The Reliability of a Tunnel Boring Machine

ABSTRACT Greater levels of complexity in tunnelling with Tunnel Boring Machine (TBM) allow higher chances of fail-ures that may increase the potential hazardous risks. This paper presents the results of a study on TBM reliability using risk analysis. Machinery Failure Mode and Effect analysis was applied to analyze the risks of a TBM using QS9000 and SAE.J1739 recommendations. For this purpose, 48 failure modes were pos-tulated for the TBM main systems and all subsystems. Afterwards, the effects of every failure were listed. Safeguards or controls that might prevent or mitigate the effects of each failure were also listed. In the final step, essential remedial actions to prevent or mitigate the failure were recommended. Risk Matrix was developed for each possible failure to be used for risk ranking. For this, the Risk Priority Number (RPN) was estimated for each failure mode for pro and post application of control measures to identify the most critical failures. The results revealed that 7 failure modes had risk priority numbers higher than 80 therefore, they were categorized unacceptable. Cutter head stop due to bad rock condition with RPN=240 was the significant critical failure. The results also showed that 3 failure modes in TBM required modifica-tion due to high Severity rate. The findings from this study were applied to a long tunnel under construc-tion and significantly reduced the accidents during the next two years tunnelling period. It can be con-cluded that MFMEA is a superb tool for TBM reliability evaluation and promotion. Keywords: Risk, Tunnelling, Analysis, Hazard, Tunnel Boring Machin

Risk Assessment of a Tunnelling Process Using Machinery Failure Mode and Effects Analysis (MFMEA)

Abstract In recent years, risk management associated with safety and reliability of the process especially in oil and gas industry has been widely used. For this purpose, different methods of risk analysis have been developed and successfully applied. Greater levels of complexity in tunnelling using TBM (Tunnel Boring Machine) especially in gassy tunnels with a large volume of water coming out of them, allow higher chances of failure that may increase the potential for tunnelling facilities to become more hazardous. When there is an ever increasing awareness of hazardous risks that need to be managed by the industrial community, the risks need to be analyzed. This paper presents the results of a study on risk management in a tunnel excavation with TBM. MFMEA was applied to analyze the risks of a tunnelling process. In order to apply MFMEA, 7 main systems and components involved in a tunnelling process were selected and split into subsystems. In total, 71 failure modes were then postulated for all subsystems. In the next step, the effects of every failure of each subsystem were listed. Safeguards or controls that might prevent or mitigate the effects of each failure were then listed. In the final step, essential 1 remedial actions to prevent or mitigate the failure were recommended. Risk Matrix was developed for each possible failure to be used for risk ranking. For this purpose the Risk Priority Number (RPN) was estimated for each failure mode to identify the most critical failures. The results revealed that, the failure of the ventilation system (RPN=480) is the most critical failure. The TBM failure due to bad rock condition (RPN=240) and rolling stock failure due to unleveled rails (RPN= 200) are the next significant critical failures. The findings from this study were applied to a long tunnel under construction and significantly reduced the accidents during the tunnelling period. Tracking of the accidents occurred during the next 2 years showed that MFMEA is a perfect method for risk management in tunnelling process as well. Key Words: Hazards Identification; Risk Analysis; Risk Management, Tunnelling

FlowSort-GDSS:a novel group multi-criteria decision support system for sorting problems with application to FMEA

Failure mode and effects analysis (FMEA) is a well-known approach for correlating the failure modes of a system to their effects, with the objective of assessing their criticality. The criticality of a failure mode is traditionally established by its risk priority number (RPN), which is the product of the scores assigned to the three risk factors, which are likeness of occurrence, the chance of being undetected and the severity of the effects. Taking a simple "unweighted" product has major shortcomings. One of them is to provide just a number, which does not sort failures modes into priority classes. Moreover, to make the decision more robust, the FMEA is better tackled by multiple decision-makers. Unfortunately, the literature lacks group decision support systems (GDSS) for sorting failures in the field of the FMEA. In this paper, a novel multi-criteria decision making (MCDM) method named FlowSort-GDSS is proposed to sort the failure modes into priority classes by involving multiple decision-makers. The essence of this method lies in the pair-wise comparison between the failure modes and the reference profiles established by the decision-makers on the risk factors. Finally a case study is presented to illustrate the advantages of this new robust method in sorting failures

Failure Mode and Effects Analysis Using Generalized Mixture Operators

Failure mode and effects analysis (FMEA) is a method based on teamwork to identify potential failures and problems in a system, design, process and service in order to remove them. The important part of this method is determining the risk priorities of failure modes using the risk priority number (RPN). However, this traditional RPN method has several shortcomings. Therefore, in this paper we propose a FMEAwhich uses generalized mixture operators to determine and aggregate the risk priorities of failure modes. In a numerical example, a FMEA of the LGS gas type circuit breaker product in Zanjan Switch Industries in Iran is presented to further illustrate the proposed method. The results show that the suggested approach is simple and provides more accurate risk assessments than the traditional RPN

A Modified FMEA Approach to Enhance Reliability of Lean Systems

Purpose - The purpose of this thesis is to encourage the integration of Lean principles with reliability models to sustain Lean efforts on long term basis. This thesis presents a modified FMEA that will allow Lean practitioners to understand and improve the reliability of Lean systems. The modified FMEA approach is developed based on the four critical resources required to sustain Lean systems: personnel, equipment, material and schedule. Design/methodology/approach – A three phased methodology approach is presented to enhance the reliability of Lean systems. The first phase compares actual business and operational conditions with conditions assumed in Lean implementation. The second phase maps potential deviations of business and operational conditions to their root cause. The third phase utilizes a modified Failure Mode and Effects Analysis (FMEA) to prioritize issues that the organization must address. Findings – A literature search shows that practical methodologies to improve the reliability of Lean systems are non existent. Research Limitations/Implications –The knowledge database involves tedious calculations and hence it needs to be automated. Originality/Value • Defined Lean system reliability • Developed conceptual model to enhance the Lean system reliability • Developed knowledge base in the form of detailed hierarchical root trees for the four critical resources that support our Lean system reliability • Developed Risk Assessment Value (RAV) based on the concept of effectiveness of detection using Lean controls when Lean designer implements Lean change. • Developed modified FMEA for the four critical resources • Developed RPLS tool to prioritize Lean failures • Developed case study to analyze RPN and RAV approac

Elements of maintenance system and tools for implementation within framework of Reliability Centred Maintenance- A review

For plant systems to remain reliable and safe they must be effectively maintained through a sound maintenance management system. The three major elements of maintenance management systems are; risk assessment, maintenance strategy selection and maintenance task interval determination. The implementation of these elements will generally determine the level of plant system safety and reliability. Reliability Centred Maintenance (RCM) is one method that can be used to optimise maintenance management systems. This paper discusses the three major elements of a maintenance system, tools utilised within the framework of RCM for performing these tasks and some of the limitations of the various tools. Each of the three elements of the maintenance management system has been considered in turn. The information will equip maintenance practitioners with basic knowledge of tools for maintenance optimisation and stimulate researchers with respect to developing alternative tools for application to plant systems for improved safety and reliability. The research findings revealed that there is a need for researchers to develop alternative tools within the framework of RCM which are efficient in terms of processing and avoid the limitations of existing methodologies in order to have a safer and more reliable plant system.

A new fuzzy-dynamic risk and reliability assessment

The purpose of this article is to consider system safety and reliability analysts to evaluate the risk associated with item failure modes. The factors considered in traditional failure mode and effect analysis (FMEA) for risk assessment are frequency of occurrence (O), severity (S) and detectability (D) of an item failure mode. Because of the subjective, qualitative and dynamic nature of the information and to make the analysis more consistent and logical, an approach using fuzzy logic and system dynamics methodology is proposed. In the proposed approach, severity is replaced by dependency parameter then, these parameters are represented as members of a fuzzy set fuzzified by using appropriate membership functions and they are evaluated in fuzzy inference engine, which makes use of well-defined rule base and fuzzy logic operations to determine the value of parameters related to system's transfer functions. The fuzzy conclusion is then defuzzified to get transfer function for risk and failure rate. The applicability of the proposed approach is investigated with the help of an illustrative case study from the automotive industry

A Novel Approach to Minimizing the Risks of Soft Errors in Mobile and Ubiquitous Systems

A novel approach to minimizing the risks of soft errors at modelling level of mobile and ubiquitous systems is outlined. From a pure dependability viewpoint, critical components, whose failure is likely to impact on system functionality, attract more attention of protection/prevention mechanisms (against soft errors) than others do. Tolerating soft errors can be much improved if critical components can be identified at an early design phase and measures are taken to lower their criticalities at that stage. This improvement is achieved by presenting a criticality ranking (among the components) formed by combining a prediction of soft errors, consequences of them, and a propagation of failures at system modelling phase; and pointing out the ways to apply changes in the model to minimize the risks of degradation of desired functionalities. Case study results are given to illustrate and validate the approach

A Model-Based Soft Errors Risks Minimization Approach

Minimizing the risk of system failure in any computer structure requires identifying those components whose failure is likely to impact on system functionality. Clearly, the degree of protection or prevention required against faults is not the same for all components. Tolerating soft errors can be much improved if critical components can be identified at an early design phase and measures are taken to lower their criticalities at that stage. This improvement is achieved by presenting a criticality ranking (among the components) formed by combining a prediction of faults, consequences of them, and a propagation of errors at the system modeling phase; and pointing out ways to apply changes in the model to minimize the risk of degradation of desired functionalities. Case study results are given to validate the approach