| 1. |
- Catelani, Marcantonio, et al.
(author)
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A Practical Solution for HVAC Life Estimation Using Failure Models
- 2020
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In: 17th IMEKO TC 10 and EUROLAB Virtual Conference. - : International Measurement Confederation (IMEKO). ; , s. 85-91
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Conference paper (peer-reviewed)abstract
- Heating, ventilation, and air conditioning (HVAC) is the technology of indoor and vehicular environmental comfort. The objectives of HVAC systems are to provide an acceptable level of occupancy comfort and process function, to maintain good indoor air quality, and to keep system costs and energy requirements to a minimum. Performing a reliability prediction provides an awareness of potential equipment degradation during the equipment life cycle. Reliability under a range of conditions is one of the most important requirements to guarantee in HVAC installed on trains. Predicting the reliability of mechanical equipment requires the consideration of its exposure to the environment and subjection to a wide range of stress levels such as impact loading. Often analysist find an unavailability of failure data in handbooks and problems for acquiring data for mechanical components, so the mentioned problems demonstrates the need for reliability prediction models. The paper deals with a HVAC installed on a high-speed train and evaluates the failure rates through the failure rate models suggested by the handbooks in order to assess a model which includes all the mechanical parts.
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| 2. |
- Catelani, Marcantonio, et al.
(author)
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Estimate the useful life for a heating, ventilation, and air conditioning system on a high-speed train using failure models
- 2021
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In: Acta IMEKO. - : International Measurement Confederation (IMEKO). - 0237-028X. ; 10:3, s. 100-107
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Journal article (peer-reviewed)abstract
- Heating, ventilation, and air conditioning (HVAC) is a widely used system used to guarantee an acceptable level of occupancy comfort, to maintain good indoor air quality, and to minimize system costs and energy requirements. If failure data coming from company database are not available, then a reliability prediction based on failure rate model and handbook data must be carried out. Performing a reliability prediction provides an awareness of potential equipment degradation during the equipment life cycle. Otherwise, if field data regarding the component failures are available, then classical reliability assessment techniques such as Fault Tree Analysis and Reliability Block Diagram should be carried out. Reliability prediction of mechanical components is a challenging task that must be carefully assessed during the design of a system. For these reasons, this paper deals with the reliability assessment of an HVAC using both failure rate model for mechanical components and field data. The reliability obtained using the field data is compared to the one achieved using the failure rate models in order to assess a model which includes all the mechanical parts. The study highlights how it is fundamental to analyze the reliability of complex system integrating both field data and mathematical model.
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| 3. |
- Catelani, Marcantonio, et al.
(author)
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FMECA assessment for railway safety-critical systems investigating a new risk threshold method
- 2021
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In: IEEE Access. - : IEEE. - 2169-3536. ; 9, s. 86243-86253
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Journal article (peer-reviewed)abstract
- 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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| 4. |
- Ciani, Lorenzo, et al.
(author)
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Improving Human Reliability Analysis for railway systems using fuzzy logic
- 2021
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In: IEEE Access. - : IEEE. - 2169-3536. ; 9, s. 128648-128662
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Journal article (peer-reviewed)abstract
- The International Union of Railway provides an annually safety report highlighting that human factor is one of the main causes of railway accidents every year. Consequently, the study of human reliability is fundamental, and it must be included within a complete reliability assessment for every railway-related system. However, currently RARA (Railway Action Reliability Assessment) is the only approach available in literature that considers human task specifically customized for railway applications. The main disadvantages of RARA are the impact of expert’s subjectivity and the difficulty of a numerical assessment for the model parameters in absence of an exhaustive error and accident database. This manuscript introduces an innovative fuzzy method for the assessment of human factor in safety-critical systems for railway applications to address the problems highlighted above. Fuzzy logic allows to simplify the assessment of the model parameters by means of linguistic variables more resemblant to human cognitive process. Moreover, it deals with uncertain and incomplete data much better than classical deterministic approach and it minimizes the subjectivity of the analyst evaluation. The output of the proposed algorithm is the result of a fuzzy interval arithmetic, α-cut theory and centroid defuzzification procedure. The proposed method has been applied to the human operations carried out on a railway signaling system. Four human tasks and two scenarios have been simulated to analyze the performance of the proposed algorithm. Finally, the results of the method are compared with the classical RARA procedure underline compliant results obtain with a simpler, less complex and more intuitive approach.
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| 5. |
- Ciani, Lorenzo, et al.
(author)
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Reliability evaluation of an HVAC ventilation system with FTA and RBD analysis
- 2020
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In: 2020 International Symposium on Systems Engineering (ISSE) Proceedings. - : IEEE.
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Conference paper (peer-reviewed)abstract
- Rail industry is rapidly developing, and rail becomes ever more viable in a wide range of regions. Therefore, the passenger experience and comfort has become a major concern for operators in the world. Heating, ventilation and air conditioning systems are used in railways to provide passengers thermal comfort and proper air motion. The ventilation system is one of the main elements of the system. Its components include both mechanical and electronic devices. All components are subjected to stress, and this tends to reduce their life cycle; the reliability of the ventilation system must be evaluated to plan and schedule appropriate maintenance activities. The paper evaluates reliability of the ventilation system using fault tree analysis and a reliability block diagram. Both techniques analyse data qualitatively; moreover, with specific algorithms they also provide quantitative results in term of reliability and probability of system failure. The paper compares the two reliability evaluation methods to verify their accuracy.
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| 6. |
- Smart, Sophie E., et al.
(author)
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Clinical predictors of antipsychotic treatment resistance: Development and internal validation of a prognostic prediction model by the STRATA-G consortium
- 2022
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In: Schizophrenia Research. - : Elsevier. - 0920-9964 .- 1573-2509. ; 250
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Journal article (peer-reviewed)abstract
- IntroductionOur aim was to, firstly, identify characteristics at first-episode of psychosis that are associated with later antipsychotic treatment resistance (TR) and, secondly, to develop a parsimonious prediction model for TR.MethodsWe combined data from ten prospective, first-episode psychosis cohorts from across Europe and categorised patients as TR or non-treatment resistant (NTR) after a mean follow up of 4.18 years (s.d. = 3.20) for secondary data analysis. We identified a list of potential predictors from clinical and demographic data recorded at first-episode. These potential predictors were entered in two models: a multivariable logistic regression to identify which were independently associated with TR and a penalised logistic regression, which performed variable selection, to produce a parsimonious prediction model. This model was internally validated using a 5-fold, 50-repeat cross-validation optimism-correction.ResultsOur sample consisted of N = 2216 participants of which 385 (17 %) developed TR. Younger age of psychosis onset and fewer years in education were independently associated with increased odds of developing TR. The prediction model selected 7 out of 17 variables that, when combined, could quantify the risk of being TR better than chance. These included age of onset, years in education, gender, BMI, relationship status, alcohol use, and positive symptoms. The optimism-corrected area under the curve was 0.59 (accuracy = 64 %, sensitivity = 48 %, and specificity = 76 %).ImplicationsOur findings show that treatment resistance can be predicted, at first-episode of psychosis. Pending a model update and external validation, we demonstrate the potential value of prediction models for TR.
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