MCSA and SVM for Gear Wear Monitoring in Lifting Cranes

Abstract

In recent years, Motor Current Signature Analysis (MCSA) were explored to diagnose common mechanical faults of the overall kinematic chain of induction machines such as bearing faults, shaft misalignment, and gears faults. In fact, current signals are known to be most representative of the machine torque which can easily distinguish between the healthy and defected operational mode. In addition the electric signals are easily acquired; related sensors are not expensive and can be mounted in a non-intrusive manner. The aim of this work is to look at failure predictions in three-phase line-operated induction machines through statistical and spectral analysis of electric current signals, validated by the use of classical classification techniques, namely the two-class Support Vector Machine (SVM) and the One Class Support Vector Machine (OCSVM). The SVM based classification methods are able to exploit several indicators at once in order to identify more precisely faulty operational modes when they appear. These methods are applied using data from a lifting crane with an Accelerated Life Time (ALT) test. The lifetime test began on September 2008 and finished on July 2012 after gear degradation has been revealed by oil analysis and inspection.