Strategies of failure diagnosis in computer-controlled manufacturing systems: empirical analysis and implications for the design of adaptive decision support systems

Abstract

This study investigates strategies in failure diagnosis at cutting-machine-tools with a verbal knowledge acquisition technique. Sixty-nine semi-structured interviews were performed with mechanical and electrical maintenance technicians, and a protocol analysis was conducted. Strategies were analysed in dependence of the technician's job experience, his familiarity with the problem and problem complexity. The technicians were categorized into three groups, novices, advanced, and experts, based upon level of experience. Results show that typical strategies of failure diagnosis are "Historical information", "Least effort", "Reconstruction", and "Sensory check". Strategies that lead to a binary reduction of the problem space, such as "Information uncertainty" and "Split half", play only a minor role in real-life failure diagnosis. Job experience and the familiarity with the problem significantly influenced the occurrence of strategies. In addition to "Symptomatic search" and "Topographic search", results show frequent use of case-based strategies, particularly in routine failures. In novel situations, technicians usually used "Topographic search". A software design method, the strategy-based software design (SSD) is proposed, that uses strategies to derive decision support systems, that are adaptive to the different working styles and the changing levels of experience in user groups. The methodology is briefly described and illustrated by the development of an information support system for maintenance and repair.