Feasibility Study Of Microphone Phased Array Based Machinery Health Monitoring

Abstract

To increase reliability and safety, industrial plant equipment such as compressors,
electric motors, gear trains, and so forth are regularly monitored for damage. A traditional
approach for monitoring is for a trained technician to make vibration measurements of the
equipment. Inspection of the vibration measurements and their comparison to known
healthy/damaged data sets allow for assessing the health status of the machines. This process is
repeated at regular intervals. However, it is time-consuming and labor-intensive. It would be
greatly convenient both in terms of time and cost to develop a remote acoustic based system to
detect the health status of industrial equipment. A microphone phased array machine health
monitoring system is proposed to remotely identify and classify machine faults. Acoustic spectral
signature analysis has existed for many years, and it is similar to vibration analysis using
accelerometers with the advantage that nothing must be mounted on the machine. In addition,
acoustic imaging systems using microphone phased arrays have also been in existence for many
years. The current state-of-the-art, however, requires an expert human operator to interpret the
data from these devices, and therefore it is not suitable for automated, online monitoring. The
implementation of a microphone phased array approach to monitor equipment faults in a typical
industrial environment presents some unique challenges. A typical industrial plant is a highly
acoustic reverberant environment that will result in significant reflections and background noise
levels. To be effective, the array will need to be able to monitor multiple machines through the
plant, simultaneously. To investigate the potential of the proposed approach, a numerical model of
the microphone phased array in a large highly reverberant room was developed. The model was
then used to investigate several array designs, study the effect of reflections and reverberation,
determine the capability of the system to monitor multiple machines and so forth. These
numerical studies revealed that the critical concern is that the array signal to noise ratio must be
larger than the noise difference between the loudest and quietest machines being monitored. It
was also found that the reverberation of typical industrial plants is not important if a sufficient
number of microphones is used in the array, e.g. for a plant with an average absorption coefficient
of 10% a minimum of 100 microphones is required in the array.