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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.

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.

It is necessary to emphasize both the process and results of performance management to find the balance between quality and quantity needed to ensure the sustainable development of universities to make the best use of limited educational resources and meet the diverse needs of students. This study applies failure mode and effects analysis (FMEA) to analyze obstacles to university sustainability by constructing complete risk assessment modes and reference indicators. The neutrosophic set theory was incorporated into the FMEA to account for information uncertainty and asymmetry. A specialist team then evaluated the importance of the risk factors using neutrosophic indifference threshold-based attribute ratio analysis to determine objective weights for the risk factors. Furthermore, the neutrosophic technique for order preference by similarity to the ideal solution based on aspiration level (N-TOPSIS-AL) is employed to aggregate the total risk scores of the failure modes. Using neutrosophic sets to measure truth, falsity, and indeterminacy in the assessment significantly improve the adaptability of the fuzzy theory to real-world problems. The study results indicate that when assessing university affairs management and analyzing possible risks, priority must be given to the occurrence of risks and that the lack of educational facilities is the riskiest item in the specialist assessment. The proposed assessment model can be applied as a basis for university sustainability assessments to accelerate the development of other forward-looking approaches.

A novel method is developed for characterizing the mechanical response and failure mechanism of brittle rocks under dynamic compression-shear loading: an inclined cylinder specimen using a modified split Hopkinson pressure bar (SHPB) system. With the specimen axis inclining to the loading direction of SHPB, a shear component can be introduced into the specimen. Both static and dynamic experiments are conducted on sandstone specimens. Given carefully pulse shaping, the dynamic equilibrium of the inclined specimens can be satisfied, and thus the quasi-static data reduction is employed. The normal and shear stress–strain relationships of specimens are subsequently established. The progressive failure process of the specimen illustrated via high-speed photographs manifests a mixed failure mode accommodating both the shear-dominated failure and the localized tensile damage. The elastic and shear moduli exhibit certain loading-path dependence under quasi-static loading but loading-path insensitivity under high loading rates. Loading rate dependence is evidently demonstrated through the failure characteristics involving fragmentation, compression and shear strength and failure surfaces based on Drucker–Prager criterion. Our proposed method is convenient and reliable to study the dynamic response and failure mechanism of rocks under combined compression-shear loading.

Failure mode and effects analysis (FMEA) is one of the typical structured, systematic and proactive approaches for product or system failure analysis. A critical step in FMEA is identifying potential failure modes for product sub-systems, components, and processes, for which component-failure mode (CF) knowledge is necessarily needed as an important source of knowledge. However, this knowledge is usually acquired manually based on historical documents such as bills of material and failure analysis reports, which is a labor-intensive and time-consuming task, incurring inefficiency and plenty of mistakes. Nevertheless, few existing studies have developed an effective and intelligent approach to acquiring accurate CF knowledge automatically. To fill the gap, this paper proposes a method to construct the CF matrix automatically by mining unstructured and short quality problem texts and mapping as well as representing them as CF knowledge. Starting with mining the frequent itemsets of failure modes through Apriori algorithm, the method uses the semantic dictionary WordNet to find synonyms in the set of failure modes, based on which the standard set of failure modes is finally built. Subsequently, upon the previous work and components set, we design the component-failure mode matrix mining (CFMM) algorithm and apply it to establish the CF matrix from unstructured quality problem texts. Lastly, we examine the quality data of the seat module of an automobile company as a case study in order to validate the proposed method. The result shows that the failure mode extraction method with standardized features can extract failure modes more effectively than the FP-growth and K-means clustering methods. Meanwhile, the devised CFMM algorithm can extract more combinations of CF than the FP-growth method and build a richer CF matrix. Although different industries have distinct domain characteristics, our proposed method can be applicable not only to manufacturing but also to other fields needing FMEA to enhance product and system reliability.

Joints and fissures with similar orientation or characteristics are common in natural rocks; the inclination and density of the fissures affect the mechanical properties and failure mechanism of the rock mass. However, the strength, crack coalescence pattern, and failure mode of rock specimens containing multi-fissures have not been studied comprehensively. In this paper, combining similar material testing and discrete element numerical method (PFC2D), the peak strength and failure characteristics of rock-like materials with multi-fissures are explored. Rock-like specimens were made of cement and sand and pre-existing fissures created by inserting steel shims into cement mortar paste and removing them during curing. The peak strength of multi-fissure specimens depends on the fissure angle α (which is measured counterclockwise from horizontal) and fissure number ( N f ). Under uniaxial compressional loading, the peak strength increased with increasing α . The material strength was lowest for α  = 25°, and highest for α  = 90°. The influence of N f on the peak strength depended on α . For α  = 25° and 45°, N f had a strong effect on the peak strength, while for higher α values, especially for the 90° sample, there were no obvious changes in peak strength with different N f . Under uniaxial compression, the coalescence modes between the fissures can be classified into three categories: S-mode, T-mode, and M-mode. Moreover, the failure mode can be classified into four categories: mixed failure, shear failure, stepped path failure, and intact failure. The failure mode of the specimen depends on α and N f . The peak strength and failure modes in the numerically simulated and experimental results are in good agreement.

A large-scale shaking table test was performed to study the dynamic response of slopes parallel to geological bedding (bedding slopes) and slopes that cross-cut geological bedding (counter-bedding slopes). The test results show that the acceleration amplification coefficients increase with increasing elevation and, when the input earthquake amplitude is greater than 0.3 g, both bedding and counter-bedding slopes begin to show nonlinear dynamic response characteristics. With increasing elevation, the displacement of the bedding slope surface increases greatly. Conversely, the displacement of the counter-bedding slope surface increases first and then decreases; the slope develops a bulge at the relative elevation of 0.85. The displacement of the bedding slope surface is greater than that of the counter-bedding slope. The counter-bedding slope is more seismically stable compared with the bedding slope. Based on the Hilbert–Huang transform and marginal spectrum theories, the processes that develop dynamic damage of the bedding and counter-bedding slopes are identified. It is shown that the dynamic failure mode of the bedding slope is mainly represented by vertical tensile cracks at the rear of the slope, bedding slide of the strata along the weak intercalation, and rock collapse from the slope crest. However, the dynamic failure mode of the counter-bedding slope is mainly represented by staggered horizontal and vertical fissures, extrusion of the weak intercalation, and breakage at the slope crest.

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.

Background Failure mode and effects analysis (FMEA) is a prospective, team based, structured process used to identify system failures of high risk processes before they occur. Medication dispensing is a risky process that should be analysed for its inherent risks using FMEA. The objective of this study was to identify possible failure modes, their effects, and causes in the dispensing process of a selected tertiary care hospital using FMEA. Methods Two independent teams (Team A and Team B) of pharmacists conducted the FMEA for two months in the Department of Pharmacy of a selected teaching hospital, Colombo, Sri Lanka. Each team had five meetings of two hours each, where the dispensing process and sub processes were mapped, and possible failure modes, their effects, and causes, were identified. A score for potential severity (S), frequency (F) and detectability (D) was assigned for each failure mode. Risk Priority Numbers (RPNs) were calculated (RPN=SxFxD), and identified failure modes were prioritised. Results Team A identified 48 failure modes while Team B identified 42. Among all 90 failure modes, 69 were common to both teams. Team A prioritised 36 failure modes, while Team B prioritised 30 failure modes for corrective action using the scores. Both teams identified overcrowded dispensing counters as a cause for 57 failure modes. Redesigning of dispensing tables, dispensing labels, the dispensing and medication re-packing processes, and establishing a patient counseling unit, were the major suggestions for correction. Conclusion FMEA was successfully used to identify and prioritise possible failure modes of the dispensing process through the active involvement of pharmacists.

Most rockbursts are associated with jointed rock masses as the energy stored inside the rock is prone to abruptly release at such discontinuities. Moreover, remote blasting at working faces can trigger the brittle failure of jointed rocks at opening boundaries. In this work, a testing system combining true-triaxial compression with dynamic disturbance is employed to investigate the failure modes of cuboid jointed granite specimens. A three-dimensional tunneling-induced stress path is created in cuboid jointed granite specimens by first applying static loads to simulate the stress state encountered around an open boundary. Then, a dynamic disturbance (load) is continuously applied to the granite specimen to simulate the effect of the vibrations experienced due to remote blasting. A dynamic disturbance in the σ2 direction is found to reduce the peak strength of the specimen (σp) by approximately 5% (compared to static stress-induced failure) and promote shearing failure at the fracture surface. The major fracture surfaces form a ‘V’-shape, along with several nearby minor cracks. However, a dynamic disturbance in the σ3 direction can reduce the peak strength by 10% and accelerate the evolution of tensile failure resulting in subvertical tensile fracture. Due to the natural stiffness of the joint, the Poisson’s ratio (υ3) in the ɛ3 direction is larger than that in ɛ2 direction, highlighting the anisotropy of the deformation. The proportion of the total stored energy that is released in the jointed rock is greater than that released in intact hard rock. More strain energy is dissipated at the joint fracture. The joint provides a location at which energy can be released, which considerably increases the severity of potential rockbursts. The energy-storage limit of granite with different dip angles is also explored based on complete stress–strain curves. Post-peak granite specimens with joints dipping at 60° are found to be the most susceptible to external dynamic disturbance but release less kinetic energy. Specimens with joints that dip at 80° incorporate the maximum amount of stored energy and are not prone to rockburst when subjected to dynamic disturbance. Once a rockburst does occur in 80°-dipping jointed granite, a strong tremor will appear, however.HighlightsDisturbance in σ2 direction decreases strength by 5% and expedites shearing failure.Disturbance in σ3 direction reduces strength by 10% and accelerates tensile failure.Poisson’s ratio (ν3) in ε3-direction presents more than that (ν2) in ε2-direction.The joint dipping of granite significantly affects energy components of rockbursts.