Abstract
Abstract: Proximity movements between vehicles transporting materials in manufacturing plants, or “interfaces”, result in occupational injuries and fatalities. Risk assessment for interfaces is currently limited to techniques such as safety audits, originally designed for static environments. A data-driven alternative for dynamic environments is desirable to quantify interface risks and to enable the development of effective countermeasures. We present a method to estimate the Risk Prioritization Number (RPN) for mobile vehicle interfaces in manufacturing environments, based on the Probability-Severity-Detectability (PSD) formulation. The highlight of the method is the estimation of the probability of occurrence (P) of vehicle interfaces using machine learning and computer vision techniques. A PCA-based sparse feature vector for machine learning characterizes vehicle geometry from a top-down perspective. Supervised classification on sparse feature vectors using Support Vector Machines (SVMs) is employed to detect vehicles. Computer vision techniques are used for position tracking to identify interfaces and to calculate their probability of occurrence (P). This leads to an automated calculation of RPN based on the PSD formulation. Experimental data is collected in the laboratory using a sample work area layout and scale versions of vehicles. Vehicle interfaces and movements were physically simulated to train and test the machine learning model. The performance of the automated system is compared with human annotation to validate the approach.
Highlights
This study presents a methodology for automated detection and risk assessment for interactions between vehicles engaged in material movement in manufacturing work areas
A modified version of the Support Vector Machines (SVMs)-BF that is based on a discounted BF further improved the precision to 93% at the expense of decreasing the coverage to 93%
Risk assessment strategies for workplace risks such as Failure Modes Effects Analysis (FMEA) based on the Risk Prioritization Number (RPN) metric tend to focus on location-specific activities, such as working in a specific area or manufacturing cell
Summary
This study presents a methodology for automated detection and risk assessment for interactions between vehicles engaged in material movement in manufacturing work areas. The manufacturing sector is vulnerable from a safety perspective: it ranked sixth in the US for number of fatal occupational injuries in 2011 (Bureau of Labor Statistics, 2011). The high incidence rate of work-related accidents, injuries, and fatalities are the primary motivation for this paper. The Bureau of Labor Statistics (2014) revealed that close to 4000 employees in the United States were fatally injured at work due to machine related incidents. The high fatality and injury rates have direct consequences to organizations, including employee turnover, absenteeism, and legal repercussions. This compels organizations to place justifiably high emphasis on workplace safety. This compels organizations to place justifiably high emphasis on workplace safety. De Vries et al (2016) provide an example of a top-down, management-driven model called safetyspecific transformational leadership (SSTL) to improve workplace safety
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