Failure mode and effect analysis with extended grey relational analysis method in cloud setting

The D numbers risk ranking based method by considering subjective weights and objective weights with incomplete linguistic information

Risk prediction, assessment, and control are key parts of the successful operation and sustainable development of any enterprise. During the process of product failure risk assessment, evaluated risk factors belong to the group of multiple-criteria decision-making (MCDM) problems, including severity, occurrence, and detection when failure occurs. However, the traditional risk ranking method does not consider the subjective and objective weights of the assessment factors, and during risk prediction, assessment, and control, some unknown information in many practical situations is included. These reasons may cause the risk assessment results to be biased. In order to effectively deal with the problem of risk assessment, this paper proposes a D numbers risk ranking method by considering subjective and objective weights between assessment factors under incomplete linguistic information. An illustrative example of screening unit failure risk assessment is used to explain and prove the rationality and correctness of the proposed method. Some risk ranking methods are compared with the proposed D numbers risk ranking method, and the simulation results present that the proposed ranking method handles the issue of incomplete information and provides more reasonable risk ranking results.

Failure Mode and Effect Analysis Based on Probabilistic Linguistic Preference Relations and Gained and Lost Dominance Score Method

Failure mode and effect analysis (FMEA) is a widely used reliability management technology to evaluate the risk of potential failures in a system, product, or service. Nevertheless, the normal risk priority number (RPN) method has been extensively criticized for many deficiencies in practical applications. To overcome the drawbacks of traditional FMEA, plenty of methods have been suggested in previous studies. But majority of them evaluated the risk factors of each failure mode directly and cannot take group and individual risk attitudes into account. In this article, we put forward a new FMEA approach integrating probabilistic linguistic preference relations (PLPRs) and gained and lost dominance score (GLDS) method. The PLPRs are adopted to describe the risk evaluations of experts by pairwise comparison of failure modes. An extended GLDS method is introduced to derive the risk ranking of failure modes considering both group and individual risk attitudes. Moreover, a two-step optimization model is proposed to determine the weights of risk factors when their weighing information is unknown. Finally, a load-haul-dumper machine risk analysis case is presented to demonstrate the proposed FMEA. It is shown that the approach being proposed in this study provides a practical and effective way for risk evaluation in FMEA.

Application of Grey Relational Analysis to Predict Dementia Tendency by Cognitive Function, Sleep Disturbances, and Health Conditions of Diabetic Patients

Background: The number of elderly diabetic patients has been increasing recently, and these patients have a higher morbidity of dementia than those without diabetes. Diabetes is associated with an increased risk for the development of dementia in elderly individuals, which is a serious health problem. Objectives: The primary aim was to examine whether diabetes is a risk factor for dementia among elderly individuals. The secondary aim was to apply grey theory to integrate the results and how they relate to cognitive impairments in elderly diabetic patients and to predict which participants are at high risk of developing dementia. Methods: Two hundred and twenty patients aged 50 years or older who were diagnosed with diabetes mellitus were recruited. Information on demographics, disease characteristics, activities of daily living, Mini Mental State Examination, sleep quality, depressive symptoms, and health-related quality of life was collected via questionnaires. The grey relational analysis approach was applied to evaluate the relationship between the results and health outcomes. Results: A total of 13.6% of participants had cognitive disturbances, of whom 1.4% had severe cognitive dysfunction. However, with regard to sleep disorders, 56.4% had sleep disturbances of varying degrees from light to severe. Further investigation is needed to address this problem. A higher prevalence of sleep disturbances among diabetic patients translates to a higher degree of depressive symptoms and a worse physical and mental health-related quality of life. Furthermore, based on the grey relational analysis, the grey relation coefficient varies from 0.6217~0.7540. Among the subjects, Participant 101 had the highest value, suggesting a need for immediate medical care. In this study, we observed that 20% of the total participants, for whom the grey relation coefficient was 0.6730, needed further and immediate medical care.

Fault Handling in Industry 4.0: Definition, Process and Applications

The increase of productivity and decrease of production loss is an important goal for modern industry to stay economically competitive. For that, efficient fault management and quick amendment of faults in production lines are needed. The prioritization of faults accelerates the fault amendment process but depends on preceding fault detection and classification. Data-driven methods can support fault management. The increasing usage of sensors to monitor machine health status in production lines leads to large amounts of data and high complexity. Machine Learning methods exploit this data to support fault management. This paper reviews literature that presents methods for several steps of fault management and provides an overview of requirements for fault handling and methods for fault detection, fault classification, and fault prioritization, as well as their prerequisites. The paper shows that fault prioritization lacks research about available learning methods and underlines that expert opinions are needed.

A hybrid model integrating FMEA and HFACS to assess the risk of inter-city bus accidents

The incidence of inter-city bus accidents receives a lot of attention from the public because they often cause heavy casualties. The Human Factors Analysis and Classification System (HFACS) is the prevailing tool used for traffic accident risk assessment. However, it has several shortcomings, for example: (1) it can only identify the potential failure modes, but lacks the capability for quantitative risk assessment; (2) it neglects the severity, occurrence and detection of different failure modes; (3) it is unable to identify the degree of risk and priorities of the failure modes. This study proposes a novel hybrid model to overcome these problems. First, the HFACS is applied to enumerate the failure modes of inter-city bus operation. Second, the Z-number-based best–worst method is used to determine the weights of the risk factors based on the failure mode and effects analysis results. Then, a Z-number-based weighted aggregated sum product Assessment is utilized to calculate the degree of risk of the failure modes and the priorities for improvement. The results of this study determine the top three ranking failure modes, which are personal readiness from pre-conditions for unsafe behavior, human resources from organizational influence, and driver decision-making error from unsafe behavior. Finally, data for inter-city buses in Taiwan in a case study to illustrate the usefulness and effectiveness of the proposed model. In addition, some management implications are provided.

New FMEA Risks Ranking Approach Utilizing Four Fuzzy Logic Systems

Process equipment and plant maintenance problems are complex in the oil refinery business, since effective maintenance needs to ensure the reliability and availability of the plant. Failure Mode and Effects Analysis (FMEA) is a risk assessment tool that aims to determine possible failure modes, and to reduce the ratio of unknown failure modes, by identifying business-critical systems and the risks of their failures. For the identified failure modes, FMEA determines risk mitigation action(s). The goal is to prevent failure and keep assets and plants running at peak performance by providing fully integrated operations, maintenance, turnarounds, modifications, and asset integrity solutions, during all phases of the asset life cycle. This research was based on FMEA use/application in refineries’ units, and proposes the new fuzzy FMEA risk quantification approach method: “four fuzzy logic system”. The model included a pre-assessment, by sets of fuzzy logic systems, that examined the input parameters that affected the variables of severity, occurrence, and detectability. The proposed model prioritized risks better and addressed the drawbacks of the conventional FMEA method.

Fuzzy multi-criteria approach for criticality assessment and optimization of decision making

Failure mode, effects, and criticality analysis (FMECA) is a proactive quality tool that allows the identification and prevention of the potential failure modes of a process or product. In a conventional FMECA, for each failure mode, three risk parameters, namely frequency, non-detection, and severity are evaluated and a risk priority number (RPN) is calculated by multiplying these parameters to assess one signal criticality. However, in many cases, it suffers from some shortcomings regarding the decision-making and the situation where the information provided is ambiguous or uncertain. This paper describes a new fuzzy multi-criticality approach for improving the use of FMECA by treating FMECA as a fuzzy multi-criteria optimization model. The new approach bases on replacing the calculation of a single criticality with a fuzzy inference system for improving the criticality evaluations which offers five partial criticalities that efficiently and separately calculate the impact of a failure on the environment, personnel, production, equipment, and management. In addition, an analytical hierarchy method (AHP) is used to calculate the priorities weights for each partial criticality and construct a criticality matrix in order to improve the relevance of decision-making. Furthermore, a real case of LPG storage system for ZCINA Hassi Messaoud in Algeria is provided to illustrate the practical implementation of the suggested approach and extremely shows the pertinence of the suggested fuzzy model as decision-making tools in preventing industrial risks with providing encouraging results regarding the criticality estimation and improve decision-making by prioritizing “preventive –corrective actions” and determine the efficient action for each partial criticality to control the risk effectively.

Integrating FMEA and the Kano Model to Improve the Service Quality of Logistics Centers

This study uses the logistics center of a large organic retail store in Taiwan to analyze service blueprint and workflow, identifying the potential points of failure and thus serving as a basis for quality improvement. The failure mode and effect analysis (FMEA) model is an effective problem prevention methodology that can easily interface with many engineering and reliability methods. The utilized method integrates the failure mode and effect analysis (FMEA) and the Kano model to explore the possible occurrence of failures in the internal workflow and services of the studied logistics center. A two-stage survey was conducted. In the first stage, an investigation was conducted by 20 logistics experts on the FMEA’s key service failures. In the second stage, a questionnaire was filled out by 220 store staff to summarize the logistics service quality factors found in the Kano model. The results show that the degree of attention and satisfaction in the priority improvement items when there were service failures vary among the opinions of different internal employees and customers. The participants jointly believed that the items that need improvement are “Damaged incoming goods” and “A shortfall in the quantities of delivered goods”.

Risk Assessment of Veterinary Drug Residues in Meat Products

With the improvement of the global food safety regulatory system, there is an increasing importance for food safety risk assessment. Veterinary drugs are widely used in poultry and livestock products. The abuse of veterinary drugs seriously threatens human health. This article explains the necessity of risk assessment for veterinary drug residues in meat products, describes the principles and functions of risk assessment, then summarizes the risk assessment process of veterinary drug residues, and then outlines the qualitative and quantitative risk assessment methods used in this field. We propose the establishment of a new meat product safety supervision model with a view to improve the current meat product safety supervision system.

A Failure Mode Assessment Model Based on Neutrosophic Logic for Switched-Mode Power Supply Risk Analysis

Reducing the potential risks in the manufacturing process to improve the reliability of the switched-mode power supply (SMPS) is a critical issue for the users’ safety. This paper proposes a novel failure mode and effects analysis (FMEA) model based on hybrid multiple criteria decision-making (MCDM), which adopts neutrosophic set theory into the proposed model. A developed neutrosophic Best Worst method (NBWM) is used to evaluate the weights of risk factors and determine their importance. Secondly, the neutrosophic Weight Aggregated Sum Product Assessments (NWASPAS) method is utilized to calculate the Risk Priority Number (RPN) of the failure modes. The proposed model improves the shortcomings of traditional FMEA and improves the practical applicability and effectiveness of the Best Worst method (BWM) and Weight Aggregated Sum Product Assessments (WASPAS) methods. In addition, this study uses neutrosophic logic to reflect the true judgments of experts in the assessment, which considers authenticity, deviation, and uncertainty to obtain more reliable information. Finally, an empirical case study from an SMPS company headquartered in Taiwan demonstrates the effectiveness and robustness of the proposed model. In addition, by comparing with two other FMEA models, the results show that the proposed model can more clearly reflect the true and effective risks in the assessment. The results can effectively help power supply manufacturers to assess risk factors and determine key failure modes.

Evaluating Surgical Risk Using FMEA and MULTIMOORA Methods under a Single-Valued Trapezoidal Neutrosophic Environment

Background

Human errors during operations may seriously threaten patient recovery and safety and affect the doctor–patient relationship. Therefore, risk evaluation of the surgical process is critical. Risk evaluation by failure mode and effect analysis (FMEA) is a prospective technology that can identify and evaluate potential failure modes in the surgical process to ensure surgical quality and patient safety. In this study, a hybrid surgical risk–evaluation model was proposed using FMEA and multiobjective optimization on the basis of ratio analysis plus full multiplicative form (MULTIMOORA) method under a single-valued trapezoidal neutrosophic environment. This work aimed to determine the most critical risk points during the surgical process and analyze corresponding solutions.

Methods

A team for FMEA was established from domain experts from different departments in a hospital in Hunan Province. Single-valued trapezoidal neutrosophic numbers (SVTNNs) were used to evaluate potential risk factors in the surgical process. Cmprehensive weights combining subjective and objective weights were determined by the best–worst method and entropy method to differentiate the importance of risk factors. The SVTNN–MULTIMOORA method was utilized to calculate the risk-priority order of failure modes in a surgical process.

Results

The hybrid FMEA model under the SVTNN–MULTIMOORA method was used to calculate the ranking of severity of 21 failure modes in the surgical process. An unclear diagnosis is the most critical failure in the surgical process of a hospital in Hunan Province.

Conclusion

The proposed model can identify and evaluate the most critical potential failure modes of the surgical process effectively. In addition, such a model can help hospitals to reduce surgical risk and improve the safety of surgery.

Deng Entropy Weighted Risk Priority Number Model for Failure Mode and Effects Analysis

Failure mode and effects analysis (FMEA), as a commonly used risk management method, has been extensively applied to the engineering domain. A vital parameter in FMEA is the risk priority number (RPN), which is the product of occurrence (O), severity (S), and detection (D) of a failure mode. To deal with the uncertainty in the assessments given by domain experts, a novel Deng entropy weighted risk priority number (DEWRPN) for FMEA is proposed in the framework of Dempster–Shafer evidence theory (DST). DEWRPN takes into consideration the relative importance in both risk factors and FMEA experts. The uncertain degree of objective assessments coming from experts are measured by the Deng entropy. An expert’s weight is comprised of the three risk factors’ weights obtained independently from expert’s assessments. In DEWRPN, the strategy of assigning weight for each expert is flexible and compatible to the real decision-making situation. The entropy-based relative weight symbolizes the relative importance. In detail, the higher the uncertain degree of a risk factor from an expert is, the lower the weight of the corresponding risk factor will be and vice versa. We utilize Deng entropy to construct the exponential weight of each risk factor as well as an expert’s relative importance on an FMEA item in a state-of-the-art way. A case study is adopted to verify the practicability and effectiveness of the proposed model.

An Extension of the Failure Mode and Effect Analysis with Hesitant Fuzzy Sets to Assess the Occupational Hazards in the Construction Industry

The construction industry is considered as one of the most dangerous industries in terms of occupational safety and has a high rate of occupational incidents and risks compared to other industries. Given the importance of identifying and assessing the occupational hazards in this industry, researchers have conducted numerous studies using statistical methods, multi-criteria decision-making methods, expert-based judgments, and so on. Although, these researchers have used linguistic variables, fuzzy sets and interval-valued intuitionistic fuzzy sets to overcome challenges such as uncertainty and ambiguity in the risk assessment conducted by experts; the previous models lack in efficiency if the experts are hesitant in their assessment. This leads to the inability to assign a specific membership degree to any risk. Therefore, in this research, it is tried to provide an improved approach to the Failure Mode and Effects Analysis (FMEA) method using an Multi-Criteria Decision-Making (MCDM) method based on the hesitant fuzzy set, which can effectively cope with the hesitance of the experts in the evaluation. Also, Stepwise Weight Assessment Ratio Analysis (SWARA) method is applied for risk factor weighing in the proposed approach. This model is applied to a construction industry case study to solve a realistic occupational risk assessment. Moreover, a comparison is made between the results of this model and those obtained by the conventional FMEA and some other aggregation operators. The results indicate that the newly developed approach is useful and flexible to address complex FMEA problems and can generate logical and reliable priority rankings for failure modes.

Developing a two-phase QFD for improving FMEA: an integrative approach

Purpose

The purpose of this paper is to propose an integrative approach for improving failure modes and effects analysis (FMEA).

Design/methodology/approach

An extensive literature review on FMEA has been performed. Then, an integrative approach has been proposed based on literature review. The proposed approach is an integration of FMEA and quality function deployment (QFD). The proposed approach includes a two-phase QFD. In the first phase, failure modes are prioritized based on failure effects and in the second phase, failure causes are prioritized based on failure modes. The proposed approach has been examined in a case example at the blast furnace operation of a steel-manufacturing company.

Findings

Results of the case example indicated that stove shell crack in hot blast blower, pump failure in cooling water supply pump and bleeder valves failed to operate are the first three important failure modes. In addition, fire and explosion are the most important failure effects. Also, improper maintenance, over pressure and excess temperature are the most important failure causes. Findings also indicated that the proposed approach with the consideration of interrelationships among failure effects, failure mode and failure causes can influence and adjust risk priority number (RPN) in FMEA.

Research limitations/implications

As manufacturing departments are mostly dealing with failure effects and modes of machinery and maintenance departments are mostly dealing with causes of failures, the proposed model can support better coordination and integration between the two departments. Such support seems to be more important in firms with continuous production lines wherein line interruption influences response to customers more seriously. A wide range of future study opportunities indicates the attractiveness and contribution of the subject to the knowledge of FMEA.

Originality/value

Although the literature indicates that in most of studies the outcomes of QFD were entered into FMEA and in some studies the RPN of FMEA was entered into QFD as importance rating, the proposed approach is a true type of the so-called “integration of FMEA and QFD” because the three main elements of FMEA formed the structure of QFD. In other words, the proposed approach can be considered as an innovation in the FMEA structure, not as a data provider prior to it or a data receiver after it.

Robot Evaluation and Selection with Entropy-Based Combination Weighting and Cloud TODIM Approach

Nowadays robots have been commonly adopted in various manufacturing industries to improve product quality and productivity. The selection of the best robot to suit a specific production setting is a difficult decision making task for manufacturers because of the increase in complexity and number of robot systems. In this paper, we explore two key issues of robot evaluation and selection: the representation of decision makers’ diversified assessments and the determination of the ranking of available robots. Specifically, a decision support model which utilizes cloud model and TODIM (an acronym in Portuguese of interactive and multiple criteria decision making) method is developed for the purpose of handling robot selection problems with hesitant linguistic information. Besides, we use an entropy-based combination weighting technique to estimate the weights of evaluation criteria. Finally, we illustrate the proposed cloud TODIM approach with a robot selection example for an automobile manufacturer, and further validate its effectiveness and benefits via a comparative analysis. The results show that the proposed robot selection model has some unique advantages, which is more realistic and flexible for robot selection under a complex and uncertain environment.