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In order to enable failure mode and effects analysis (FMEA) to play a better quality control role in complex manufacturing products or systems, the current research status of FMEA is reviewed from failure mode identification, risk assessment, and industrial standard application. Firstly, the research status of system failure identification is summarized from the following aspects: the breakthrough point of identification, the types of identification methods, and the normalized description of failure modes. Then, sort out the research status of risk assessment from five aspects: risk factor evaluation criteria, risk assessment opinion expression, expert opinion consensus, risk opinion assessment aggregation, and sensitivity analysis, and find out research hotspots and blind spots; finally, the changes of FMEA standards in various fields are summarized and compared, and the future development trend of FMEA in the context of intelligent manufacturing is discussed.

Failure mode and effects analysis (FMEA) is known to be a proactive reliability analysis model broadly utilized to recognize and evaluate potential failure modes in various industries. The normal risk priority number (RPN) method, however, has suffers from a lot of criticisms, such as requirement of precise risk estimation, lack of scientific basis in computing RPN, and neglecting the weights of risk factors. Therefore, this paper devises a new FMEA model to evaluate and prioritize the risk of failure modes by integrating probabilistic linguistic term sets and TODIM (an acronym in Portuguese for interactive multi-criteria decision making) method. The probabilistic linguistic term sets are utilized to handle the intrinsic ambiguity existed in the risk assessments of FMEA team members, whilst an extended TODIM method is employed for determining the priority ranking of the individuated failure modes. Further, based on the technique for order of preference by similarity to ideal solution (TOPSIS), an objective weighting method is presented to derive the relative weights of risk factors. Finally, two illustrative examples are implemented and comparisons with other existing methods are performed to demonstrate the rationality and superiority of our proposed FMEA model.

The effective identification of critical failure modes of individual equipment components or processes and the development of plans for improvement are crucial for the manufacturing industry. Recently, the failure modes and effects analysis (FMEA) approach based on multiple criteria decision making (MCDM) has been utilized effectively for the assessment of primary failure modes and risks. However, the ranking results of failure modes produced by different MCDM methods might be different. This study proposes an integrated risk assessment model where several techniques are combined to produce an FMEA model for the generation of comprehensive failure mode ranking. First, the anticipated costs and environmental protection indicators are included in the FMEA model to enhance the comprehensiveness of assessment. Then, an influential network relationship map of risk factors is obtained by using the decision-making trial and evaluation laboratory (DEMATEL) technique to assist in identifying the critical factors. Finally, the ranking of the failure modes is identified using the four integrated MCDM methods, based on the technique for order preference by similarity to ideal solution (TOPSIS) concept. In addition, data from a machine tool manufacturing company survey are applied to demonstrate the effectiveness and robustness of the proposed model.

For complex structures like aeroengine turbine rotor, its reliability performance is jointly determined by multiple correlated failure modes. Probabilistic evaluation is an effective way to reveal the output response traits and quantify the structural reliability performance. However, for the requirement of evaluating the multivariate output responses and considering the correlation relationships, the multi-failure correlated probabilistic evaluation often shows the complex characteristics of high-nonlinearity and strong-coupling, leading to the conventional evaluation methods are hard to meet the requirements of accuracy and efficiency. To address this problem, a vectorial surrogate model (VSM) method is proposed by fusing the linkage sampling technique and model updating strategy. First, the linkage sampling technique is developed to build the vectorial sample set and the initial VSM by collaboratively extracting multidimensional input variables and multivariate output responses; moreover, the model updating strategy (MU) is presented to find the optimal undetermined parameters and construct the final VSM by addressing the issues of premature convergence and over-fitting problems. Regarding a typical high-pressure turbine rotor with multiple correlated failure modes (i.e., deformation failure, stress failure, strain failure) as engineering application case, the response distributions, reliability degree, sensitivity degree, correlation relationships for each/all failure modes of turbine rotor are obtained by the proposed method. Through the comparison of methods (direct Monte Carlo simulation, polynomial response surface, random forest, support vector regression, artificial neural network, VSM-I (without MU strategy), VSM-II (with MU strategy)), it is verified that the proposed VSM method can efficiently and accurately accomplish the multi-failure correlated probabilistic evaluation.

Multiple failure modes and temporal parameters are usually inherent in the complicated products’ performance, which bring new challenges for conducting time-variant reliability analysis and design. This paper proposes a novel time-variant reliability analysis method for multiple failure modes and temporal parameters based on the combination of the extreme value moment method and improved maximum entropy method. Firstly, a scaling function is established to reduce the integration error of the original maximum entropy method. The improved maximum entropy method can transform an infinite interval to a limited interval for a range of integration. The extreme value moments are then obtained by employing the sparse grid technique and the extreme value theory. Finally, the probability density function (PDF) of responses can be obtained by the combination of the extreme value moment method and the improved maximum entropy method and so the time-variant reliability can be estimated. The effectiveness of the proposed method is illustrated with two examples.

PurposeThis study aims to propose a facilitating methodology for the application of Fuzzy FMEA (Failure Mode and Effect Analysis), comparing the traditional approach with fuzzy variations, supported by a case application in the aeronautical sector.Design/methodology/approachBased on experts' opinions in risk analysis within the aeronautical sector, rules governing the relationship between severity, occurrence, detection and risk factor were defined. This served as input for developing a fuzzyfied FMEA tool using the Matlab Fuzzy Logic Toolbox. The tool was applied to the sealing process in a company within the aeronautical sector, using triangular and trapezoidal membership functions, and the results were compared with the traditional FMEA approach.FindingsThe results of the comparative application of traditional FMEA and fuzzyfied FMEA using triangular and trapezoidal functions have yielded valuable insights into risk analysis. The findings indicated that fuzzyfied FMEA maintained coherence with the traditional analysis in identifying higher-risk effects, aligning with the prioritization of critical failure modes. Additionally, fuzzyfied FMEA allowed for a more refined prioritization by accounting for variations in each variable through fuzzy rules, thereby improving the accuracy of risk analysis and providing a more realistic representation of potential hazards. The application of the developed fuzzyfied FMEA approach showed promise in enhancing risk assessment in the aeronautical sector by considering uncertainties and offering a more detailed and context-specific analysis compared to conventional FMEA.Practical implicationsThis study emphasizes the potential of fuzzyfied FMEA in enhancing risk assessment by accurately identifying critical failure modes and providing a more realistic representation of potential hazards. The application case reveals that the proposed tool can be integrated with expert knowledge to improve decision-making processes and risk mitigation strategies within the aeronautical industry. Due to its straightforward approach, this facilitating methodology could also prove beneficial in other industrial sectors.Originality/valueThis paper presents the development and application of a facilitating methodology for implementing Fuzzy FMEA, comparing it with the traditional approach and incorporating variations using triangular and trapezoidal functions. This proposed methodology uses the Toolbox Fuzzy Logic of Matlab to create a fuzzyfied FMEA tool, enabling a more nuanced and context-specific risk analysis by considering uncertainties.

Due to multiple implicit limit state functions needed to be surrogated, adaptive Kriging model for system reliability analysis with multiple failure modes meets a big challenge in accuracy and efficiency. In order to improve the accuracy of adaptive Kriging meta-model in system reliability analysis, this paper mainly proposes an improved AK-SYS by using a refined U learning function. The improved AK-SYS updates the Kriging meta-model from the most easily identifiable failure mode among the multiple failure modes, and this strategy can avoid identifying the minimum mode or the maximum mode by the initial and the in-process Kriging meta-models and eliminate the corresponding inaccuracy propagating to the final result. By analyzing three case studies, the effectiveness and the accuracy of the proposed refined U learning function are verified.

Failure mode and effect analysis (FMEA) is a prospective risk assessment tool used to identify, assess and eliminate potential failure modes in various industries to improve security and reliability. However, the conventional risk priority number (RPN) method has been widely criticized for the deficiencies in risk factor weights, calculation of RPN, evaluation of failure modes, etc. In this paper, we present a novel approach for FMEA based on interval-valued intuitionistic fuzzy sets (IVIFSs) and MULTIMOORA method to handle the uncertainty and vagueness from FMEA team members’ subjective assessments and to get a more accurate ranking of failure modes identified in FMEA. In this proposed model, interval-valued intuitionistic fuzzy (IVIF) continuous weighted entropy is applied for risk factor weighting and the IVIF-MULTIMOORA method is used to determine the risk priority order of failure modes. Finally, an illustrative case is provided to demonstrate the effectiveness and practicality of the proposed FMEA and a comparison analysis with other relevant methods is conducted to show its merits.

Despite being strong under compression, concrete is rather weak when subjected to tensile stress. Concrete has been reinforced with a variety of materials over time in order to resist tensile stresses. Among various types of fibers, polypropylene fiber, which is available in a range of sizes, is being used to strengthen concrete. The fiber also increases the concrete’s toughness, durability, and low permeability. Polypropylene fibers may be utilized in place of conventional reinforcement, according to a number of researchers. The aim of this study is to collect information from already carried out research on polypropylene fibers. Important characteristics of concrete, such as workability, compressive, tensile, and flexural strength, are reviewed. The review also explores cracking behavior and failure modes of polypropylene fiber reinforced concrete. Furthermore, durability aspects, such as water absorption, porosity, dry shrinkage, and microstructure study (scan electronic microscopy), were also reviewed. Results indicate that polypropylene fiber improved the mechanical strength and durability of concrete (particularly tensile capacity) but decreased the flowability of concrete. The optimum dose is important, as a higher dose adversely affects strength and durability due to a lack of flowability. Scanning electronic microscopy results indicate that the polypropylene fibers restrict the propagation of cracks, which improves the strength and durability of concrete. The review also indicates that shrinkage cracks are considerably reduced with the addition of polypropylene fibers. Finally, the review also provides future research guidelines for upcoming generations to further improve the performance of polypropylene fibers that reinforce concrete.

Failure mode and effect analysis (FMEA) is a risk analysis tool widely used in the manufacturing industry. However, traditional FMEA has limitations such as the inability to deal with uncertain failure data including subjective evaluations of experts, the absence of weight values of risk parameters, and not considering the conditionality between failure events. In this paper, we propose a holistic FMEA to overcome these limitations. The proposed approach uses the fuzzy best–worst (FBWM) method in weighting three risk parameters of FMEA, which are severity ( S ), occurrence ( O ), and detection ( D ), and to find the preference values of the failure modes according to parameters S and D . On the other side, it uses the fuzzy Bayesian network (FBN) to determine occurrence probabilities of the failure modes. Experts use a procedure using linguistic variables whose corresponding values are expressed in trapezoidal fuzzy numbers, and determine the preference values of the failure modes according to parameter O in the constructed BN. Thus, the FBN including expert judgments and fuzzy set theory addresses uncertainty in failure data and includes a robust probabilistic risk analysis logic to capture the dependence between failure events. As a demonstration of the approach, a case study was conducted in an industrial kitchen equipment manufacturing facility. The results of the approach have also been compared with existed methods demonstrating its robustness.