Fault diagnosis using audio and vibration signals in a circulating pump

Abstract

This paper presents the use of audio and vibration signals in fault diagnosis of a circulating pump. The novelty of this paper is the use of audio signals acquired by microphones. The objective of this paper is to determine if audio signals are capable to distinguish between normal and different abnormal conditions in a circulating pump. In order to compare results, vibration signals are also acquired and analysed. Wavelet package is used to obtain the energies in different frequency bands from the audio and vibration signals. Neural networks are used to evaluate the discrimination ability of the extracted features between normal and fault conditions. The results show that information from sound signals can distinguish between normal and different faulty conditions with a success rate of 83.33%, 98% and 91.33% for each microphone respectively. These success rates are similar and even higher that those obtained from accelerometers (68%, 90.67% and 71.33% for each accelerometer respectively). Success rates also show that the position of microphones and accelerometers affects on the final results.