Ergonomics for enhancing detection of machine abnormalities.
BACKGROUND
Detecting abnormal machine conditions is of great importance in an autonomous maintenance environment. Ergonomic aspects can be invaluable when detection of machine abnormalities using human senses is examined.


OBJECTIVE
This research outlines the ergonomic issues involved in detecting machine abnormalities and suggests how ergonomics would improve such detections.


METHODS
Cognitive Task Analysis was performed in a plant in Sri Lanka where Total Productive Maintenance is being implemented to identify sensory types that would be used to detect machine abnormalities and relevant Ergonomic characteristics.


CONCLUSIONS
As the outcome of this research, a methodology comprising of an Ergonomic Gap Analysis Matrix for machine abnormality detection is presented.
Luleå University of Technology, Sweden.
Prasanna
P
Illankoon
Work Science Academy, Sweden/Sri Lanka.
John
J
Abeysekera
Mechanical Engineering Department, Government College of Engineering & Technology, Jammu, India.
Sarbjeet
S
Singh
D001307:Auditory Perception; D006804:Ergonomics / Q000379:methods*; D006801:Humans; D008283:Maintenance; D008328:Man-Machine Systems; D055696:Olfactory Perception; D010465:Perception; D013188:Sri Lanka; D013783:Textile Industry; D013697:Thermosensing; D055698:Touch Perception; D014796:Visual Perception
Autonomous maintenance; cognitive task analysis; sensory types