Siril Yella

Condition Monitoring Using Pattern Recognition Techniques on Data from Acoustic Emissions

Condition monitoring applications deploying the usage of impact acoustic techniques are mostly done intuitively by skilled personnel. In this article, a pattern recognition approach is taken to automate such intuitive human skills for the development of more robust and reliable testing methods. The focus of this work is to use the approach as a part of a major research project in the rail inspection area, within the domain of intelligent transport systems. Data from impact acoustic tests made on wooden beams have been used. The relation between condition of the wooden beams and respective sounds they make when struck, has been analyzed experimentally. Features were extracted from the acoustic emissions of wooden beams and were used for pattern classification. Features such as magnitude of the signal, natural logarithm of the magnitude and Mel-frequency cepstral coefficients, yielded good results. The extracted feature vectors were used as input to various pattern classifiers for further pattern recognition task. The effect of using classifiers like support vector machines and multi-layer perceptron has been tested and compared. Results obtained experimentally, demonstrate that support vector machines provide good detection rates for the classification of impact acoustic signals in the NDT domain

Image processing technique to count the number of logs in a timber truck

This paper summarises the results of using image processing technique to get information about the load of timber trucks before their arrival using digital images or geo tagged images. Once the images are captured and sent to sawmill by drivers from forest, we can predict their arrival time using geo tagged coordinates, count the number of (timber) logs piled up in a truck, identify their type and calculate their diameter. With this information we can schedule and prioritise the inflow and unloading of trucks in the light of production schedules and raw material stocks available at the sawmill yard. It is important to keep all the actors in a supply chain integrated coordinated, so that optimal working routines can be reached in the sawmill yard.

Effectiveness of Trigger Speed of Vehicle-Activated Signs on Mean and Standard Deviation of Speed

ABSTRACT Excessive or inappropriate speeds are a key factor in traffic fatalities and crashes. Vehicle-activated signs (VASs) are therefore being extensively used to reduce speeding to increase traffic safety. A VAS is triggered by an individual vehicle when the driver exceeds a speed threshold, otherwise known as trigger speed (TS). The TS is usually set to a constant, normally proportional to the speed limit on the particular segment of road. Decisions concerning the TS largely depend on the local traffic authorities. The primary objective of this article is to help authorities determine the TS that gives an optimal effect on the Mean and Standard Deviation of speed. The data were systematically collected using radar technology whilst varying the TS. The results show that when the applied TS was set near the speed limit, the standard deviation was high. However, the Standard Deviation decreased substantially when the threshold was set to the 85th percentile. This decrease occurred without a significant increase in the mean speed. It is concluded that the optimal threshold speed should approximate the 85th percentile, though VASs should ideally be individually calibrated to the traffic conditions at each site.

Machine vision approach for automating vegetation detection on railway tracks

Abstract The presence of vegetation on railway tracks (amongst other issues) threatens track safety and longevity. However, vegetation inspections in Sweden (and elsewhere in the world) are currently being carried out manually. Manually inspecting vegetation is very slow and time consuming. Maintaining an even quality standard is also very difficult. A machine vision-based approach is therefore proposed to emulate the visual abilities of the human inspector. Work aimed at detecting vegetation on railway tracks has been split into two main phases. The first phase is aimed at detecting vegetation on the tracks using appropriate image analysis techniques. The second phase is aimed at detecting the rails in the image to determine the cover of vegetation that is present between the rails as opposed to vegetation present outside the rails. Results achieved in the current work indicate that the machine vision approach has performed reasonably well in detecting the presence/absence of vegetation on railway tracks when compared with a human operator.

Condition monitoring of wooden railway sleepers using time-frequency techniques and pattern classification

Railway sleepers are a key engineering element of all railways. Lack of much sophistication in monitoring railway sleepers makes it a key problem within the rail transportation domain. Current day condition monitoring applications involving wooden railway sleepers are mostly carried out through visual inspection and if necessary some impact acoustic examination is carried out. Decision making is largely based on intuition; moreover the process of manually inspecting sleepers is rather slow and expensive. Maintaining an even quality standard is another serious issue. In this article, a pattern recognition and classification approach is taken to automate such intuitive human skills for the development of more robust and reliable testing methods. Features were extracted from the impact acoustic emissions of wooden sleepers and were used for pattern classification. Time-frequency based feature extraction techniques such Short-time Fourier Transform and Discrete Wavelet Transform yielded good results. Multi-layer perceptron, Radial Basis Function Neural Networks and Support vector machine classifiers have been tested and compared. Further classifier fusion was investigated by considering the output of single best classifiers as input to a new classifier with an aim of improving performance. Results obtained experimentally demonstrate a classification accuracy of around 84%.

Reliability of Manual Assessments in Determining the Types of Vegetation on Railway Tracks

Current day vegetation assessments within railway maintenance are (to a large extent) carried out manually. This study has investigated the reliability of such manual assessments by taking three non-domain experts into account. Thirty-five track images under different conditions were acquired for the purpose. For each image, the raters’ were asked to estimate the cover of woody plants, herbs and grass separately (in %) using methods such as aerial canopy cover, aerial foliar cover and sub-plot frequency. Visual estimates of raters’ were recorded and analysis-of-variance tests on the mean cover estimates were investigated to see whether if there were disagreements between the raters’. Intra-correlation coefficient was used to study the differences between the estimates. Results achieved in this work revealed that seven out of the nine analysis-of-variance tests conducted in this study have demonstrated significant difference in the mean estimates of cover (p < 0.05).

Multi-agent simulation of sawmill yard operations

This paper reports the findings of using multi-agent based simulation model to evaluate the sawmill yard operations within a large privately owned sawmill in Sweden, Bergkvist Insjon AB in the curr ...

Feature Selection and Bleach Time Modelling of Paper Pulp Using Tree Based Learners

Paper manufacturing is energy demanding and improved modelling of the pulp bleach process is the main non-invasive means of reducing energy costs. In this paper, time it takes to bleach paper pulp to desired brightness is examined. The model currently used is analysed and benchmarked against two machine learning models Random Forest and TreeBoost. Results suggests that the current model can be superseded by the machine learning models and it does not use the optimal compact subset of features. Despite the differences between the machine learning models, a feature ranking correlation has been observed for the new models. One novel, yet unused, feature that both machine learning models found to be important is the concentration of bleach agent.

Inter-rater Reliability in Determining the Types of Vegetation on Railway Trackbeds

Vegetation growing on railway trackbeds and embankments can present several potential problems. Consequently, such vegetation is controlled through various maintenance procedures. In order to investigate the extent of maintenance needed, one of the first steps in any maintenance procedure is to monitor or inspect the railway section in question. Monitoring is often carried out manually by sending out inspectors or by watching recorded video clips of the section in question. To facilitate maintenance planning, the ability to assess the extent of vegetation becomes important. This paper investigates the reliability of human assessments of vegetation on railway trackbeds.

SIMULATION MODEL USING META HEURISTIC ALGORITHMS FOR ACHIEVING OPTIMAL ARRANGEMENT OF STORAGE BINS IN A SAWMILL YARD

Bin planning (arrangements) is a key factor in the timber industry. Improper planning of the storage bins may lead to inefficient transportation of resources, which threaten the overall efficiency and thereby limit the profit margins of sawmills. To address this challenge, a simulation model has been developed. However, as numerous alternatives are available for arranging bins, simulating all possibilities will take an enormous amount of time and it is computationally infeasible. A discrete-event simulation model incorporating meta-heuristic algorithms has therefore been investigated in this study. Preliminary investigations indicate that the results achieved by GA based simulation model are promising and better than the other meta-heuristic algorithm. Further, a sensitivity analysis has been done on the GA based optimal arrangement which contributes to gaining insights and knowledge about the real system that ultimately leads to improved and enhanced efficiency in sawmill yards. It is expected that the results achieved in the work will support timber industries in making optimal decisions with respect to arrangement of storage bins in a sawmill yard.