摘要: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.