Tensile force monitoring on large winch-assist forwarders operating in British Columbia

The forest industry around the world is facing common challenges in accessing wood fiber on steep terrain. Fully mechanized harvesting systems based on specialized machines, such as winch-assist forwarders, have been specifically developed for improving the harvesting perfor- mances in steep grounds. While the mechanization process is recognized as a safety benefit, the use of cables for supporting the machine traction needs a proper investigation. Only a few studies have analyzed the cable tensile forces of winch-assist forwarders during real operations, and none of them focused on large machines normally used in North America. Consequently, a preliminary study focused on tensile force analysis of large winch-assist forwarders was conducted in three sites in the interior of British Columbia during the fall of 2017. The results report that in 86% of the cycles, the maximum working load of the cable was less than one-third of the minimum breaking load. The tensile force analysis showed an expected pattern of minimum tensile forces while the forwarders were traveling or unloading on the road site and high tensile forces when operating on steep trails, loading or traveling. Further analysis found that the maximum cycle tensile forces occurred most frequently when the machines were moving uphill, independently of whether they were empty or loaded. While the forwarders were operating on the trails, slope, travel direction, and distance of the machines from the anchor resulted statistically significant and able to account for 49% of tensile force variability. However, in the same conditions, the operator settings accounted for 77% of the tensile force variability, suggesting the human factor as the main variable in cable tensile force behavior during winch-assist operations

An optimization of on-line monitoring of simple linear and polynomial quality functions

This research aims to introduce a number of contributions for enhancing the statistical performance of some of Phase II linear and polynomial profile monitoring techniques. For linear profiles the idea of variable sampling size (VSS) and variable sampling interval (VSI) have been extended from multivariate control charts to the profile monitoring framework to enhance the power of the traditional T^2 chart in detecting shifts in linear quality models. Finding the optimal settings of the proposed schemes has been formulated as an optimization problem solved by using a Genetic Approach (GA). Here the average time to signal (ATS) and the average run length (ARL) are regarded as the objective functions, and ATS and ARL approximations, based on Markov Chain Principals, are extended and modified to capture the special structure of the profile monitoring. Furthermore,the performances of the proposed control schemes are compared with their fixed sampling counterparts for different shift levels in the parameters. The extensive comparison studies reveal the potentials of the proposed schemes in enhancing the performance of T^2 control chart when a process yields a simple linear profile. For polynomial profiles, where the linear regression model is not sufficient, the relationship between the parameters of the original and orthogonal polynomial quality profiles is considered and utilized to enhance the power of the orthogonal polynomial method (EWMA4). The problem of finding the optimal set of explanatory variable minimizing the average run length is described by a mathematical model and solved using the Genetic Approach. In the case that the shift in the second or the third parameter is the only shift of interest, the simulation results show a significant reduction in the mean of the run length distribution of the EWMA4 technique

SPC Methods for Detecting Simple Sawing Defects Using Real-Time Laser Range Sensor Data

Effective statistical process control (SPC) procedures can greatly enhance product value and yield in the lumber industry, ensuring accuracy and minimum waste. To this end, many mills are implementing automated real-time SPC with non-contact laser range sensors (LRS). These systems have, thus far, had only limited success because of frequent false alarms and have led to tolerances being set excessively wide and real problems being missed. Current SPC algorithms are based on manual sampling methods and, consequently, are not appropriate for the volume of data generated by real-time systems. The objective of this research was to establish a system for real-time LRS size control data for automated lumber manufacturing. An SPC system was developed that incorporated multi-sensor data, and new SPC charts were developed that went beyond traditional size control methods, simultaneously monitoring multiple surfaces and specifically targeting common sawing defects. In this paper, eleven candidate control charts were evaluated. Traditional X-bar and range charts are suggested, which were explicitly developed to take into account the components of variance in the model. Applying these methods will lead to process improvements for sawmills using automated quality control systems, so that machines producing defective material can be identified and prompt repairs made

Using Electromagnetic Induction Sensing to Understand the Dynamics and Interacting Factors Controlling Soil Salinity

Soil salinization is of great concern in the irrigated arid and semi-arid western United States due to its threat to sustainable agricultural productivity and thus is closely monitored. A widely accepted and traditional standard method for estimating soil salinity is the electrical conductivity of the saturated paste extracts (ECe). However, this method underestimates salinity due to ion pair formation in high ionic strength solution. Numerous studies have recommended the use of an electromagnetic induction (EMI) sensing technique to monitor field-scale soil salinity due to rapidness and non-destructiveness of the sampling. However, because the EMI measurement (ECa) is related to a host of soil properties, calibrating ECa to salinity in a non-homogeneous setting is particularly challenging. The main objective of this study is to understand the dynamics and interacting factors controlling soil salinity using an EMI sensor. Specifically, a correction is made for the underestimation of soil salinity from saturated paste extracts, and a calibration model is developed that is capable of predicting salinity directly from ECa despite the non-homogeneity of potential perturbing factors. A comparison is made of salinity measurement methods based on soil saturated pastes with respect to specific soil management goals. Results show that ion pairing exists even in low ionic strength solution and by diluting the saturated paste extracts to conductivities ≤ 0.03 dS m -1 (ECed), ion pairing is minimized. An improved salinity estimate is obtained by computing total dissolved solids (TDS, in mM) from the ECed values, and then multiplying the TDS by the dilution factor. We also developed a calibration model using quantile regression, which makes no assumption about the distribution of the errors, and which is capable of predicting low range soil salinity (such as that in calcareous soils) from ECa depth-weighted measurements (ECH25ECe). A comparison of ECe, ECed, ECH25ECe, and direct measurement of EC in soil pastes (“ Bureau of Soils Cup ” method, ECcup) across six depths, three texture groups, and the combinations of EC method and depth or texture groups, supports the use of the ECH25ECe method to rapidly and reliably monitor salinity in calcareous soils of arid and semiarid regions

Integrative Model-based clustering of microarray methylation and expression data

In many fields, researchers are interested in large and complex biological processes. Two important examples are gene expression and DNA methylation in genetics. One key problem is to identify aberrant patterns of these processes and discover biologically distinct groups. In this article we develop a model-based method for clustering such data. The basis of our method involves the construction of a likelihood for any given partition of the subjects. We introduce cluster specific latent indicators that, along with some standard assumptions, impose a specific mixture distribution on each cluster. Estimation is carried out using the EM algorithm. The methods extend naturally to multiple data types of a similar nature, which leads to an integrated analysis over multiple data platforms, resulting in higher discriminating power.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS533 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org