A new application of an evolving tree to failure mode and effect analysis methodology

Failure Mode and Effect Analysis (FMEA) is a popular safety and reliability analysis methodology for examining potential failure modes of products, process, designs, or services, in a wide range of industries. Despite its popularity, there are a number of limitations of FMEA, and two highlighted issues are the bulky FMEA form and its intricacy of use. To overcome these shortcomings, we introduce the idea of visualisation pertaining to the failure modes or control actions in FMEA. A visualisation model with an incremental learning feature, i.e., the evolving tree (ETree), is adopted to allow the failure modes or control actions in FMEA to be clustered and visualized. The failure modes or control actions are grouped and visualized with consideration of their Severity, Occurrence, and Detection scores. Our proposed approach allows the failure modes or control actions to be mapped into a tree structure for visualisation. The devised approach is evaluated with a benchmark problem. The experiments show that the control actions of FMEA can be visualised through the tree structure, which provides a quick and easily understandable platform of the FMEA spreadsheet to facilitate decision making tasks.

A clustering-based failure mode and effect analysis model and its application to the edible bird nest industry

Failure mode and effect analysis (FMEA) is a popular safety and reliability analysis tool in examining potential failures of products, process, designs, or services, in a wide range of industries. While FMEA is a popular tool, the limitations of the traditional Risk Priority Number (RPN) model in FMEA have been highlighted in the literature. Even though many alternatives to the traditional RPN model have been proposed, there are not many investigations on the use of clustering techniques in FMEA. The main aim of this paper was to examine the use of a new Euclidean distance-based similarity measure and an incremental-learning clustering model, i.e., fuzzy adaptive resonance theory neural network, for similarity analysis and clustering of failure modes in FMEA; therefore, allowing the failure modes to be analyzed, visualized, and clustered. In this paper, the concept of a risk interval encompassing a group of failure modes is investigated. Besides that, a new approach to analyze risk ordering of different failure groups is introduced. These proposed methods are evaluated using a case study related to the edible bird nest industry in Sarawak, Malaysia. In short, the contributions of this paper are threefold: (1) a new Euclidean distance-based similarity measure, (2) a new risk interval measure for a group of failure modes, and (3) a new analysis of risk ordering of different failure groups. © 2014 The Natural Computing Applications Forum.

A clustering-based failure mode and effect analysis model and its application to the edible bird nest industry

Failure mode and effect analysis (FMEA) is a popular safety and reliability analysis tool in examining potential failures of products, process, designs, or services, in a wide range of industries. While FMEA is a popular tool, the limitations of the traditional Risk Priority Number (RPN) model in FMEA have been highlighted in the literature. Even though many alternatives to the traditional RPN model have been proposed, there are not many investigations on the use of clustering techniques in FMEA. The main aim of this paper was to examine the use of a new Euclidean distance-based similarity measure and an incremental-learning clustering model, i.e., fuzzy adaptive resonance theory neural network, for similarity analysis and clustering of failure modes in FMEA; therefore, allowing the failure modes to be analyzed, visualized, and clustered. In this paper, the concept of a risk interval encompassing a group of failure modes is investigated. Besides that, a new approach to analyze risk ordering of different failure groups is introduced. These proposed methods are evaluated using a case study related to the edible bird nest industry in Sarawak, Malaysia. In short, the contributions of this paper are threefold: (1) a new Euclidean distance-based similarity measure, (2) a new risk interval measure for a group of failure modes, and (3) a new analysis of risk ordering of different failure groups.

Retrospective application of remote sensing to the Tasmanian lakeland

This thesis describes the research undertaken for a degree of Master of Science in a retrospective study of airborne remotely sensed data registered in 1990 and 1993, and field captured data of aquatic humus concentrations for ~ 45 lakes in Tasmania. The aim was to investigate and describe the relationship between the remotely sensed data and the field data and to test the hypothesis that the remotely sensed data would establish further evidence of a limnological corridor of change running north-west to south- east. The airborne remotely sensed data consisted of data captured by the CSIRO Ocean Colour Scanner (OCS) and a newly developed Canadian scanner, a compact airborne spectrographic imager (CASI). The thesis investigates the relationship between the two kinds of data sources. The remotely sensed data was collected with the OCS scanner in 1990 (during one day) and with both the OCS and the CASI in 1993 (during three days). The OCS scanner registers data in 9 wavelength bands between 380 nm and 960 nm with a 10-20 nm bandwidth, and the CASI in 288 wavelength bands between 379.57 nm and 893.5 nm (ie. spectral mode) with a spectral resolution of 2.5 nm. The remotely sensed data were extracted from the original tapes with the help of the CSIRO and supplied software and digital sample areas (band value means) for each lake were subsequently extracted for data manipulation and statistical analysis. Field data was captured concurrently with the remotely sensed data in 1993 by lake hopping using a light aircraft with floats. The field data used for analysis with the remotely sensed data were the laboratory determined g440 values from the 1993 water samples collated with g440 values determined from earlier years. No spectro-radiometric data of the lakes, data of incoming irradiance or ancillary climatic data were captured during the remote sensing missions. The sections of the background chapter in the thesis provide a background to the research both in regards to remote sensing of water quality and the relationship between remotely sensed spectral data and water quality parameters, as well as a description of the Tasmanian lakes flown. The lakes were divided into four groups based on results from previous studies and optical parameters, especially aquatic humus concentrations as measured from field captured data. The four groups consist of the ‘green” clear water lakes mostly situated on the Central Plateau, the ‘brown” highly dystrophic lakes in western Tasmania, the ‘corridor” lakes situated along a corridor of change lying approximately between the two lines denoting the Jurassic edge and 1200 mm isohyet, and the ‘eastern, turbid” lakes make up the fourth group. The analytical part of the research work was mostly concerned with manipulating and analysing the CASI data because of its higher spectral resolution. The research explores methods to apply corrections to this data to reduce the disturbing effects of varying illumination and atmospheric conditions. Three different methods were attempted. In the first method two different standardisation formulas are applied to the data as well as ‘day correction” factors calculated from data from one of the lakes, Lake Rolleston, which had data captured for all three days of the remote sensing operations. The standardisation formulas were also applied to the OCS data. In second method an attempt to reduce the effects of the atmosphere was performed using spectro-radiometric captured in 1988 for one of the lakes flown, Great Lake. All the lake sample data were time normalised using general irradiance data obtained from the University of Tasmania and the sky portion as calculated from Great Lake upwelling irradiance data was then subtracted. The last method involved using two different band ratios to eliminate atmospheric effects. Statistical analysis was applied to the data resulting from the three methods to try to describe the relationship between the remotely sensed data and the field captured data. Discriminant analysis, cluster analysis and factor analysis using principal component analysis (pea) were applied to the remotely sensed data and the field data. The factor scores resulting from the pca were regressed against the field collated data of g440 as were the values resulting from last method. The results from the statistical analysis of the data from the first method show that the lakes group well (100%) against the predetermined groups using discriminant analysis applied to the remotely sensed CASI data. Most variance in the data are contained in the first factor resulting from pca regardless of data manipulation method. Regression of the factor scores against g440 field data show a strong non- linear relationship and a one-sided linear regression test is therefore considered an inappropriate analysis method to describe the dataset relationships. The research has shown that with the available data, correction and analysis methods, and within the scope of the Masters study, it was not possible to establish the relationships between the remotely sensed data and the field measured parameters as hoped. The main reason for this was the failure to retrieve remotely sensed lake signatures adequately corrected for atmospheric noise for comparison with the field data. This in turn is a result of the lack of detailed ancillary information needed to apply available established methods for noise reduction - to apply these methods we require field spectroradiometric measurements and environmental information of the varying conditions both within the study area and within the time frame of capture of the remotely sensed data.

Application of fuzzy inference techniques to FMEA

In traditional Failure Mode and Effect Analysis (FMEA), the Risk Priority Number (RPN) ranking system is used to evaluate the risk level of failures, to rank failures, and to prioritize actions. This approach is simple but it suffers from several weaknesses. In an attempt to overcome the weaknesses associated with the traditional RPN ranking system, several fuzzy inference techniques for RPN determination are investigated in this paper. A generic Fuzzy RPN approach is described, and its performance is evaluated using a case study relating to a semiconductor manufacturing process. In addition, enhancements for the fuzzy RPN approach are proposed by refining the weights of the fuzzy production rules.

Magnetic liquid marbles, their manipulation and application in optical probing

Magnetic liquid marbles, an encapsulation of liquid droplet with hydrophobic magnetic particles, show remarkable responsiveness to external magnetic force and great potential to be used as a discrete droplet microfluidic system. In this study, we presented the manipulation of a magnetic liquid marble under an external magnetic field and calculated the maximum frictional force, the magnetic force required for actuating the liquid marbles and the effective surface tension of the magnetic liquid marble, as well as the threshold volume for the transition from quasi-spherical to puddle-like shape. By taking advantage of the unique feature of being opened and closed reversibly, we have proven the encapsulated droplets can be detected optically with a reflection-mode probe. Combining the open-close and optical detection also enables to probe chemical reactions taking place within liquid marbles. These remarkable features offer a simple yet powerful alternative to conventional discrete microfluidic systems and may have wide applications in biomedical and drug discovery. © 2012 The Author(s).

Adapting ultrasonic assisted wool scouring for industrial application

Scouring is the first stage of wool processing and is essential for determining the quality of fiber. Traditional aqueous scouring is a method that emulsifies and removes contaminants (such as wool grease, suint, and dirt) from the fiber surface; however, it promotes wool felting and is energy and water intensive. This study has shown that modification of the traditional wool scouring line by introducing an ultrasonic device could be a viable alternative for the wool scouring industry. A standard six-bath wool scouring line was retrofitted with two ultrasonic panels working at 80 kHz in bath 2. Scouring was carried out in three modes: conventional mode without the transport rake, ultrasonic mode without the transport rake, and conventional mode with the transport rake. Fiber samples after scouring were measured for color index, residual grease content, and residual ash content. Ultrasonic scouring was found to improve removal of grease and ash from the wool fiber. Modifications were proposed for the design of an industrial scouring line including the addition of fiber transport and dunking rollers and number of baths for the installation. © The Author(s) 2014.

Application of the embedded element technique to predict interlaminar failure

In this study a modelling technique, namely, the embedded element method is assessed to evaluate its ability to predict Mode I interlaminar failure. The embedded element technique takes advantage of the embedded constraint in ABAQUS and allows the two constituents, fibre and matrix, to be meshed independently. Since the two constituents can be meshed independently a contiguous mesh is not required and the time taken to create an acceptable mesh is significantly reduced. The embedded element technique has been used to model fibre-reinforced composite structures, however, to date no studies have been conducted which combine the embedded element technique with an interlaminar damage model. The work described herein evaluates the ability of the embedded element technique to predict mode I interlaminar failure. DCB specimens were modelled using the embedded element method and a traditional 3D solid FE modelling approach with the predictions compared against experimental data. Both modelling approaches provided good agreement with experimental results. The good agreement demonstrates that the embedded element technique is capable of providing a response that is equivalent to a traditional 3D solid FE models and is particularly suited to modelling thick composite structures with complex geometry.