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The purpose of this paper is to inform facility managers of the type of failure affecting certain pipe types more than others. This is useful in asset management as preventive maintenance can be undertaken for those pipe types that experience high probabilities of failure.

The probability of a specific pipe type failing given the cause of break, age at failure, pipe diameter, and type of soil at the location of the break was found using inventory and main break data from the Honolulu Board of Water Supply (HBWS). Bayes’ theorem was then applied to find the posterior probabilities of failure starting from the prior probabilities of failure.

It was observed that the greatest probabilities of failure involved corrosion, pipes aged between 20‐30 years, 8″ pipes, and pipes in fill material. The pipe types were ranked and scored based on their probability of failing due to break cause, age, diameter, and soil type. Cast iron pipes were shown to have the highest probability of failing. As such, attention should be given to replace segments of cast iron pipes as they reach the end of their service lives.

This study serves to address a major query in asset management at a public utility, that of which pipes should be selected for replacement when they reach the end of their service life. In addition, this study helps to understand the causes of failure for the various types of pipe.

The importance of having reliable water supply at low cost has immense social implications in modern communities. To deliver such service, water pipe assets have to be managed efficiently.

This paper addresses the probability of failure in a straightforward manner that the water utility can easily apply to its own data, both in its design and asset management.

This paper aims to develop a system dynamics (SD) model to identify causal relationships among the elements of failure modes and effects analysis (FMEA), i.e. failure modes, effects and causes.

A causal loop diagram (CLD) has been developed based on the results obtained from interdependencies and correlations analysis among the FMEA elements through applying the integrated approach of FMEA-quality function deployment (QFD) developed by Shaker et al. (2019). The proposed model was examined in a steel manufacturing company to identify and model the causes and effects relationships among failure modes, effects and causes of a roller-transmission system.

Findings indicated interactions among the most significant failure modes, effects and causes. Moreover, corrective actions defined to eliminate or relieve critical failure causes. Consequently, production costs decreased, and the production rate increased due to eliminated/decreased failure modes.

The application of CLD illustrates causal relationships among FMEA elements in a more effective way and results in a more precise recognition of the root causes of the potential failure modes and their easy elimination/decrease. Therefore, applying the proposed approach leads to a better analysis of the interactions among FMEA elements, decreased system's failure rate and increased system availability.

The literature review indicated a few studies on the application of SD methodology in the maintenance area, and no study was performed on the causal interactions among FMEA elements through an FMEA-QFD based SD approach. Although the interactions of these elements are significant and helpful in risks ranking, researchers fail to investigate them sufficiently.

Improving reliability is a key factor in reducing the cost of wind energy, which is strongly influenced by the cost of maintenance operations. In this context, this paper aims to propose a degradation model that describes the phenomenon of fault propagation to apply proactive maintenance that will act on the cause of failure to prevent its reoccurrence as well as to improve future system designs.

The methodology adopted consists in identifying the different components of a wind turbine, their causes and failure modes, and then, classifying these components according to their causes of failure.

The result is a classification of the different components of a wind turbine according to their failure causes. From the obtained classification, the authors observed that the failure modes for one component are a failure cause for another component, which describes the phenomenon of failure propagation.

The different classifications existing in the literature depend on the nature, position and function of the different components. The classification of this study consists in grouping the components of a wind turbine according to their failure causes to develop a degradation model considering the propagation of failure in the field of wind turbines.

The railway signal equipment failure diagnosis is a vital element to keep the railway system operating safely. One of the most difficulties in signal equipment failure diagnosis is the uncertainty of causality between the consequence and cause for the accident. The traditional method to solve this problem is based on Bayesian Network, which needs a rigid and independent assumption basis and prior probability knowledge but ignoring the semantic relationship in causality analysis. This paper aims to perform the uncertainty of causality in signal equipment failure diagnosis through a new way that emphasis on mining semantic relationships.

This study proposes a deterministic failure diagnosis (DFD) model based on the question answering system to implement railway signal equipment failure diagnosis. It includes the failure diagnosis module and deterministic diagnosis module. In the failure diagnosis module, this paper exploits the question answering system to recognise the cause of failure consequences. The question answering is composed of multi-layer neural networks, which extracts the position and part of speech features of text data from lower layers and acquires contextual features and interactive features of text data by Bi-LSTM and Match-LSTM, respectively, from high layers, subsequently generates the candidate failure cause set by proposed the enhanced boundary unit. In the second module, this study ranks the candidate failure cause set in the semantic matching mechanism (SMM), choosing the top 1st semantic matching degree as the deterministic failure causative factor.

Experiments on real data set railway maintenance signal equipment show that the proposed DFD model can implement the deterministic diagnosis of railway signal equipment failure. Comparing massive existing methods, the model achieves the state of art in the natural understanding semantic of railway signal equipment diagnosis domain.

It is the first time to use a question answering system executing signal equipment failure diagnoses, which makes failure diagnosis more intelligent than before. The EMU enables the DFD model to understand the natural semantic in long sequence contexture. Then, the SMM makes the DFD model acquire the certainty failure cause in the failure diagnosis of railway signal equipment.

To meet energy demand and tackle the challenges posed by global warming, Bagasse-based Cogeneration Power Generation (BCPG) plant in sugar mills have tremendous potential due to large-scale supply of renewable fuel called bagasse. To meet this goal, an integrated framework has been proposed for analyzing performance issues of BCPG.

Intuitionistic Fuzzy Lambda-Tau (IFLT) approach was implemented to compute various reliability parameters. Intuitionistic Fuzzy Failure Mode and Effect Analysis (IF-FMEA) approach has been implemented for studying risk issues results in decrease in plant's availability. Moreover, IF- Technique for Order Performance by Similarity to Ideal Solution (IF-TOPSIS) is implemented to verify accuracy of IF-FMEA approach.

For membership and non-membership functions, availability decreases to 0.0006% and 0.0020% respectively for spread ±15% to ±30%, and further decreases to 0.0127% and 0.0221% for spread ±30% to ±45%. Under risk assessment failure causes namely Storage tank (ST3), Valve (VL6), Transfer pump (TF8), Deaerator tank (DT11), High pressure heater and economiser (HP15), Boiler drum and super heater (BS22), Forced draft and Secondary air fan (FS25), Air preheater (AH29) and Furnace (FR31) with Intuitionistic Fuzzy Hybrid Weighted Euclidean Distance (IFHWED) based output scores – 0.8988, 0.9752, 0.9400, 0.8988, 0.9267, 1.1131, 1.0039, 0.8185, 1.0604 were identified as the most critical failure causes.

Reliability and risk analysis results derived from IFLT and IF-FMEA approaches respectively, to address the performance issues of BCPG is based on the quantitative and qualitative data collected from the industrial experts and maintenance log book. Moreover, to take care of hesitation in expert's knowledge, IF theory-based concept is incorporated so as to achieve more accuracy in analysis results. Reliability and risk analysis results together will be helpful in analyzing the performance characteristics and diagnosis of critical failure causes, which will minimize frequent failure in BCPG.

The framework will help plant managers to frame optimal maintenance policy in order to enhance the operational aspects of the considered unit. Moreover, the accurate and early detection of failure causes will also help managers to take prudent decision for smooth operation of plant.

The results obtained ensure continuous operation of plant by utilizing the bagasse as fuel in boiler and also mitigate the wastages of fuel. If this bagasse (green fuel) is not properly utilized, there remains a dependency on coal-based power plants to meet the power demand. The results obtained are useful for decreasing dependency on coal, and promoting bagasse as the green, and alternative fuel, the emission by burning of these fuels are not harmful for environment and thereby contribute in preventing the environment from harmful effect of GHGs gases.

IFLT approach has been implemented to develop reliability modeling equations of the BCPG unit, and furthermore to compute various reliability parameters for both membership and non-membership function. The ranking results of IF-FMEA are compared to IF-TOPSIS approach. Sensitivity analysis is done to check stability of proposed framework.

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

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.

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.

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.

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.

This article presents four newly developed mathematical models representing non‐maintained parallel systems with hardware failures, common‐cause failures and human error. The Markov method was used to develop expressions for parallel system state probabilities, system reliability and mean time to failure (MTTF). System reliability and MTTF plots are shown. These plots clearly show the effect of common‐cause failures and human error on system reliability.

This study's main objective is to examine the effect of learning from entrepreneurial failure on performance, with a type of failure as a moderator variable. Interactions between internal and external causes of failure and learning from entrepreneurial failure are also investigated, as well as entrepreneurs' aspects (i.e. age, experience and education) and organisational contextual factors (i.e. size, sector and location).

This study employed a hypothetico-deductive approach through a survey of 250 purposively sampled entrepreneurs who had suffered business failures. The survey data were subjected to regression analysis and moderated regression using WarpPLS software and an independent sample t test for an in-depth analysis.

The results indicated that learning from entrepreneurial failure positively affected business performance, an effect moderated by the type of failure, particularly with large failures. Only perceived internal causes of failure exerted a positive effect on learning from entrepreneurial failure; the external causes did not. The effect of failure on business performance was stronger on entrepreneurs who were older and experienced, had non-university educations and operated small- and medium-sized enterprises (SMEs) outside Java–Bali islands.

This study's findings provide empirical evidence that supports the experiential learning theory and attribution theory in explaining the interaction between learning and failure, its cause, its consequences and its magnitude as perceived by entrepreneurs of SMEs in Indonesia, where the rate of failure is relatively high. The authors’ study also emphasises the roles of the entrepreneur and organisational contextual factors, which matter in learning to improve performance.

This paper aims to explore the specific causes of failure among Malaysian female entrepreneurs who were provided with financial services by the microfinance institution: Amanah Ikhtiar Malaysia (AIM) to start up their own businesses.

This paper adopts a qualitative-based case study design approach, with data collected from a total of 18 female entrepreneurs who had failed to develop their businesses. In-depth personal interviews were conducted, coupled with personal observation via purposive cum snowball sampling.

Thematic analysis revealed a pattern-based outcome which discloses a variety of causes affecting the failure of Malaysian female entrepreneurship. These causes ranged from inter-related external factors which were perceived as beyond their control, such as personal life events, intensive competition and loan inflexibility to internal causes, which were related to lack of resources, poor financial management and personal dissatisfaction with their own business performance.

The findings of this study provide valuable information for Malaysian economic policymakers in how to practically address the objectives of the National Women's Policy (NPW) and improve the innovative quality of their products and services. A thorough understanding of the specific obstacles facing female entrepreneurs in Malaysia is essential if policymakers are to improve opportunity exploitation efficiency and assist in mitigating the external and internal causes of business failure among Malaysian females.

Studies in this field have demonstrated that most new “start-ups” fail within three years of their establishment. While determinist, emotive and voluntarist theories can often provide an adequate explanation for the causes of business failure, it is clear that no single factor is usually responsible. Rather, multiple interrelated factors are found to be at play. This study, therefore, provides an integrative model for causes of business failure among small-business female entrepreneurs. It also represents one of only a few such studies in the literature and, to the best of knowledge at the time of writing, is the first such study that used an integrative approach to explain the causes of business failure in the Malaysian context.