Danas se u industriji sve veći akcenat stavlja na razvijanje ekonomičnijih načina
održavanja mašina i blagovremene detekcije otkaza. Ovo nije toliko iznenađujuće ako se
uzme u obzir procena da je čak jedna trećina troškova održavanja prosečnog industrijskog
postrojenja posledica suvišnog ili neefikasnog održavanja...
In recent years in modern industry great emphasis has been placed on the
development of cost-eective approaches to machine maintenance and timely fault detection.
This is not so surprising if one takes into account the estimate that one third of
the maintenance costs of an average industrial plant are due to excessive or inecient
upkeep. Modern predictive maintenance techniques were developed with goal to prolong
life expectancy of machines and detect worn components in timely manner, thus significantly
reducing maintenance costs. These methods are based on acquisition of appropriate
signals (vibration measurements, thermographic images, etc.), processing and analysis of
those signals and, finally, estimating the state of the machine and assessing the necessity
of repairs.
Rotating machines are especially common in industry and are frequently mentioned
in modern predictive maintenance literature, with vibration signals being the most informative
resource for their diagnostics. The usag...e of vibration signals for feature extraction
and machine condition diagnostics is well founded in the literature and tested on various
types of actuators, including industrial mills. However, in the last decade an increased
emphasis has been placed on usage of acoustic signals with purpose of fault detection. It
has been shown that they can be as informative as vibration signals and can enable the
detection of certain changes even faster, due to their high sensitivity to certain changes in
the environment. Also, acoustic sensors are cheaper, contactless and can record signals in
an immediate vicinity of an actuator while it is in operation.
There has been much success in using acoustic sensors for predictive maintenance;
however, almost all those results are obtained in strictly controlled laboratory conditions.
A major issue in using these signals in industrial surroundings is their high susceptibility
to the surrounding noise which is always present in real-life conditions. Filtering
the noise using traditional techniques is usually not possible without significant loss of
useful information, so all the benefits of using sound signals for fault detection are often
overshadowed by this one flaw.
The main objective of the research conducted within this doctoral dissertation is the
development of new methodology which, using acoustic signals in contaminated environment,
searches for informative features in time and frequency domain. The special
attention will be given to preprocessing of the signal for the purpose of detecting the
existence of contaminating components. The research will be focused on problems in
thermal power plants (state detection of impellers within mills), but it is our belief that
the results can be generalized and expanded to broad family of rotating actuators like
feedwater pumps, compressors, ventilators, etc...
