An Integrated Fuzzy Fault Tree Model with Bayesian Network-Based Maintenance Optimization of Complex Equipment in Automotive Manufacturing

Abstract

Process integrity, insufficient data, and system complexity in the automotive manufacturing sector are the major uncertainty factors used to predict failure probability (FP), and which are very influential in achieving a reliable maintenance program. To deal with such uncertainties, this study proposes a fuzzy fault tree analysis (FFTA) approach as a proactive knowledge-based technique to estimate the FP towards a convenient maintenance plan in the automotive manufacturing industry. Furthermore, in order to enhance the accuracy of the FFTA model in predicting FP, the effective decision attributes, such as the experts’ trait impacts; scales variation; and assorted membership, and the defuzzification functions were investigated. Moreover, due to the undynamic relationship between the failures of complex systems in the current FFTA model, a Bayesian network (BN) theory was employed. The results of the FFTA model revealed that the changes in various decision attributes were not statistically significant for FP variation, while the BN model, that considered conditional rules to reflect the dynamic relationship between the failures, had a greater impact on predicting the FP. Additionally, the integrated FFTA–BN model was used in the optimization model to find the optimal maintenance intervals according to the estimated FP and total expected cost. As a case study, the proposed model was implemented in a fluid filling system in an automotive assembly line. The FPs of the entire system and its three critical subsystems, such as the filling headset, hydraulic–pneumatic circuit, and the electronic circuit, were estimated as 0.206, 0.057, 0.065, and 0.129, respectively. Moreover, the optimal maintenance interval for the whole filling system considering the total expected costs was determined as 7th with USD 3286 during 5000 h of the operation time.

Modeling and Optimizing N/O-Enriched Bio-Derived Adsorbents for CO2 Capture: Machine Learning and DFT Calculation Approaches

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“…Similar to other neural networks, RBF-NN can effectively learn the relationship between input and output variables based on existing data sets. 102 As shown in Figure 3, RBF-NN consisted of parallel so-called neurons, along with one hidden layer containing the number of neurons. The independent variables are presented to the network, in which neurons accomplish proper computations on input variables in the input to the hidden layer processes.…”

Sustainable Food Production: An Intelligent Fault Diagnosis Framework for Analyzing the Risk of Critical Processes

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