Ergonomics for enhancing detection of machine abnormalities

Issue title: Special Section: Ergonomics and Work in Sri Lanka

Guest editors: K. Jayaratne, C. De Silva and M. Danansuriya

Article type: Research Article

Authors: Illankoon, Prasanna^{a; *} | Abeysekera, John^b | Singh, Sarbjeet^c

Affiliations: [a] Luleå University of Technology, Sweden | [b] Work Science Academy, Sweden/Sri Lanka | [c] Mechanical Engineering Department, Government College of Engineering & Technology, Jammu, India

Correspondence: [*] Address for correspondence: Prasanna Illankoon, No. 14, Sangabo Mawatha, Borupana Road, Rathmalana, Sri Lanka. Tel.: +94 772640795; Fax: +94 112465011; E-mail: [email protected].

Abstract: 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. OBJECTIVES: 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. RESULTS AND CONCLUSIONS: As the outcome of this research, a methodology comprising of an Ergonomic Gap Analysis Matrix for machine abnormality detection is presented.

Keywords: Autonomous maintenance, sensory types, cognitive task analysis

DOI: 10.3233/WOR-162416

Journal: Work, vol. 55, no. 2, pp. 271-280, 2016