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Ergonomics for enhancing detection of machine abnormalities.

Prasanna Illankoon¹, John Abeysekera², Sarbjeet Singh³

¹Luleå University of Technology, Sweden.

Work (Reading, Mass.)

|October 1, 2016

Summary

This study highlights how ergonomics can improve machine abnormality detection in autonomous maintenance. An Ergonomic Gap Analysis Matrix methodology was developed to enhance human sensory detection of machine faults.

Keywords:

Autonomous maintenance cognitive task analysis sensory types

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Area of Science:

Industrial Engineering
Human Factors Engineering

Background:

Effective detection of abnormal machine conditions is crucial for autonomous maintenance.
Human sensory perception plays a key role in identifying machine abnormalities.

Purpose of the Study:

To outline ergonomic issues in machine abnormality detection.
To demonstrate how ergonomics can enhance the detection of machine abnormalities.

Main Methods:

Cognitive Task Analysis was employed in a Sri Lankan plant implementing Total Productive Maintenance.
Sensory types and ergonomic characteristics for abnormality detection were identified.

Main Results:

A methodology for machine abnormality detection was developed.
The Ergonomic Gap Analysis Matrix was created.

Conclusions:

The proposed methodology integrates ergonomic principles to improve machine abnormality detection.
This research provides a framework for enhancing human-machine interaction in maintenance settings.