Automated Time Study of Forwarders using GPS and a vibration sensor

Abstract

Manual time and motion studies are the most common method to collect forest harvesting machine performance data. However, manual methods require skilled observers and are generally limited in duration, making it difficult to obtain a sufficiently large sample for machines with long cycle times such as skidders and forwarders. Of the automated data capture techniques studied previously, few have the breadth and ease of application to conduct long term autonomous studies for a range of harvesting machines. Analysis of Global Positioning System (GPS) data has been successfully trialled previously to conduct time studies of comparable accuracy with skilled observers, however, these approaches have been limited by the need for a degree of manual data processing. The current study trialled a fully automated system using analysis of GPS and vibration sensor data to estimate cycle times and time elements, and compare them with those determined using traditional time and motion studies for three forwarders at different sites. The mean difference between the cycle times estimated by the two methods was <1 second. This demonstrated the automated system’s ability to accurately determine each log landing location and extent and each work cycle start and end points. The correspondence between time elements using each approach was poorer. This was mainly caused by mislabelling of brief periods by the automated system as loading events when the forwarder slowed to negotiate steep areas at one study site. These errors may be able to be addressed by adding further rules to the automated system.