The effect of partial automation on the productivity and cost of a mobile tower yarder

Spinelli, Raffaele; Visser, Rien; Magagnotti, Natascia; Lombardini, Carolina

doi:10.15287/afr.2020.1883

Cited by 8 publications

(1 citation statement)

References 16 publications

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“…Consequently, the above-mentioned were among the reasons for which the usefulness of cheap solutions was researched in previous studies with the aim of providing data and tools to support operational planning and management, although they were externally generated and implemented. The rationale behind developing them was that once data could be extracted, or part of the functions of a system could be automated, they would positively be contributing to the overall efficiency, either by developing models in a traditional way with the aim of predicting efficiency or as an effect of function automation, as proved by other studies [14]. For instance, concepts of using different kinds of sensors to automate the extraction of useful data were described for rather more complicated operations and equipment [15,16].…”

Section: Introductionmentioning

confidence: 99%

Development of a Robust Machine Learning Model to Monitor the Operational Performance of Fixed-Post Multi-Blade Vertical Sawing Machines

Borz

Forkuo

Oprea-Sorescu

et al. 2022

Forests

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Monitoring the operational performance of the sawmilling industry has become important for many applications including strategic and tactical planning. Small-scale sawmilling facilities do not hold automatic production management capabilities mainly due to using obsolete technology which is an effect of low financial capacity and focus their strategy on increasing value recovery and saving resources and energy. Based on triaxial acceleration data collected over five days at a sampling rate of 1 Hz, a robust machine learning model was developed with the purpose of using it to infer the operational events based on lower sampling rates adopted as a strategy to collect long-term data. Among its performance metrics, the model was characterized in its training phase by a very high overall classification accuracy (CA = 98.7%), F1 score (98.4%) and a very low error rate (LOG LOSS = 5.6%). For a three-class problem, it worked very well in classifying the main events related to the operation of the machine, with active work being characterized by an F1 score of 99.6% and an error of 3.6%. By accounting for the same metrics, the model was proven to be invariant to the sampling rates of up to 0.05 Hz (20 s) and produced even better results in the testing phase (CA = 98.9%, F1 = 98.6%, LOG LOSS = 5.5%, for a testing sample extracted at 0.05 Hz), while there were no differences in the share of class data irrespective of the sampling rate. The developed model not only preserves a high classification performance in the training and testing phases but it also seems to be invariant to lower sampling rates, making it useful for prediction over data collected at low sampling rates. In turn, this would enable the use of cheap data collectors to be operated for extended periods of time in various locations and will save human resources and money associated with data collection. Further tests would be required only for validation and they could be supported by collecting and feeding new data to the model to infer the long-term performance of similar sawmilling machines.

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Section: Introductionmentioning

confidence: 99%

Development of a Robust Machine Learning Model to Monitor the Operational Performance of Fixed-Post Multi-Blade Vertical Sawing Machines

Borz

Forkuo

Oprea-Sorescu

et al. 2022

Forests

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Tower yarder powertrain performance simulation analysis: electrification study

et al. 2023

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Radical changes are necessary to address challenges related to global warming and pollution. Ever-tightening emission standards for combustion engines have already led to a drastic reduction in the amount of harmful gas and matter emitted. Drivetrain hybridization and electrification, which are becoming increasingly popular in all sectors, are two additional ways to achieve that goal. However, within the forestry sector most of the equipments still rely on conventional mechanic or hydraulic drivetrains. An example of this is tower yarders, the workhorse of the alpine logging industry. This work simulates the duty cycle and energy flow of tower yarders in logging operations, both with conventional diesel–hydraulic configuration and a proposed hybrid configuration. The objective is to determine the potential of hybridized drivetrains for tower yarder applications. Detailed models are developed to describe the cable-based extraction of timber and tower yarder internal processes. Extensive simulations were performed to determine force, power and energy components during the harvesting operation for both the diesel–hydraulic and hybrid drivetrains. Results confirm the large potential of the hybrid configuration for efficiency improvement and emission reduction, with estimated fuel savings of 45% and 63% in the uphill and downhill configurations, respectively. Extensive sensitivity analysis further demonstrates that the hybrid concept remains effective across a wide range of cable setup and transport characteristics. This confirms the large potential of electrified drivetrains, especially in the presence of very dynamic duty cycles, as is the case in cable-based logging equipment.

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