Abstract
Safety in the complex industry such as monitoring, maintenance and inspection tasks in nuclear power plants (NPPs) and aircraft industry relies on high team performance. Although various preventive methods have been developed, human error still exists. In order to increase monitoring, maintenance and inspection safety, the aims of this study are to (1) evaluate the mental workload of maintenance engineers at a NPP in Taiwan according to the factors affecting the mental workload, (2) develop a real-time warning model (RTWM) by assessing team performance (response time, error rates) and mental workload, and (3) develop an on-line maintenance assistance platform (on-line MAP) for technician training and performing aircraft maintenance tasks. The research methods include (1) designing the questionnaire and field study on mental workload comparison during the on-line maintenance of digital and analog systems, (2) applying the group method of data handling (GMDH) algorithm to predict team performance and the fuzzy inference to construct the RTWM, and (3) considering the impact of the performance-shaping factors (PSFs) and human error as well as error effect in each procedure to develop an on-line MAP. To model RTWM, experiments were conducted on computer-supported cooperative work (CSCW) in the personal computer transient analyzer (PCTRAN) simulator. The simulator and teamwork were designed to simulate real tasks of the control room of a new nuclear power plant in Taiwan. In addition, important physiological parameters, the NASA-TLX questionnaire, team response time, and team error rates were collected from 39 participants. Moreover, to design the on-line MAP, the functions include (1) native language explanations, (2) the effects of human errors on system and human, (3) human errors relative to seriousness rank and frequency, and (4) graphic information aids in each removal and installation procedure. The results indicated (1) mental workload was lower in maintaining digital systems than that in analog systems, (2) the proposed RTWM can efficiently predict teamwork performance to maintain appropriate mental workload as well as ensure system safety, and (3) the proposed on-line MAP may potentially not only increase risk knowledge, situation awareness, and performance of workers but also decrease workload and maintenance incidents.