Presentation on theme: "1 Chian Haur Jong, 1* Kai Meng Tay, 2 Chee Peng Lim 1 Faculty of Engineering, Universiti Malaysia Sarawak, Malaysia 2 Centre for Intelligent Systems Research,"— Presentation transcript:
1 Chian Haur Jong, 1* Kai Meng Tay, 2 Chee Peng Lim 1 Faculty of Engineering, Universiti Malaysia Sarawak, Malaysia 2 Centre for Intelligent Systems Research, Deakin University, Australia. 1* email@example.com A Single Input Rule Modules Connected Fuzzy FMEA Methodology for Edible Bird Nest Processing
Presentation Outline Introduction Problem Statements Objectives Preliminary The Proposed Fuzzy FMEA procedure Case study: EBN food processing Concluding Remarks
Failure Mode and Effects Analysis (FMEA) is a tool for quality assurance and reliability improvement. In FMEA, a failure mode occurs when a component, system, subsystem, or process fails to meet the designated intent. Traditionally, the Risk Priority Number (RPN) model is used to rank failure modes and it is defined by the equation below: RPN=S x O x D An RPN model defined by 3 risk factors, i.e. Severity (S), Occurrence (O) and Detect (D). S and O are the frequency and seriousness (effects) of a failure mode, and D is the effectiveness to detect a failure mode before it reaches the customer. Introduction: FMEA methodology and its RPN model
Bowles and Paláez (1995) had suggested using an Fuzzy Inference System (FIS) to aggregate S, O, and D ratings (namely an FIS-based RPN model), instead of a simple product function An FIS-based RPN was introduced, for the following reasons. 1. It allows expert knowledge and experience to be incorporated 2. It is robust against uncertainty and vagueness 3. It allows a nonlinear relationship between the RPN score and the three risk factors to be formed 4. The three risk factors can be captured qualitatively, instead of quantitatively J.B. Bowles and C.E. Pelaez, Fuzzy Logic prioritization of failures in a system failure mode, effect and criticality analysis, Reliability Engineering and System Safety, Vol 50, No 2, pp. 203-213, (1995). Introduction: The FIS-based RPN model
Various FIS-based RPN models have since been developed and applied to a variety of application domains, e.g. 1. Maritime Z. Yang, S. Bonsall and J. Wang, Fuzzy rule-based Bayesian reasoning approach for prioritization of failures in FMEA, IEEE Transactions on Reliability, Vol. 57, No.3, pp. 517-528, (2008). 2. Nuclear Engineering Systems A.C.F. Guimarães and C.M.F. Lapa, Fuzzy inference to risk assessment on nuclear engineering systems, Applied Soft Computing, Vol 7, No1, pp17-28, (2007) 3. Semiconductor Industry K.M. Tay, C.P. Lim, Fuzzy FMEA with a guided rules reduction system for prioritization of failures. International Journal of Quality And Reliability Management. Vol. 23, No. 8, pp. 1047 – 1066 (2006). 4. Engine system K Xu, L.C Tang, M Xie, S.L Ho, M.L Zhu, Fuzzy assessment of FMEA for engine systems, Reliability Engineering & System Safety, Vol 75, No 1, pp.17-29, (2002)
An FIS-based RPN model suffers from two major shortcomings viz., Shortcoming 1: combinatorial rule explosion The first shortcoming suggests that an FIS-based RPN model requires a large number of fuzzy rules, and it is a tedious process to gather a complete fuzzy rule base in practice. K.M. Tay and C.P. Lim, Fuzzy FMEA with a guided rules reduction system for prioritization of failures, International Journal of Quality & Reliability Management, Vol. 23, No 8, pp.1047 – 1066, (2006). Y.C. Jin, Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement, IEEE Transactions on Fuzzy Systems, Vol. 2 No. 8, pp. 212-21, (2000). Problem Statements
Indeed, a search in the literature reveals that a lot of investigations for rule reduction in FIS-based RPN models have been reported for problem related to the combinatorial rule explosion. Tay and Lim (2006) implement a guided rule reduction system to improve the FMEA methodology by identifying only the important fuzzy rules An FIS-based RPN model with 125 fuzzy rules was reduced to 35 fuzzy rules with the use of the method in Pillay and Wang (2003) K.M. Tay and C.P. Lim, Fuzzy FMEA with a guided rules reduction system for prioritization of failures, International Journal of Quality & Reliability Management, Vol. 23, No 8, pp.1047 – 1066, (2006). A. Pillay and J. Wang, Modified failure mode and effects analysis using approximate reasoning, Reliability Engineering and System Safety, Vol 79, No 1, pp. 69-85,(2003). Problem Statements
A method which proposed by Guimara˜es and Lapa (2004) was successfully reduced FIS-based FMEA model with 125 fuzzy rules to 14 fuzzy rules. In Guimara˜es and Lapa (2006), the authors further reduced FIS-based FMEA model with 125 fuzzy rules to 6 fuzzy rules. A.C.F. Guimarães and C.M.F. Lapa, Fuzzy FMEA applied to PWR chemical and volume control system, Progress in Nuclear Energy, Vol. 44, No. 3, pp. 191-213, (2004). A.C.F. Guimaraes and C.M.F. Lapa, Hazard and operability study using approximate reasoning in light-water reactors passive systems, Nuclear Engineering and Design, Vol 236, No 12, pp. 1256-1263,(2006). Problem Statements
Shortcoming 2: Monotonicity property fulfillment For an FIS-based RPN that fulfills the monotonicity property, dRPN / dx 0, where x[S, O, D]. It is essential to fulfill the monotonicity property. Fulfillment of the monotonicity property is difficult, and yet important to ensure the validity of the RPN scores K.M. Tay and C.P. Lim, On monotonic sufficient conditions of fuzzy inference systems and their applications, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Vol 19, No 5, pp. 731-757, (2011). K.M. Tay and C.P. Lim, On the use of fuzzy inference techniques in assessment models: part I - theoretical properties, Fuzzy Optimization and Decision Making, Vol 7, No 3, pp.269- 281, (2008). K.M. Tay and C.P. Lim, On the use of fuzzy inference techniques in assessment models: part II: industrial applications, Fuzzy Optimization and Decision Making, Vol 7, No 3, pp. 283-302, (2008). Problem Statements
A zero-order Single Input Rule Modules (SIRMs) connected Fuzzy Inference System (FIS) is used. Theorems from Sekis papers is simplified and used. N. Yubazaki, J.Q. Yi and K. Hirota, SIRMs (Single Input Rule Modules) Connected fuzzy inference model, Journal of Advanced Computational Intelligence, Vol 1, No1, pp 22-29, (1997). H. Seki, H. Ishii and M. Mizumoto, On the generalization of Single Input Rule Modules Connected Type fuzzy reasoning method, IEEE Transactions on Fuzzy Systems, Vol.16, No.5, pp.1180-1187, (2008). Seki, H., Ishii, H., Mizumoto, M.: On the monotonicity of fuzzy-inference method related to T-S inference Method. IEEE Trans. Fuzzy Syst. 18, 629-634 (2010). Seki, H.,Tay, K.M.: On the monotonicity of fuzzy inference models.J. Adv. Comput. Intell. Intell. Informat. 16, 592-602 (2012). Objectives: Direction of the paper
A new fuzzy FMEA methodology with Single Input Rule Modules (SIRMs) connected Fuzzy Inference System (FIS)-FIS-based RPN model is proposed. Monotonicity property of the SIRMs-connected FIS-based RPN model is preserved to ensure an valid output for risk evaluation. A case study relating to edible bird nest (EBN) processing is demonstrated. Objectives of the paper
Preliminary: SIRMs connected FIS model The use of an SIRMs connected FIS model in FMEA shall reduce the number of fuzzy rules drastically. From the literature, an SIRMs connected FIS model require less fuzzy rule in FIS modeling. EXAMPLES: 1. The zeros-order SIRMs connected FIS was proposed by Yubazaki et al (1997) for a plural input fuzzy control to reduce the number of fuzzy rules required in FIS modeling. 2. Seki et al (2008) proposed Functional-type SIRMs connected FIS, in which the consequences are generalized as functions. N. Yubazaki, J.Q. Yi and K. Hirota, SIRMs (Single Input Rule Modules) Connected fuzzy inference model, Journal of Advanced Computational Intelligence, Vol 1, No1, pp 22-29, (1997). H. Seki, H. Ishii and M. Mizumoto, On the generalization of Single Input Rule Modules Connected Type fuzzy reasoning method, IEEE Transactions on Fuzzy Systems, Vol.16, No.5, pp.1180-1187, (2008).
The proposed SIRMs-based FMEA methodology is summarized in Fig. 3. The details are as follows. 1. Define the scale tables for S, O, and D. 2. Construct the membership functions for each input factor (i.e., S, O and D). Condition 1 is used as the governing equation. 3. Gather expert knowledge to construct the fuzzy rule base. Condition 2 is imposed in the construction phase. 4. Construct the SIRMs connected FIS-based RPN model. 5. Study the intent, purpose, goal, objective for the product/process. Generally, it is identified by studying the interaction among the component/process flow diagram and is followed by a task analysis. 6. Identify the potential failures of a product/process, which include problems, concerns, and opportunities for improvement.
7. Identify the consequence of each failure to other components or the other processes, operation customers, and government regulations. 8. Identify the potential root causes of the potential failures. 9. Identify the method/procedure to detect/ prevent the potential failures. 10. Evaluate S, O, and D based on the predefined scale tables. 11. Calculate the fuzzy RPN (FRPN) scores using the SIRMs connected FIS-based RPN model. 12. Make any necessary corrections. Go back to step (5) if needed. 13. End
Modeling of the SIRMs connected FIS-based RPN Model
Define the scale tables First, the scale tables for S, O, and D is defined. An example of scale table for O
The membership functions for S, O and D are designed in such fuzzy membership functions are compare-able (refer to Definition 1 and 2) to ensure the condition 1 is fulfilled (refer theorem 1). Example: Fig. 5.The membership function for Occurrrence Design of the membership function
A set of fuzzy rules, as summarized following is used, is used. Gathering of the fuzzy rules
Data and information gathered from two swiftlets farms and two EBN production plants in Sarawak (as depicted in the Fig. 7), Malaysia are used to assess the efficacy of the proposed approach. Fig 7: Geographical locations of two swiftlets farms and two EBN production plants in Sarawak, Malaysia Case study: EBN food processing
Risk evaluation results with the SIRMs FIS-based RPN model
From Table 3, Columns Sev (S), Occ (O), and Det (D) show the S, O, and D ratings that describe each failure mode. The failure risk evaluation and prioritization outcomes from the conventional RPN model are explained in columns RPN and RPN Rank, respectively. For the SIRMs connected FIS-based RPN model, its risk evaluation and prioritization outcomes are summarized in columns FRPN and FRPN Rank, respectively.
From Table 3, the fulfillment of the monotonicity property can be observed. As an example, failure modes #1, #2, and #3 have the same S and O ratings, i.e., 1 and 1, respectively. The D scores are 1, 2, and 3, for each of the failure modes, respectively. With the SIRMs connected FIS-based RPN model, the FRPN scores are 1, 51, and 101, respectively; hence satisfying the monotonicity property.
Surface plot To easily observe the monotonity property of the overall model (as shown in Table 3), a surface plot can be used. Fig. 7. A surface plot of FRPN versus severity and occurrence (with detect = 10) Fig. 7 depicts a surface plot for FRPN versus S and O, with D = 10. A monotonic surface can be observed easily.
Summary A new fuzzy FMEA methodology with a zero-order SIRMs connected FIS-based RPN model has been proposed. The theorems in [9-10] have been simplified and adopted as a set of governing equations in the proposed fuzzy FMEA methodology. The proposed approach constitutes a new fuzzy FMEA methodology with a reduced fuzzy rule base, which satisfies the monotonicity property. A case study relating to EBN processing has been examined. The results have shown that the proposed approach is able to reduce the number of fuzzy rules effectively and yet, to satisfy the monotonicity property. This paper also contributes to a new application of fuzzy FMEA to food processing (i.e., EBN processing).