Analysis of Stray Flux Signals for Sparking Fault Deteccion in DC machines

DC electrical motors and generators continue to play an important role in many industrial applications despite their declining use. A poor maintenance may yield significant economic losses for the industries where they operate. One of the common symptoms of failure in these machines is the presence of sparks in the interface between brushes and commutator. This phenomenon is usually linked with a diversity of anomalies and defects which may even lead to the forced motor outage. Therefore, its detection in its incipient stage and the quantification of its severity becomes crucial to adopt proper maintenance actions in due time to prevent the corresponding fault. This paper explores an innovative method to detect and assess the sparking phenomenon in DC machines. The method is based on the analysis of steady-state and transient stray flux signals and the further identification of low and high frequency harmonics linked with the fault. The results prove the high potential of the approach for becoming a reliable tool for the assessment of the commutation quality in DC machines.