Reliability and safety in the process industries like automotive industry are important key success factors for upgrading availability and preventing catastrophic failures. In this context, failure mode and effect analysis (FMEA) technique is a proactive diagnostic tool for evaluating all failure modes which reduces the highest risk priority failures. However, it still suffers from subjective uncertainty and ambiguity which are important factors in risk analysis procedures. Hence, this paper provides a comprehensive survey to overcome the drawbacks of the traditional FMEA through improved FMEA, incorporating the fuzzy inference system (FIS) environment. For this purpose, the effective attributes, such as; various scales and rules, various membership functions, different defuzzification algorithms and their impacts on fuzzy RPN (FRPN) have been investigated. Moreover, three types of sensitivity analysis were performed to identify the effect and authority control of risk parameters, i.e., severity, occurrence and detection on FRPN. To demonstrate the feasibility of the proposed framework, as a practical example, the method was implemented in complex equipment in an automotive production line. The result of FIS-FMEA model revealed that the proposed framework could be useful in recognizing the failure modes with critical risk values compared to the traditional FMEA. Given the potential applications of this approach, suitable maintenance actions can be recommended to improve the reliability and safety of process industry, such as automotive production line.

Automotive industries require effective and reliable maintenance strategies to ensure high levels of availability and safety. Risk-based maintenance approach is a useful tool for maintenance decision making with the aim of reducing the overall risk in operating activities. In this paper, a Failure Mode and Effect Analysis (FMEA) model as one of the risk assessment techniques is developed with subjective information derived from domain experts. To overcome the drawbacks of traditional FMEA for risk priority number (RPN) estimation, a linguistic fuzzy set theory, through effective decision attributes in complex automotive equipment is conducted. The main attributes of this approach include the effect of experts’ traits, scales variation, using various membership functions and defuzzification algorithms on reliable Fuzzy-RPN (FRPN) estimation. The result of the proposed model revealed that altering membership functions and defuzzification algorithms had no significant effect on the FRPN estimation, but their values are highly affected by the number of scales. The sensitivity analysis showed that experts’ traits have no sensible impact on experts’ opinion for FRPN estimation, while the detectability index has more impact on FRPN variation. The result of risk classification number showed that the maintenance decision making could be included for the failure modes with the highest RPN values as a priority, which it would be useful to achieve the high level of availability and safety.

Reliability evaluation plays a critical role in upgrading the availability and productivity of automotive manufacturing industries by adopting the well-planned maintenance. Due to the lack of operation management studies in automotive industry, this paper addresses an operational reliability evaluation through failure behavior trend in an automotive production line. The main approaches for reliability analysis in this study include statistical structure and Monte Carlo simulation model. The statistical structure consists of three steps: data acquisition and homogenization process, validity of the trend hypothesis and parameters estimation. The reliability evaluation under statistical approach identified the main bottlenecks through the recognized behavior trend of system so that needs to be considered as a priority. Besides, K–R algorithm as Monte Carlo simulation was carried out to simulate reliability regarding failure distribution function. The result of Monte Carlo simulation with different iterations provides a high prediction accuracy of reliability with the lowest error. In addition, regarding the computed reliability through the proposed approaches and total expected cost, a reliability-based maintenance optimization model was conducted. The proposed maintenance intervals could be useful for improving the operational performance of critical components in automotive system.

Automotive manufacturing industries are required to improve their productivity with higher production rates at the lowest cost, less number of unexpected shutdowns, and reliable operation. In order to achieve the above objectives, the application of reliability, availability, and maintainability methodologies can constitute for resilient operation, identifying the bottlenecks of manufacturing process and optimization of maintenance actions. In this article, we propose a framework for reliability, availability, and maintainability evaluation and maintenance optimization to improve the performance of conveying process of vehicle body in an automotive assembly line. The results of reliability, availability, and maintainability analysis showed that the reliability and maintainability of forklift and loading equipment are the main bottlenecks. To find the optimal maintenance intervals of each unit, a multi-attribute utility theory is applied for multi-criteria decision model considering reliability, availability, and costs. Due to the series configuration of conveying process in automotive assembly line, the optimized time intervals are obtained using opportunistic maintenance strategy. The results could be useful to improve operational performance and sustainability of the production process.

Considering the present business competitive scenario, the automotiveindustry is under pressure to achieve higher productivity. A high level ofperformance and quality standard could be achieved through improvingthe Overall Equipment Effectiveness (OEE) of the equipment in anautomotive industry. Thus, the aim of this study is to investigate theperformance measurement through OEE theory in an Iranian automotiveindustry. Data and basic information collected from the ComputerizedMaintenance Management System (CMMS) of the automotive assemblylines. In this case study, two different assembly lines such Peugeot andSports Utility Vehicle (SUV) were studied. The results indicated that theindices such availability rate, performance and quality for Peugeotassembly line obtained an OEE value of 0.99, 0.70 and 0.38,respectively, and, these indices for SUV assembly line obtained as 0.99,0.39 and 0.53, respectively. Statistical analysis results of net operatingtime parameter for two assembly lines revealed that there is significantdifference in the confidence level of 5% (P-value < 0.05). In addition, theOEE index for Peugeot and SUV assembly lines gained 0.27 and 0.21over a period of one year. Consequently, to improve the OEE in theautomotive assembly lines, managing the time losses by systematicplanning of manufacturing and the implementation of Total ProductiveMaintenance (TPM) are suggested.

Proactive condition monitoring, diagnosis and prognosis of modern complex engineering systems are becoming an increasingly challenging issue. This is mainly attributed to the dynamic global scenario and the ever increasing stakeholders conflicting interests. One of the most important missing links that is often given a low priority while assessing the health of a complex dynamic system is the key criticality of the system in question. This paper discusses some of the challenging issues facing the Asset Management personnel and highlights the importance of incorporating criticality aspect of the key performance indicator in the diagnosis and prognosis of all modern complex systems.

Lean Maintenance (LM) enhances organizational profitability by identifying and eliminating maintenance related wastes. There exists no singular metric that measures maintenance related wastes. The paper identifies the LM features and models them using incidence-matrix. LM features are represented by diagonal elements, while its off-diagonal elements represent mutual influences among the LM features. The maintenance system leanness is quantified using the permanent of the matrix. The metric of leanness is proposed to be defined as Lean maintenance index (LMI) and is a ratio of the actual to the ideal values of permanent of actual and ideal maintenance system matrices. A high value of LMI indicates that the maintenance system is operating in a reduced waste scenario with respect to its resources. Among all the LM features, LMI was found to be most sensitive to management support including organizational processes. The results of the methodology are a good guide for managers. The shortcoming of the methodology is that, it relies on values and weights of the inter-relations among the features, which may not be necessarily true and may need further scientific rigor. The proposed methodology of using LMI as a singular metric to judge maintenance efficacy is expected to aid the operation managers in quantifying the maintenance leanness and may help them focus their efforts appropriately. There is no evidence to indicate existence of comprehensive list of LM features that culminate into a singular metric of maintenance productivity. This paper attempts to fill this gap.

In this study, a formula is described for calculating the relationship between preventive maintenance inspections and train delays for rail infrastructure. The reduction in train delays due to preventive maintenance has been calculated as follows: When an inspection is performed, the probability to find a potential failure is between 0 and 100 %. The potential failure is registered as an inspection remark. The risk that the potential failure will degrade to a functional failure within a certain time is also between 0 and 100 %. Strictly speaking, this time is equal to the specified maximum time to restoration in the inspection remark registration, e.g. within one week, but in practice longer. As an example, one can assume 100 inspections, with 10 % probability to find a potential failure, 75 % risk for functional failure if an action is not taken within a near future and 25 % risk for train delays. This gives 100 · 0.1 · 0.75 · 0.25 = 1.875 prevented train-delaying functional failures, and therefore 1.875 times the average number of train delay minutes in reduced minutes of train delays.

A case study was carried out to verify the proposed method. The prevented train-delay minutes per maintenance inspection for various rail infrastructure systems were found to be between 0-40 minutes per inspection. However, the result depends to a large extent on the definition of inspection within the maintenance database. The result also depends on the type of inspection, e.g. safety or maintenance inspections. Moreover, the result depends greatly on the criteria that are being used for specifying the risk that a potential failure will degrade to a functional failure within a certain time period. These factors need to be clear before actions are taken upon the results.