Abstract
Sawmilling operations are typically one of the most important cells of the wood supply chain as they take the log assortments as inputs to which they add value by processing lumber and other semi-finite products. For this kind of operations, and especially for those developed at a small scale, long-term monitoring data is a prerequisite to make decisions, to increase the operational efficiency and to enable the precision of operations. In many cases, however, collection and handling of such data is limited to a set of options which may come at high costs. In this study, a low-cost solution integrating offline object tracking, signal processing and artificial intelligence was tested to evaluate its capability to correctly classify in the time domain the events specific to the monitoring of wood sawmilling operations. Discrete scalar signals produced from media files by tracking functionalities of the Kinovea® software (13,000 frames) were used to derive a differential signal, then a filtering-to-the-root procedure was applied to them. Both, the raw and filtered signals were used as inputs in the training of an artificial neural network at two levels of operational detail: fully and essentially documented data. While the addition of the derived signal made sense because it improved the outcomes of classification (recall of 92–97%) filtered signals were found to add less contribution to the classification accuracy. The use of essentially documented data has improved substantially the classification outcomes and it could be an excellent solution in monitoring applications requiring a basic level of detail. The tested system could represent a good and cheap solution to monitor sawmilling facilities aiming to develop our understanding on their technical efficiency.
Highlights
Reliable production monitoring data collected on long term is of a crucial importance in many industries because it provides an informed background for resource allocation and saving, optimization and operational improvement [1]
Of a particular importance in production monitoring is the ability to identify and delimitate different kinds of delays, which gives the computational basis for the net and gross productive performance metrics [7]; in relation to the delay-free time, often the studies are framed around the main functions that a machine or tool may enable, with the functions being interpreted in a spatial context
The main conclusion of this study is that the described and tested system holds a lot of potential for automating data collection, processing, and analysis in wood sawmilling time-and-motion applications
Summary
Reliable production monitoring data collected on long term is of a crucial importance in many industries because it provides an informed background for resource allocation and saving, optimization and operational improvement [1]. The situation is even more bottlenecked in the case of small-scale sawmills, which are relying on simple machines, that do not integrate production monitoring systems, operate at low production rates [3,4,5], and do not hold the financial ability to procure sophisticated monitoring systems. At least in such cases, the production monitoring solutions are few and limited by the amount of resources needed under the regular or advanced approaches to the problem. Of a particular importance in production monitoring is the ability to identify and delimitate different kinds of delays, which gives the computational basis for the net and gross productive performance metrics [7]; in relation to the delay-free time, often the studies are framed around the main functions that a machine or tool may enable, with the functions being interpreted in a spatial context
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