Using high-frequency accelerometer to detect machine tilt

Abstract

The use of full-mechanized harvesting systems is limited by terrain factors, such as very steep slopes and roughness terrain. Focusing on salvage logging conditions, forest operations are characterized by high costs and reduced productivity, as well as in terms of safety. This work aims to identify and compare the relation between the machine tilt and ground slope and the effect in different harvesters and forwarders, based on the technology and type of machine. In particular, the machines involved in this study are a harvester and forwarder working in the specific condition of salvage logging in windthrow areas. High-frequency 3-axis accelerometers and a GNSS sensor were used to monitoring in continuous tilt and motion of the machines. From the position detected by the GNSS sensor, the ground slope was obtained. The results report that the correlation between the machine tilt and ground slope depends on the type of the machine and technology. The high-frequency 3-axis accelerometer results are affordable to detect an accurate machine tilt showing the possibility to use the combination of different low-cost sensors to analyze the operation condition of forest machines in complex terrain.

Development and validation of smartwatch-based activity recognition models for rigging crew workers on cable logging operations

“…In recent years, GNSS has been used in time studies to automate the estimation of cycle times [ 81 ], calculate machine productivity [ 82 ], characterize machine movements [ 83 ], and improve operational monitoring [ 84 ]. Additionally, time and motion studies have been conducted using both GNSS receivers and accelerometers to monitor tree planting [ 85 ], characterize manual felling using brush cutters [ 86 ], distinguish between chipping tasks [ 87 ], and monitor tilt and motion of various harvesters and forwarders in order to analyze operating conditions [ 88 ]. The availability of high-resolution sensor data collected seamlessly from GNSS-enabled mobile and wearable devices in real-time provides an opportunity to further model forestry work activities in greater detail than has been done using traditional methods, while simultaneously providing the basis for improved characterization of digital health and safety.…”