Risk priority number (RPN) is a widely used approach, and it is a powerful means to assess the criticality of modes in a failure modes, effects, and criticality analysis (FMECA) worksheet. In the application of the traditional FMECA, the RPN is determined to rank the failure modes; however, the method has been criticized several times for having many drawbacks and weaknesses, such as the presence of gaps in the range of admissible values, the duplicates value provided by different combinations of the base factors, and the high sensitivity to small changes. This paper analyses and compares some alternative RPN formulation proposed in the literature to overcome these limits. This paper takes into account only the alternative RPN, which proposes a powerful solution without increasing the computational complexity and remaining coherent to the classical idea included in the international standard IEC 60812. In order to compare the advantages and disadvantages of these alternative RPNs, an FMECA was developed for a heating, ventilation, and air condition (HVAC) system in railway application. The critical analysis of the comparison can provide recommendations and suggestions regarding the choice of the alternative RPN based on the type of application. Finally, this paper takes into account the scales reduction of possible values related to the parameters (i.e., occurrence, severity, and detection), which influence the assessment of the RPN. This approach allows the designers to mitigate the drawbacks related to the full scale and provide an easier and faster assessment of the scores to evaluate the criticality analysis and prioritization. INDEX TERMS FMECA, railway engineering, reliability theory, risk priority number.
Wireless Sensor Networks are subjected to some design constraints (e.g., processing capability, storage memory, energy consumption, fixed deployment, etc.) and to outdoor harsh conditions that deeply affect the network reliability. The aim of this work is to provide a deeper understanding about the way redundancy and node deployment affect the network reliability. In more detail, the paper analyzes the design and implementation of a wireless sensor network for low-power and low-cost applications and calculates its reliability considering the real environmental conditions and the real arrangement of the nodes deployed in the field. The reliability of the system has been evaluated by looking for both hardware failures and communication errors. A reliability prediction based on different handbooks has been carried out to estimate the failure rate of the nodes self-designed and self-developed to be used under harsh environments. Then, using the Fault Tree Analysis the real deployment of the nodes is taken into account considering the Wi-Fi coverage area and the possible communication link between nearby nodes. The findings show how different node arrangements provide significantly different reliability. The positioning is therefore essential in order to obtain maximum performance from a Wireless sensor network.
This paper develops a Failure Mode, Effects and Criticality Analysis (FMECA) for a heating, ventilation and air conditioning (HVAC) system in railway. HVAC is a safety critical system which must ensure emergency ventilation in case of fire and in case of loss of primary ventilation functions. A study of the HVAC's critical areas is mandatory to optimize its reliability and availability and consequently to guarantee a low operation and maintenance cost. The first part of the paper describes the FMECA which is performed and reported to highlight the main criticalities of the HVAC system under analysis. Secondly, the paper deals with the problem of the evaluation of a threshold risk value, which can distinguish negligible and critical failure modes. Literature barely considers the problem of an objective risk threshold estimation. Therefore, a new analytical method based on finite difference is introduced to find a univocal risk threshold value. The method is then tested on two Risk Priority Number datasets related to the same HVAC. The threshold obtained in both cases is a good tradeoff between the risk mitigation and the cost investment for the corrective actions required to mitigate the risk level. Finally, the threshold obtained with the proposed method is compared with the methods available in literature. The comparison shows that the proposed finite difference method is a well-structured technique, with a low computational cost. Furthermore, the proposed approach provides results in line with the literature, but it completely deletes the problem of subjectivity.
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