Estimate the useful life for a heating, ventilation, and air conditioning system on a high-speed train using failure models ACTA IMEKO ISSN: 2221-870X September 2021, Volume 10, Number 3, 100 - 107 ACTA IMEKO | www.imeko.org September 2021 | Volume 10 | Number 3 | 100 Estimate the useful life for a heating, ventilation, and air conditioning system on a high-speed train using failure models Marcantonio Catelani1, Lorenzo Ciani1, Giulia Guidi1, Gabriele Patrizi1, Diego Galar2 1 Department of information engineering, University of Florence via di S. Marta 3, 50139, Florence (Italy) 2 Luleå University of Technology, Lulea, Sweden Section: RESEARCH PAPER Keywords: Reliability; Diagnostic; Railway engineering; failure rate; HVAC; useful life Citation: Marcantonio Catelani, Lorenzo Ciani, Giulia Guidi, Gabriele Patrizi, Diego Galar, Estimate the useful life for a heating, ventilation, and air conditioning system on a high-speed train using failure models, Acta IMEKO, vol. 10, no. 3, article 10, September 2021, identifier: IMEKO-ACTA-10 (2021)-03- 10 Section Editor: Lorenzo Ciani, University of Florence, Italy Received January 29, 2021; In final form August 2, 2021; Published September 2021 Copyright: This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Corresponding author: Giulia Guidi, e-mail: giulia.guidi@unifi.it 1. INTRODUCTION All devices are constituted from materials that will tend to degrade with time. The materials degradation will continue until some critical device parameter can no longer meet the required specification for proper device functionality [1]-[8]. For this reason, as well as the growing complexity of equipment and the rapidly increasing cost incurred by loss of operation and for maintenance, the interest in reliability is growing in many industrial fields. Generally reliability could be assessed through different methods, such as Reliability prediction, Fault Tree Analysis , Reliability Block Diagram etc (see for instance [9]-[12]). Fault tree analysis (FTA) [13], [14] is an analytical and deductive (top- down) method. It is an organized graphical representation of the conditions or other factors causing or contributing to the occurrence of a defined outcome, referred to as the "top event". While, Reliability Block Diagram (RBD) [15] is a functional diagram of all the components making up the system that shows how component reliability contributes to failure or success of the whole system. These above-mentioned techniques need input data to be performed but sometimes data are not available, and they need to be predicted. An accurate reliability prediction should be performed in the early stages of a development program to support the design process [16]-[21]. A reliability prediction of electronic components could be assessed following the guidelines of several handbooks, while the prediction of mechanical components is more challenging because of the following reasons [16], [22]: • Individual mechanical components such as valves and gearboxes often perform more than one function and 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. mailto:giulia.guidi@unifi.it ACTA IMEKO | www.imeko.org September 2021 | Volume 10 | Number 3 | 101 failure data for specific applications of nonstandard components are seldom available. • Failure rates of mechanical components are not usually described by a constant failure rate distribution because of wear, fatigue and other stress-related failure mechanisms resulting in equipment degradation. Data gathering is complicated when the constant failure rate distribution cannot be assumed and individual times to failure must be recorded in addition to total operating hours and total failures. • Mechanical equipment reliability is more sensitive to loading, operating mode and utilization rate than electronic equipment reliability. Failure rate data based on operating time alone are usually inadequate for a reliability prediction of mechanical equipment • Definition of failure for mechanical equipment depends upon its application. Lack of such information in a failure rate data bank limits its usefulness. The above listed problems demonstrates the need for reliability prediction models that do not rely solely on existing failure rate data banks [23], [24]. Trying to solve these needs, this paper aims to introduce a reliability assessment procedure which integrates failure rate models and field data to optimize the reliability analysis of a railway heating, ventilation and air conditioning (HVAC) system. The paper uses both FTA and RBD techniques to estimate the system reliability based on realistic failure rate models for mechanical components. The rest of the paper is organized as follow: section 2 illustrates the aim of an HVAC and it presents the high-level taxonomy of the system under test; section 3 presents the failure rate prediction of three mechanical components (compressor, heat exchanger and blower) using failure models; section 4 shows the results of the reliability assessment carried out using FTA and RBD techniques and finally section 5 compares the results achieved with the different techniques. 2. HVAC FOR HIGH-SPEED TRAIN Underground transport and rail systems become more and more frequent as they allow rapid transit times while transporting a large number of users [25]. Consequently, RAMS (reliability, availability, maintainability and safety) analysis has become a fundamental tool during the design of railway systems [25]-[27]. The network of high-speed trains and also standard rails are more and more transferred to underground tunnels in order to mitigate the environmental impact. Both applications need ventilation rates. In Metros the influx of a large number of people and the presence of moving trains generate a reduction of oxygen and an increase in heat and pollutant. Mechanical ventilation is then required to achieve the necessary air exchange and grant users of the underground train systems comfortable conditions. Ventilation systems have a second and even more important purpose. That is to guarantee safety in case of fire emergency. In order to create a safe and clean environment for escaping mechanical ventilation both in tunnels and in the stations is activated. In rails the ventilation of tunnels is mainly dedicated to fire emergencies where it is vital to keep under control the smoke propagation and create safe areas and clear environment for the users. Furthermore, efficient temperature regulation is becoming a necessity to face overcrowded carriages [28]-[30]. HVAC is the best way of regulating temperature and air quality on crowded trains [31]. One of the most important guarantees that rail manufacturers should look for during the design of an air conditioning systems is reliability under the actual operating conditions [28], [32]. During the design of an HVAC system it is necessary to achieve information about the HVAC equipment and their uses[33]. The taxonomy is a systematic classification of items into generic groups based on factors possibly common to several of the items (location, use, equipment subdivision, etc.). Referring to Figure 1, levels 1 to 5 represent a high-level categorization that relates to industries and plant application regardless of the equipment units (see level 6) involved. This is because an equipment unit (e.g., air conditioning unit) can be used in many different industries and plant configurations and, for analysing the failure/reliability/maintainability of similar equipment, it is necessary to have information about the operating context. Taxonomic information on these levels (1 to 5) shall be included in the database for each equipment unit as “use/location data”. Levels 6 to 9 are related to the equipment unit (inventory) with the subdivision in lower indenture levels corresponding to a parent-child relationship. The taxonomy of the system under test, from level 1 to level 5 is reported in Table 1. The levels from 6 to 9 are very structured and include the level of the components divided also in the part sections. 3. FAILURE RATE MODELS Predicting the life of a mechanical element is not easy, it includes mathematical equations to estimate the design life of mechanical components [16]. These reliability equations consider the design parameters, environmental extremes and operational stresses to predict the reliability parameters. The total failure rate of the Figure 1. Taxonomy classification with taxonomic levels (SOURCE ISO 14224 - 2016 [34]). Table 1. Taxonomy of the system from level 1 to level 5. Taxonomy level Description Level 1 - Industry Railway Level 2 - Business Category High Speed Level 3 - Installation S121 Level 4 - Unit Front car Level 5 - System HVAC system ACTA IMEKO | www.imeko.org September 2021 | Volume 10 | Number 3 | 102 component is the sum of the failure rates for the parts for a particular time period in question. The equations rely on a base failure rate derived from laboratory test data where the exact stress levels are known. More information about the failure rate data used in this work could be found in [19]. The most critical components of an Heating, Ventilation and Air Conditioning (HVAC) system are the compressor, the heat exchanger and the blower[25], [35]. In order to improve the failure rate of these items, the relative failure models have been analysed in the following sections. 3.1. Compressor model A compressor system is made up of one or more stages. The compressor compresses the gas, increasing its temperature and pressure [16], [36]. The total compressor may be comprised of elements or groups of elements in series to form a multistage compressor based on the change in temperature and pressure across each stage. Every compressor to be analyzed will be characterized by a unique design and it will be comprised of many different components. According to [16] and to the compressor datasheet, the designed HVAC compressor is a reciprocating type compressor. The following equation has been obtained in order to estimate the failure rate of the actual compressor used in the considered HVAC design. 𝜆C = (𝜆FD ∙ 𝐶SF)+𝜆CA + 𝜆BE + 𝜆VA + 𝜆SE + 𝜆SH , (1) where • 𝜆C is the total failure rate of compressor • 𝜆FD is failure rate of fluid driver • 𝐶SF is the compressor service multiplying factor • 𝜆CA is the failure rate of the compressor casing • 𝜆BE is the total failure rate of compressor shaft bearings • 𝜆VA is the total failure rate of control valve assemblies • 𝜆SE is the total failure rate of compressor seals • 𝜆SH is the failure rate of compressor shaft. Different compressor configurations such as piston, rotary screw and centrifugal have different parts within the total compressor and it is important to obtain a parts list for the compressor prior to estimating its reliability. The failure rate for each part comprising the compressor must be determined before the entire compressor assembly failure rate, λC, can be determined. Failure rates for each part will depend on expected operational and environmental factors that exist during compressor operation. The total failure rate of compressor shaft bearings is: 𝜆BE = 𝜆BE,B ∙ 𝐶R ∙ 𝐶V ∙ 𝐶CW ∙ 𝐶t ∙ 𝐶SF ∙ 𝐶C , (2) where • 𝜆BE is the total failure rate of bearing • 𝜆BE,B is base failure rate • 𝐶R is life adjustment factor for reliability • 𝐶V is multiplying factor for lubricant • 𝐶CW is multiplying factor for water contaminant level • 𝐶t is multiplying factor for operating temperature • 𝐶SF is multiplying factor for operating service conditions • 𝐶C is multiplying factor for lubrication contamination level. The total failure rate of control valve assemblies is given by: 𝜆VA = 𝜆PO + 𝜆SE + 𝜆SP + 𝜆SO + 𝜆HO , (3) where • 𝜆VA is the total failure rate of total valve assemblies • 𝜆PO is the failure rate of poppet assembly • 𝜆SE is the failure rate of the seals • 𝜆SP is the failure rate of spring(s) • 𝜆SO is the failure rate of solenoid • 𝜆HO is the failure rate of valve housing Consequently, using the failure data illustrated in [19] it is possible to solve equation (2)-(3). Then, the compressor failure rate could be estimated integrating these results into equation (1), as follow: 𝜆C = 1.56 ∙ 10 −5 failure/h (4) Usually, failure rates of components implemented in railway applications are expressed in failure/km or for sake of simplicity FPMK (Failure Per Million Kilometers). Moreover, the duty cycle of the compressor must be taken into account in order to obtain a more accurate evaluation. Consequently, considering an approximate annual distance for high-speed train of half a million kilometers and a duty cycle of 30 %, the failure rate of the compressor becomes: 𝜆Compressor = 8.22 ∙ 10 −2 FPMK . (5) The failure rate achieved above must be compared with the failure rate provided by the manufacturer of the component (MERAK) which is obtained integrating field data and internal company tests. 𝜆Compressor,merak = 7.79 ∙ 10 −2 FPMK . (6) Comparing equations (5) and (6) it is possible to note that the failure rate obtained using the compressor model provides a value slightly higher than the one provided by the compressor manufacturer. The several variables considered by the failure model in equation (1) produce the different results since the Merak value is based mainly on field data. Figure 2 shows the reliability curves relative to the failure rates of equations (5) and (6). The blue line represents the reliability calculated with the manufacturer “MERAK” failure rate while the red one the reliability calculated using the failure model of the compressor. The curve obtained using the model decreases faster because its failure rate is higher than the MERAK failure rate. Anyway, the difference of the two curves is limited, at the beginning the Figure 2. Reliability curves of the compressor assembly calculated through Merak data and compressor model given by equation (1). ACTA IMEKO | www.imeko.org September 2021 | Volume 10 | Number 3 | 103 curves are approximately equal then they decrease with different exponential decay rates. 3.2. Heat exchanger model Heat exchangers are essential part of any kind of HVAC system nowadays. The main function of a heat recovery system is to increase the energy efficiency by reducing energy consumption and also by reducing the cost of operating by transferring heat between two gases or fluids, thus reducing the energy consumptions. In heat exchangers, as the name suggests, there is a transfer of energy from one fluid to another. Both these fluids are physically separated and there is no direct contact between the fluids. There are different types of heat exchangers such as shell and tube, U tube, shell and coil, helical, plate etc. The transfer of heat can be between steam and water, water and steam, refrigerant and water, refrigerant and air, water and water. The HVAC system’s compressor generates heat by compressing refrigerant. This heat can be captured and used for heating domestic water. For this purpose, a heat exchanger is placed in between the compressor and the condenser. The water that is to be heated is circulated though this heat exchanger with the help of a pump whenever the HVAC system is on. The heat exchanger included in the system under test is composed by a tube and an expansion valve. The failure rate of a fluid conductor is extremely sensitive to the operating environment of the system in which it is installed as compared to the design of the pipe. Each application must be evaluated individually because of the many installation, usage and maintenance variables that affect the failure rate. The failure of a piping assembly depends primarily on the connection joints and it can be estimated with the following equation [16]: 𝜆P = 𝜆P,B ∙ 𝐶E = 2.2 ∙ 10 −6 failure/h (7) where • 𝜆P is the failure rate of pipe assembly • 𝜆P,B is the base failure rate of pipe assembly, which is 1.57 ∙ 10−6 failure/h • 𝐶E is the environmental factor equal to 1.4 in case of a railway application. For the expansion valve the failure rate is provided by [16] and it is 𝜆VA = 4.5 ∙ 10 −6 failure/h. Therefore, the whole failure rate of the heat exchanger is given by the sum of the failure rate of the pipe and the failure rate of the valve, each one weighted on its own duty cycle. In particular, the duty cycle of the pipe is 80 % while the duty cycle of the valve is 30 %. Consequently, the failure rate of the heat exchanger is given by: 𝜆heat exchanger = 3.11 ∙ 10 −6 failure h⁄ . (8) Quite the same as the compressor, also the failure rate of the heat exchanger must be converted from failure/h into FPMK. The heat exchanger failure rate in case of railway application is the following: 𝜆heat exchanger = 5.4 ∙ 10 −2 FPMK . (9) Also in this case, the failure rate based on field data has been provided by the component manufacturer MERAK and it is equal to: 𝜆heat exchanger,merak = 4.124 ∙ 10 −2 FPMK . (10) Figure 3 shows the two different reliability curves, the blue one is related to MERAK data while the red one is related to the model results achieved in equation (9). The model line results a pessimistic estimation also for this component. The difference between the two reliability trends is extremely low. This is mainly due to the fact that for a simpler element like the heat exchanger, the manufacturer MERAK and model lead to a similar result. 3.3. Blower model One of the most common downfalls of installed HVAC systems is their inability to distribute the correct amount of air to where it’s needed most. When systems are restrictive, or blowers aren’t powerful enough, the air simply doesn’t make it to where it needs to go. A blower is composed by: • an AC motor; • two bearings; • a fan. The failure rate of a motor is affected by such factors as insulation deterioration, wear of sliding parts, bearing deterioration, torque, load size and type, overhung loads, thrust loads and rotational speed. The failure rate model developed is based on a fractional or integral horsepower AC type motor. The reliability of an electric motor is dependent upon the reliability of its parts, which may include bearings, electrical windings, armature/shaft, housing, gears and brushes. Failure mechanisms resulting in part degradation and failure rate distribution (as a function of time) are considered to be independent in each failure rate model. The total motor system failure rate is the sum of the failure rate of each of the parts in the motor: 𝜆motor = 𝜆M,B ∙ 𝐶SF + 𝜆WI + 𝜆ST + 𝜆AS + 𝜆BE + 𝜆GR + 𝜆C, (11) where • 𝜆motor is the total failure rate for the motor system • 𝜆M,B is the base failure rate of motor • 𝐶SF is the motor load service factor • 𝜆WI is the failure rate of electric motor windings • 𝜆ST is the failure rate of the stator housing • 𝜆AS is the failure rate of the armature shaft • 𝜆BE is the failure rate of the bearing evaluated using equation (2) and the suitable factors • 𝜆GR is the failure rate of gears • 𝜆C is the failure rate of capacitor. The bearings failure rate could be estimated following the guidelines in section 3.1. The fans are modelled in according to MIL-STD-217F [37] by: Figure 3. Reliability curves of the heat exchanger assembly calculated through Merak data and heat exchanger model according to equation (9). ACTA IMEKO | www.imeko.org September 2021 | Volume 10 | Number 3 | 104 𝜆Fan = [ 𝑡 2 𝛼𝐵 3 + 1 𝛼𝑊 ] failure/h (12) where • t is the motor operating time period • 𝛼B the Weibull characteristics life for motor bearing • 𝛼W the Weibull characteristics life for motor windings Finally, the whole blower failure rate is given by the sum of the failure rates of its components, so: 𝜆Blower = 𝜆motor + 2 ∙ 𝜆bearing + 𝜆Fan = 1.328 ∙ 10−5 failure/h . (13) Then considering the duty cycle of 100 % and the required conversion from failure/h into FPMK, the blower failure rate become: 𝜆Blower = 2.33 ∙ 10 −1 FPMK (14) The failure rate achieved analyzing field data has been provided by the component manufacturer MERAK, and it is illustrated in the following: 𝜆Blower,merak = 7.97 ∙ 10 −2 FPMK . (15) Figure 4 shows the two reliability curves, the blue one is related to the MERAK data and the red one is related to the model data. Like for the other components, the reliability calculated through the models provides a pessimistic reliability trend respect the reliability calculated with the field data provided by MERAK. This time, the differences between field data and model data is quite remarkable. This could be due to the harsh operating condition considered by the failure rate model in [16]. 4. RELIABILITY ANALYSIS When the data (coming from tests or from the manufacturer) are available, techniques such as FTA or RBD could be used to estimate the useful life of the system. 4.1. Fault tree analysis Fault tree diagrams consist of gates and events connected with lines. The AND and OR gates are the two most commonly used gates in a fault tree. To illustrate the use of these gates, consider two events (called "input events") that can lead to another event (called the "output event")[14], [35]. If the occurrence of either input event causes the output event to occur, then these input events are connected using an OR gate. Alternatively, if both input events must occur in order to the occurrence of the output event, then they are connected by an AND gate. Fault tree analysis gates can also be combined to create more complex representations. In case of the HVAC system under analysis, the top event is “HVAC failure” and it’s caused by four different events, as illustrate in Figure 5: • “Possible fire”, when some events could involve a risk of fire in the railway cabin. • “Loss of emergency ventilation”, when the emergency ventilation doesn’t work. • “Loss of functions caused by a single event”, when a single event causes a direct loss of all the cooling, heating, ventilation function • “Indirect loss of cooling heating and ventilation”, when some events cause independently a loss of cooling, heating and ventilation functions. The top event “HVAC failure” is linked to the above- described input events trough an OR gate, that means if at least one of the four input events happens the whole system fails. Every one of the input events in Figure 5 is in turn caused by an extremely complex combinations of several events. The complete FTA diagram is very large and structured, and it is not possible to show it entirely. So, for the sake of simplicity, Figure 5 shows only an extract of these FTA. The reliability trend considering the FTA configuration is shown in Figure 6. The curve is a decreasing exponential, it starts from a unitary reliability and it tends to zero. The analysis is simulated starting from 0 km up to 6∙106 km. According to an annual forecast distance traversed of about 487∙103 km, the simulation in term of time is over 12 years. At distance 0.5∙106 km (approximately 1 year) the reliability is around the 80 %, while after 1∙106 km (approximately 2 years) is decrease approximately to the 60 % and then it tends to zero at 5∙106 km (approximately 10 years). These results are justified by two reasons: • The mechanical nature of the whole system, that contributes to a fast decrease of the reliability. • The OR gate that lead to the top event, which is the worst-case scenario between the several ones considered during the design. 4.2. Reliability block diagram An overall system reliability prediction can be made by looking at the reliabilities of the components that make up the whole system or product. In order to construct a reliability block diagram, the reliability-wise configuration of the components must be determined. Consequently, the analysis method used for computing the reliability of a system will also depend on the reliability-wise configuration of the components/subsystems. Figure 4. Reliability curves of the blower assembly calculated through Merak data and blower model as in equation (14). Figure 5. Extract of the FTA Diagram for the HVAC system under analysis. ACTA IMEKO | www.imeko.org September 2021 | Volume 10 | Number 3 | 105 That configuration can be as simple as units arranged in a pure series or parallel configuration. There can also be systems of combined series/parallel configurations or complex systems that cannot be decomposed into groups of series and parallel configurations. The HVAC system under analysis could be described using a series of three main blocks (see Figure 7): • Cooling system • Heating system • Ventilation system. Therefore, supposing the exponential distribution for all the items [18], [38], [39], the reliability equation of the whole system is: 𝑅SYS(𝑡) = e −(𝜆cooling+𝜆heating+𝜆ventilation)∙𝑡 . (16) Figure 8 shows a comparison between cooling, heating and ventilation reliability curves. The figure also shows the whole system reliability trend calculated with equation (16), which is illustrated using a dashed black line. The red line represents the heating system, the blue line the ventilation system and the green line the cooling system. The worst system, in reliability terms, is the cooling system, because it contains a lot of series elements and most of them are mechanical items. The three systems are connected in a series configuration, where the component with the least reliability has the biggest effect on the system's reliability. As a result, the reliability of a series system is always less than the reliability of the least reliable component. That’s why the black line, representing the whole system reliability is lower than the cooling curve (the least reliable). 4.3. Comparison between FTA and RBD results Table 2 shows the comparison of the reliability trends between the two proposed methods: Reliability Block Diagram and Fault Tree Analysis. The two curves are very similar, but the RBD reliability is always higher than the FTA results (at every distance). The differences could be caused by: • Different algorithm used by the software for the calculation of the reliability. • The FTA analysis results are more complete and they consider all the possible path that lead to the top event, so it considers also the relationship between failures. The first column of Table 2 reports the distance travelled by the train, the second the corresponding time, the third and the fourth the reliability values of the FTA and RBD respectively. The last one reports the absolute percentage difference of the two previous columns. All the values relative to the RBD are higher than the FTA, but their difference is lower than 6 %. Figure 9 shows the trend of the difference between the two curves, it illustrates that the maximum value is 6.5 %, so the difference is very low. At the beginning the difference is not remarkable, in particular before 1000000 km is lower than 4 %, then it increases, and the peak is between 1500000 km and 2500000 km where the difference is 6 %. After that, it decreases slowly and it reaches the value of 2 % at 6000000 km. Therefore, the two methods provide comparable results, and both the outcomes are valid. 5. COMPARISON BETWEEN FIELD DATA AND MODEL DATA A comparison between the failure estimation of the previous paragraphs and the failure data provided by the manufacturer of the HVAC has been carried out in order to investigate how the model-based failure rates affect the reliability trend of the whole system. The model-based failure rates of compressor, blower and heat exchanger are used to calculate the whole HVAC reliability together with the failure rate estimation of the other components, which make up the system. Figure 6. Reliability trend of the FTA. Figure 7. Reliability block diagram of the HVAC system. Figure 8. Reliability trends of cooling, heating and ventilation systems (continuous lines) compared with the HVAC system reliability (dashed line). Table 2. Reliability data of OR type FTA and RBD. Distance km time Rfta Rrbd Difference 0.5 ∙ 106 1 year 0.78 0.80 3 % 1 ∙ 106 2 years 0.60 0.64 4 % 2 ∙ 106 4 years 0.34 0.40 6 % 3 ∙ 106 6 years 0.18 0.24 6 % 4 ∙ 106 8 years 0.1 0.14 4 % 5 ∙ 106 10 years 0.05 0.08 3 % 6 ∙ 106 12 years 0.03 0.05 2 % ACTA IMEKO | www.imeko.org September 2021 | Volume 10 | Number 3 | 106 Figure 10 shows three reliability curves, the blue trend is related to the FTA reliability, the red one is calculated with the RBD analysis, while the green one is related to the failure rate of the components calculated using the failure models. It’s possible to note that the model-based failure rates contribute to reduce the reliability and have an important contribution to the whole system reliability. The failure rate models provide a pessimistic reliability results for the three components analyzed before. Consequently, their reliability curves affect the whole system reliability, producing a trend lower than the ones calculated with the manufacturer data (in case of both FTA and RBD techniques. 6. CONCLUSION The paper deals with a heating, ventilation and air conditioning system mounted on a high-speed train. The first part of the paper illustrates the taxonomy of the system under study. The architecture of an HVAC system includes several critical components, such as: a fan (blower), a heat exchanger and a compressor. A detailed study on the failure rates of the HVAC most critical components is presented in this paper. Compressor, heat exchanger and blower show a model-based reliability lower than the reliability achieved using the field data provided by the manufacturer of the HVAC “MERAK”. Then, the reliability of the complete HVAC system has been estimated using two well- known techniques: Fault Tree Analysis and Reliability Block Diagram. FTA and RBD methods take the field data provided by MERAK as input to evaluate the system reliability over distance travelled by the train. The final analysis shows how the model failure rates affect the whole HVAC reliability comparing the results achieved using FTA and RBD with the one obtained using the failure rate models. The model-based failure rate provides a pessimistic result because it considers every possible failure modes and failure mechanisms of each subitem that make up the component. Despite this. it could be not so realistic since it doesn’t properly consider the real operating conditions of the system under test. Quite the opposite, the reliability evaluated using the field data takes into account the real context of the HVAC but some of the failure mechanisms might be not occur during the observed time interval. 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