measurements of a machine hall and address these aspects. We then investigate a statistical signal modelling technique to describe the sound scene of the hall and analyse the feasibility for the detection of different fault scenarios. We show that model based detection methods indicate a good detectability of fault states.