SRMES: an expert system for performance analysis of small refrigerating machines

SRMES is a rule-based expert system designed for use in the refrigeration industry as an aid in diagnosing the most frequent malfunctions in small refrigerating machines of either the air- or water-cooled condenser type. Suitably expanded, it may cover other more complex types of refrigerating machine. The system could also be suitable for education and training. In the version described here, SRMES is intended to demonstrate that the use of expert system tools in the refrigeration industry may lead to higher efficiency and better performance. Although the version described in the paper has been optimized for off-line performance, its extension to on-line application may be justified by relevant experience gained through its practical operation. Particular attention is devoted in the paper to the development of the decision tree. It is shown that with computer hardware of limited capacity knowledge-based rules can be used to reach specific diagnoses of the performance of small refrigerating machines.     

A linguistic risk prioritization approach for failure mode and effects analysis: A case study of medical product development

Failure mode and effects analysis (FMEA) is a widely used risk management technique for identifying the potential failures from a system, design, or process and determining the most serious ones for risk reduction. Nonetheless, the traditional FMEA method has been criticized for having many deficiencies. Further, in the real world, FMEA team members are usually bounded rationality, and thus, their psychological behaviors should be considered. In response, this study presents a novel risk priority model for FMEA by using interval two‐tuple linguistic variables and an integrated multicriteria decision‐making (MCDM) method. The interval two‐tuple linguistic variables are used to capture FMEA team members' diverse assessments on the risk of failure modes and the weights of risk factors. An integrated MCDM method based on regret theory and TODIM (an acronym in Portuguese for interactive MCDM) is developed to prioritize failure modes taking experts' psychological behaviors into account. Finally, an illustrative example regarding medical product development is included to verify the feasibility and effectiveness of the proposed FMEA. By comparing with other existing methods, the proposed linguistic FMEA approach is shown to be more advantageous in ranking failure modes under the uncertain and complex environment.     

Improved FMEA based on IVF and fuzzy VIKOR method: A case study of workpiece box system of CNC gear milling machine

Failure mode and effect analysis (FMEA) is a tool used to define, identify, and prevent known or unknown potential risks. An improved FMEA based on interval triangular fuzzy numbers (IVF) and fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method is proposed in this study to solve problems of expression and processing of uncertain information, weights of risk factors, and ranking of failure modes in traditional FMEA. Linguistic variables are used to evaluate failure modes level and relative importance of risk factors and are expressed via interval-valued triangular fuzzy number. Determining the subjective weights of risk factors using fuzzy AHP, calculating the objective weights of risk factors using the extended VIKOR method, and obtaining the comprehensive weights of risk factors via ICWGT are proposed for solving the weight problem of risk factors. Finally, the fuzzy VIKOR method is used to rank risk priority of failure modes. The proposed method is used to evaluate workpiece box system of CNC gear milling machine and the results are compared with the findings of other methods to verify effectiveness and rationality of the proposed method.     

An improved failure mode and effects analysis method based on uncertainty measure in the evidence theory

mode and effects analysis (FMEA) is an effective tool to assess the risk of a system or process under uncertain environment. However, how to handle the uncertainty in the subjective assessment is an open issue. In this paper, a novel method to deal with the uncertainty coming from subjective assessments of FMEA experts is proposed in the framework of Dempster–Shafer evidence theory. First, the uncertain degree of the assessment is measured by the ambiguity measure. Then, the uncertainty is transformed to the reliability of each FMEA expert and the relative importance of each risk factor. After that, the assessments from FMEA team will be fused with a discounting-based combination rule to address the potential conflict. Moreover, to avoid the situation that different risk priorities of failure modes may have the same ranking based on classical risk priority number method, the gray relational projection method (GRPM) is adopted for ranking risk priorities of failure modes. Finally, an application of the improved FMEA model in sheet steel production process verifies the reliability and validity of the proposed method.     

Risk Failure Deployment: A novel integrated tool to prioritize corrective actions in failure mode and effects analysis

In this paper, a novel integrated tool for failure mode and effects analysis (FMEA), opportunely named Risk Failure Deployment (RFD), which is able to evaluate the most critical failure modes and provide analyst with a practical and step-by-step guidance by selecting the most effective corrective actions for removal/mitigation process of root causes, is presented. Thanks to the modification of the framework of the Manufacturing cost deployment (MCD) and to its well-structured use of matrices, the novel RFD is able both to handle the dependencies and interactions between different and numerous failures and to evaluate the most critical ones on the basis of the risk priority number (RPN). Thereafter, the logical relationship between root causes and failure modes is assessed. Successively, the prioritization of corrective actions that are the most suitable for root causes is executed using not only the RPN but also other criteria, such as the economic aspect and the ease of implementation, that are unavoidable to execute a rational and effective selection of improvement activities. The effectiveness and usefulness in practice of the original tool for the prioritization of corrective actions to mitigate the risks due to failure modes collected during FMEA are presented in a case study.     

An improving approach for failure mode and effect analysis under uncertainty environment: A case study of critical function component

Failure mode and effect analysis (FMEA) is a powerful risk discerning technique for identifying, evaluating, and reducing possible failures of products or processes. However, the classical FMEA has been criticized for inherent limitations, such as equal weights of risk elements and lack of capability in handling inaccurate information. Although fuzzy-based modified FMEA methods are frequently utilized to handle vagueness of experts' judgments, they still have some drawbacks, for example, requiring extra assumptions, neglecting experts' bounded rationality and psychological effects, lacking consideration of randomness, and only considering three classical risk elements among most of them. Therefore, this study develops an extended risk assessment method to enhance the performance of FMEA, which integrates the superiority of rough number theory in handling subjective and inaccurate information and the advantage of cloud model theory in reflecting the randomness of qualitative evaluations. Moreover, two synthetic weighting methods are developed to determine the weights of risk elements and handle the experts' individual effects, respectively, which consider both subjective and objective aspects. In addition, maintenance is added into the classical risk elements, and then a hierarchical structure containing four risk dimensions is built to evaluate failures' risk levels comprehensively. Finally, an application case to demonstrate the effectiveness of the developed FMEA model is presented.     

Risk evaluation in failure mode and effects analysis of aircraft turbine rotor blades using Dempster–Shafer evidence theory under uncertainty

Rotor blades are the major components of an aircraft turbine. Their reliability seriously affects the overall aircraft turbine security. Failure mode and effects analysis (FMEA), especially, the risk priority order of failure modes, is essential in the design process. The risk priority number (RPN) has been extensively used to determine the risk priority order of failure modes. When multiple experts give different risk evaluations to one failure mode, which may be imprecise and uncertain, the traditional RPN is not a sufficient tool for risk evaluation. In this paper, the modified Dempster–Shafer (D–S) is adopted to aggregate the different evaluation information by considering multiple experts’ evaluation opinions, failure modes and three risk factors respectively. A simplified discernment frame is proposed according to the practical application. Moreover, the mean value of the new RPN is used to determine the risk priority order of multiple failure modes. Finally, this method is used to deal with the risk priority evaluation of the failure modes of rotor blades of an aircraft turbine under multiple sources of different and uncertain evaluation information. The consequence of this method is rational and efficient.     

The Luenberger indicator and directions of measurement: a bottoms-up approach with an empirical illustration to German savings banks

The Luenberger productivity indicator applies directional distance functions which allow to specifying in what direction (i.e. direction of measurement) the operating units will be evaluated. In the presence of a change in the direction of measurement, the standard components of the existing Luenberger productivity indicator may provide values which are not compatible with reality. In order to eliminate this pitfall, the so-called bottoms-up approach is used to revisit the definition of the indicator and its components. We start with a list of selected sources of productivity change, namely efficiency change, technical change and direction change, then examine the best possible way of measuring each of the sources and combine them to derive a new measure of productivity change. The proposed indicator will be illustrated by means of an empirical application to a panel of 417 German savings banks over the time period 2006–2012. The example explains how the proposed approach is able to properly measure efficiency change, technical change and direction change. The results also provide conclusive evidence about the effect of the change in direction of measurement on the results of the productivity over time in a centralised management scenario.     

Reliability and economic analysis and improvement of complex mechanical products based on DEA: An empirical case study of hydraulic products

Complex mechanical products involve many specialized fields such as machinery, electronics, hydraulics, and computers, so their initial cost is usually higher. In addition, regular and irregular maintenance are also needed to extend their service life, so that the cost of the product in its life cycle will be increased. Therefore, for the manufacturing enterprises of complex mechanical products, it is very important to achieve the best matching between reliability and economy. In order to survive in the fierce market competition, it is necessary to compare with other similar products in the market, so as to clearly understand the level of their products, find out the gap, and improve it. However, the existing product reliability assessment methods based on probability and statistics theory cannot provide such help for them to solve this problem. Therefore, this paper puts forward a method to evaluate and improve complex mechanical products by using a data envelopment analysis (DEA) model considering the reliability and economy of product comprehensively. Through the relative effectiveness of DEA, the same type of products on the market can be compared, providing specific method for enterprises to evaluate and improve their own product level to achieve the best matching of economy and reliability. This paper introduces the principle and steps of this method and takes the hydraulic component manufacturing enterprise as an example to carry out practical research, which verifies the feasibility and effectiveness of this method.     

The development of a decision support framework for a quantitative risk assessment in multimodal green logistics: an empirical study

The objective of this research was to develop a decision support framework (DSF) to assess quantitative risk in multimodal green logistics. This risk assessment is the combination of a number of models, the failure mode and effects analysis, the risk contour plot, the quantitative risk assessment, the analytic hierarchy process and the data envelopment analysis which can support a user to perform risk assessment in various decisions. The contribution of this research is that the risk assessment model can generate an optimal green logistics route in accordance with weight from the user. The highlight of this DSF is that the quantitative assessment model can reduce bias on risk assessment of logistics route. An in-depth case study, recommendations, limitations and further research are also provided.     

The development of a naturalistic data collection system to perform critical incident analysis: an investigation of safety and fatigue issues in long-haul trucking

Traditionally, both epidemiological and empirical methods have been used to assess driving safety. This paper describes an alternative, hybrid, naturalistic approach to data collection that shares advantages with each traditional approach. Though this naturalistic approach draws on elements of several safety techniques that have been developed in the past, including the Hazard Analysis Technique, instrumented vehicle studies, and fleet studies of driving safety interventions, it has a number of unique elements. Sophisticated instrumented vehicles collected over 400,000 km of commercial vehicle data to address the long-haul trucking application described in this paper. The development of this data collection and analysis method and data collection instrumentation has resulted in a set of valuable tools to advance the current state-of-the-practice in driving safety assessment. An application of this unique approach to a study of long-haul truck driver performance, behavior, and fatigue is described herein.     

Integrated analysis of the performance of TQM tools and techniques: a case study in the Taiwanese motor industry

Most studies of total quality management (TQM) have focused on the managerial aspects and associated success factors of TQM programmes, whereas relatively few studies have examined the appropriateness and performance of the large number of available tools and techniques that actually implement TQM practices. To assist in determining the best strategy for improving the performance of TQM tools and techniques, the present study integrates a modified importance–performance matrix with elements of failure mode and effects analysis (FMEA) to provide a comprehensive methodology for assessing and improving the performance of TQM tools and techniques in the Taiwanese motor industry. The methodology presented here can be applied in a variety of industries, including the Taiwanese motor industry, to enable efficient utilisation of TQM tools and techniques and thus improve quality, reduce costs and shorten cycle times.     

Routes to failure: analysis of 41 civil aviation accidents from the Republic of China using the human factors analysis and classification system

The human factors analysis and classification system (HFACS) is based upon Reason's organizational model of human error. HFACS was developed as an analytical framework for the investigation of the role of human error in aviation accidents, however, there is little empirical work formally describing the relationship between the components in the model. This research analyses 41 civil aviation accidents occurring to aircraft registered in the Republic of China (ROC) between 1999 and 2006 using the HFACS framework. The results show statistically significant relationships between errors at the operational level and organizational inadequacies at both the immediately adjacent level (preconditions for unsafe acts) and higher levels in the organization (unsafe supervision and organizational influences). The pattern of the 'routes to failure' observed in the data from this analysis of civil aircraft accidents show great similarities to that observed in the analysis of military accidents. This research lends further support to Reason's model that suggests that active failures are promoted by latent conditions in the organization. Statistical relationships linking fallible decisions in upper management levels were found to directly affect supervisory practices, thereby creating the psychological preconditions for unsafe acts and hence indirectly impairing the performance of pilots, ultimately leading to accidents.     

OPPS-PRI 2.0: an open and optimized process planning system for prismatic parts to improve the performance of SMEs in the machining industry

The needs of customers are now highly specific and continually changing, although they still want to have high-quality and low-cost products. Companies must respond rapidly to the demands of customers and improve their productivity. They must also provide a suitable product variety in order to survive and be competitive in the market. They attempt to find strong and flexible solutions to this problem. One of the keys for improving productivity and responsiveness is computer-aided process planning (CAPP). The automatic generation or preparation of effective and optimal process plans within a short period of time is vitally important in manufacturing, since small- and medium-sized enterprises (SMEs) have recently become a central part of the manufacturing industry of developing countries. The use of new technology and CNC machine tools has dramatically increased in developing countries, while complete CAD/CAM integration through CAPP is still not seen as ‘realizable’. The productivity and responsiveness of SMEs are the two fundamental issues to be improved to provide qualitative success as well as quantitative success and competitiveness. CAPP is the key technology for the increased productivity and responsiveness in SMEs, which are very sensitive to the diffusion of process planning know-how. SMEs have started to believe that it is necessary to use CAPP systems to improve the productivity of their CNC machine tools and systems. Therefore, the main objective of this research is to focus on the development of an open process planning system that can produce optimal process plans and machining codes for SMEs in the machining industry. The present paper introduces the updated version of a process planning system called OPPS-PRI 2.0 (Optimised Process Planning System for PRIsmatic parts) which was developed for SMEs.     

Cognitive modeling and dynamic probabilistic simulation of operating crew response to complex system accidents. Part 2: IDAC performance influencing factors model

This is the second in a series of five papers describing the information, decision, and action in crew context (IDAC) model for human reliability analysis. An example application of this modeling technique is also discussed in this series. The model is developed to probabilistically predict the responses of the nuclear power plant control room operating crew in accident conditions. The operator response spectrum includes cognitive, psychological, and physical activities during the course of an accident. This paper identifies the IDAC set of performance influencing factors (PIFs), providing their definitions and causal organization in the form of a modular influence diagram. Fifty PIFs are identified to support the IDAC model to be implemented in a computer simulation environment. They are classified into eleven hierarchically structured groups. The PIFs within each group are independent to each other; however, dependencies may exist between PIFs within different groups. The supporting evidence for the selection and organization of the influence paths based on psychological literature, observations, and various human reliability analysis methodologies is also indicated.     

Generic probabilistic safety analysis: application to the comparison of the emergency feedwater system designs of three PWRs and to the modernisation and backfitting of older Swedish NPPs

The relationship between configuration characteristics of safety systems and their unavailabilities was examined on the basis of the results of 25 PSAs for PWR and BWR plants [1 and 2]. The results of the evaluations provide groupings of configuration characteristics and associated ranges of unavailabilities that can be expected for the various groups. The safety functions

• residual heat removal at BWR plants

• coolant make-up at BWR plants

• auxiliary/emergency feedwater supply to steam generators at PWR plants

• emergency core cooling, including containment spray injection/recirculation, at PWR plants

were considered.Two applications are reported:

• evidence of validation of the generic approach, based on the results of a blind prediction of the unavailability of the emergency feedwater function at the plants N4, Sizewell-B and Konvoi, and subsequent comparison with results obtained by conventional reliability analysis;

• outline of the use of the approach in the framework of the modernisation and backfitting programme for Swedish nuclear power plants.

In the early discussions of safety objectives for the modernisation programme, the generic approach provided input to the exploration of possible target values for safety systems. In the process of reviewing proposed technical solutions, Swedish Nuclear Power Inspectorate (SKI) is using the generic approach as one of the tools for assessing safety function unavailabilities.