A Single Input Rule Modules Connected Fuzzy FMEA Methodology for Edible Bird Nest Processing

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

2014

Self Cite

The principle of ratios has been applied to many real world problems, e.g. the part-to-part and part-to-whole ratio formulations. As it is difficult for humans to provide an exact ratio in many real situations, we introduce a fuzzy ratio in this paper. We use some notions from fuzzy arithmetic to analyze fuzzy ratios captured from humans. An application of the formulated fuzzy ratio to a Single Input Rule Modules connected Fuzzy Inference System (SIRMs-FIS) is demonstrated. Instead of using a precise weight, fuzzy sets are employed to represent the relative importance of each rule module. The resulting fuzzy weights are explained as a fuzzy ratio on a weight domain. In addition, a new SIRMs-FIS model with fuzzy weights and part-to-whole fuzzy ratio is devised. A simulated example is presented to clarify the proposed SIRM-FIS model.

“…Besides that, the applicability of SIRMs-FIS with imprecise weights to real-world problems (e.g., FMEA [9] and education assessment [12]) will be investigated.…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A new fuzzy ratio and its application to the single input rule modules connected fuzzy inference system

Jong

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

2014

Self Cite

“…Liu, You, and You (2014g) proposed an interval 2-tuple hybrid weighted distance measure -based method for risk evaluation. In addition, a fuzzy SIRM (Single-Input-Rule-Module)-based RPN model (Jong, Tay, & Lim, 2014) and a two-stage Sugeno fuzzy-based RPN model with similarity reasoning (Jee, Tay, & Lim, 2015) were also proposed.…”

Section: Introductionmentioning

confidence: 99%

“…In our previous research, fuzzy inference systems (Tay & Lim, 2006;Jong et al, 2013Jong et al, , 2014Jee et al, 2015) and an adaptive clustering method, i.e., fuzzy adaptive reasoning theory (Tay, Jong, & Lim, 2015), have been applied to FMEA. Based on the findings, we have proven the importance of maintaining the monotonicity relationship between inputs (S, O, D) and the output (RPN score) (Tay & Lim, 2008a, 2008bJee et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Clustering and visualization of failure modes using an evolving tree

Chang

Expert Systems with Applications

2015

Self Cite

Despite the popularity of Failure Mode and Effect Analysis (FMEA) in a wide range of industries, two well-known shortcomings are the complexity of the FMEA worksheet and its intricacy of use. To the best of our knowledge, the use of computation techniques for solving the aforementioned shortcomings is limited. As such, the idea of clustering and visualization pertaining to the failure modes in FMEA is proposed in this paper. A neural network visualization model with an incremental learning feature, i.e., the evolving tree (ETree), is adopted to allow the failure modes in FMEA to be clustered and visualized as a tree structure. In addition, the ideas of risk interval and risk ordering for different groups of failure modes are proposed to allow the failure modes to be ordered, analyzed, and evaluated in groups. The main advantages of the proposed method lie in its ability to transform failure modes in a complex FMEA worksheet to a tree structure for better visualization, while maintaining the risk evaluation and ordering features. It can be applied to the conventional FMEA methodology without requiring additional information or data. A real world case study in the edible bird nest industry in Sarawak (Borneo Island) is used to evaluate the usefulness of the proposed method. The experiments show that the failure modes in FMEA can be effectively visualized through the tree structure. A discussion with FMEA users engaged in the case study indicates that such visualization is helpful in comprehending and analyzing the respective failure modes, as compared with those in an FMEA table. The resulting tree structure, together with risk interval and risk ordering, provides a quick and easily understandable framework to elucidate important information from complex FMEA forms; therefore facilitating the decision-making tasks by FMEA users. The significance of this study is twofold, viz., the use of a computational visualization approach to tackling two well-known shortcomings of FMEA; and the use of ETree as an effective neural network learning paradigm to facilitate FMEA implementations. These findings aim to spearhead the potential adoption of FMEA as a useful and usable risk evaluation and management tool by the wider community.

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

Jong

2014

Neural Comput & Applic

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.