scholarly journals Estimate the useful life for a heating, ventilation, and air conditioning system on a high-speed train using failure models

ACTA IMEKO ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 100
Author(s):  
Marcantonio Catelani ◽  
Lorenzo Ciani ◽  
Giulia Guidi ◽  
Gabriele Patrizi ◽  
Diego Galar
Keyword(s):  
Failure Rate ◽  
Air Conditioning ◽  
High Speed ◽  
Field Data ◽  
Block Diagram ◽  
Reliability Assessment ◽  
Fault Tree Analysis ◽  
Reliability Prediction ◽  
Rate Model ◽  
Mechanical Components

<p class="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.</p>

Reliability Prediction for Offshore Renewable Energy: Data Driven Insights

2017 ◽  
Author(s):  
Fraser J. Ewing ◽  
Philipp R. Thies ◽  
Benson Waldron ◽  
Jonathan Shek ◽  
Michael Wilkinson
Keyword(s):  
Renewable Energy ◽  
Failure Rate ◽  
Reliability Assessment ◽  
Surrogate Data ◽  
Data Sources ◽  
Reliability Prediction ◽  
Failure Rates ◽  
Onshore Wind ◽  
Energy Devices ◽  
Frequency Converters

Accurately quantifying and assessing the reliability of Offshore Renewable Energy (ORE) devices is critical for the successful commercialisation of the industry. At present, due to the nascent stage of the industry and commercial sensitivities there is very little available reliability field data. This presents an issue: how can the reliability of ORE’s be accurately assessed and predicted with a lack of specific reliability data? ORE devices largely rely on the assessment of surrogate data sources for their reliability assessment. To date there are very few published studies that empirically assess the failure rates of offshore renewable energy devices [1]. The applicability of surrogate data sources to the ORE environment is critical and needs to be more thoroughly evaluated for a robust ORE device reliability assessment. This paper tests two commonly held assumptions used in the reliability assessment of ORE devices. Firstly, the constant failure rate assumption that underpins ORE component failure rate estimations is addressed. Secondly, a model that is often used to assess the reliability of onshore wind components, the Non-Homogeneous Poisson Power Law Process (PLP) model is empirically assessed and trend tested to determine its suitability for use in ORE reliability prediction. This paper suggests that pitch systems, generators and frequency converters cannot be considered to have constant failure rates when analysed via nonrepairable methods. Thus, when performing a reliability assessment of an ORE device using non-repairable surrogate data it cannot always be assumed that these components will exhibit random failures. Secondly, this paper suggests when using repairable system methods, the PLP model is not always accurate at describing the failure behaviour of onshore wind pitch systems, generators and frequency converters whether they are assessed as groups of turbines or individually. Thus, when performing a reliability assessment of an ORE device using repairable surrogate data both model choice and assumptions should be carefully considered.

Analysis on the Influence Factors of Smart Meters Reliability Prediction

2014 ◽  
Vol 602-605 ◽  
pp. 862-865
Author(s):  
Jing Ji ◽  
Xing Zhe Hou ◽  
Juan Tian ◽  
Yi Wang ◽  
Fu Ping Zhao ◽  
...  
Keyword(s):  
Failure Rate ◽  
Influence Factors ◽  
Practical Method ◽  
Reliability Prediction ◽  
Rate Model ◽  
Smart Meters ◽  
Improvement Measure ◽  
Major Factors

Currently, parts stress method is the most suitable and practical method for smart meters reliability prediction. This paper establishes the components’ failure rate model, selects parameters based on SR-332 handbook, analyzes the major factors which affect the components and meters reliability prediction results, and proposes the improvement measure to enhance the reliability of smart meters.

Systematic Methodology for Reliability Analysis of Components in Axial Piston Units

2019 ◽  
Author(s):  
Ivan Baus ◽  
Robert Rahmfeld ◽  
Andreas Schumacher ◽  
Henrik C. Pedersen
Keyword(s):  
Reliability Analysis ◽  
Field Data ◽  
Failure Modes ◽  
Reliability Assessment ◽  
Fault Tree Analysis ◽  
Real Field ◽  
Risk Priority Number ◽  
Design Assessment ◽  
Theoretical Design ◽  
Axial Piston

Abstract This paper covers a reliability analysis as a qualitative method, especially focused on axial piston units. The method is based on Fault Tree Analysis (FTA) and results in risk and reliability assessment at the components level. Especially, the development of the reliability assessment as a methodical tool is the core of the paper. Moreover, the FTA is combined with the industrial standard method known as Design Failure Mode Effects Analysis (DFMEA) which is typically used in the development phase of the design. The evaluation and the usability of the FTA methodology is analyzed in connection with field data. Thus, the deviation of the theoretical valuation from the field data was utilized as a success indicator of the method. The analysis of the fault spreading covers the assessment of component faults and links failure states with unit effects. The analysis of the axial piston unit as a system is made on idealized/theoretical design and functional behavior only. Hence, the failure rating and the effect is subsequently applied to determine the fault risk in form of the Risk Priority Number (RPN). The failure modes and effects are based on engineering experience of past decades, supported by existing DFMEAs of axial piston units. Thus, the assessment of the risk priority number is based on previous data, yielding the given severity, occurrence and detection quantification. This approach opens new opportunities of design assessment and the results show a good agreement to the damage accumulation seen in real field data. Furthermore, the connection between theoretical design assessment and field data do support the failure ranking improvement of the DFMEA.

Reliability Reviewed

1979 ◽  
Vol 193 (1) ◽  
pp. 81-92 ◽  
Author(s):  
A. D. S. Carter
Keyword(s):  
Mathematical Model ◽  
Design Process ◽  
Failure Rate ◽  
Field Data ◽  
Safety Margin ◽  
Maintenance Activity ◽  
Logical Specification ◽  
Mechanical Components ◽  
Reliable Design

Using a conventional mathematical model it has been shown that a component can be designed so that it is ‘intrinsically reliable’ i.e. the failure rate will be zero for all practical purposes so long as the strength of the item remains constant. Failure can then only take place by wear-out as the strength deteriorates. Reference to field data shows that most, though by no means all, existing mechanical components conform to this pattern but fewer electronic items do. The behaviour of items which are not intrinsically reliable is shown to be substantially indeterminate and hence not amenable to assessment. It is claimed that the concept of intrinsic reliability leads to a more rational and quantifiable approach to the best existing practice. For example, the minimum safety margin for intrinsic reliability can be calculated in terms of loading roughness. No empirical subjective factors are necessary in the design process. Leading from the concept, recommendations are made for a more logical specification of reliability and more positive methods of interpreting prototype trials. Given an intrinsically reliable design, achieving reliability in the field depends on avoiding wear-out, i.e. on the maintenance activity. Consequently the roles of the designer and maintainer can to some extent be separated, allowing a more definite allocation of responsibilities.

Reliability Distribution Law and Statistics Analysis for High-Speed EMU

2014 ◽  
Vol 633-634 ◽  
pp. 1213-1219
Author(s):  
Zhi Guo Cui ◽  
Hao Dong
Keyword(s):  
Failure Rate ◽  
High Speed ◽  
Design Method ◽  
Reliability Assessment ◽  
Statistical Testing ◽  
Distribution Law ◽  
Reliability Design ◽  
Data Statistics ◽  
Time Of Operation ◽  
The Given

The reliability design method is considered based on the EMU application in our country. It is verified that the vehicle reliability obeys the exponential distribution according to the statistical testing principles and practical data statistics of some high-speed EMU, in addition, the reliability after a period of time of operation, or to say, the operational time which meet demand of the certain reliability requirement, is predicted. The reliability assessment for the EMU operating for a period of time is proceed on the basis of the suggestion table of failure rate levels obtained through quantities of statistics. Apart from this,suggestions about reliability design is proposed based on the given condition of failure rate levels. The method can be used as one of reliability assessment indicator of EMU in the future.

scholarly journals Dependability Measures Estimation Using Binomial Failure Rate Model

2017 ◽  
Vol 2 (4) ◽  
pp. 199-206
Author(s):  
Mourad CHEBILA ◽  
Fares INNAL
Keyword(s):  
Failure Rate ◽  
Parameter Uncertainty ◽  
Factor Model ◽  
Fault Tree Analysis ◽  
Rate Model ◽  
Common Cause ◽  
Modeling Process ◽  
Component Systems ◽  
Failure Intensity ◽  
Common Cause Failures

Dependability of multi-component systems is highly impacted by common cause failures, what necessitates the appropriate consideration of such events in the dependability modeling process. This paper is dedicated to study the application of the binomial failure rate model in handling the contribution of common cause failures to estimate two key dependability indicators, namely: unavailability and unconditional failure intensity, using fault tree analysis with the probabilistic treatment of the associated parameter uncertainty. The results of such application are thoroughly compared to those of the traditional Beta factor model to highlight the possible differences.

Modelling weather dependence in online reliability assessment of power systems

2017 ◽  
Vol 231 (4) ◽  
pp. 364-372 ◽  
Author(s):  
Samuel Perkin ◽  
Arne Brufladt Svendsen ◽  
Trond Tollefsen ◽  
Ingrid Honve ◽  
Iris Baldursdottir ◽  
...  
Keyword(s):  
Power Systems ◽  
Failure Rate ◽  
Assessment Tool ◽  
Risk Estimation ◽  
Reliability Assessment ◽  
Low Frequency ◽  
Skewed Distribution ◽  
Weather Data ◽  
Rate Model ◽  
Blind Test

Probabilistic reliability assessment of power systems is an ongoing field of research, particularly in the development of tools to model the probability of exogenous threats and their potential consequences. This paper describes the application of a weather-dependent failure rate model to a region of the Icelandic transmission system, using 10 years of weather data and overhead line fault records. The studied failure rate model is compared with a constant failure rate model, in terms of variability and how well the models perform in a blind test over a 2 year period in reflecting the occurrence of outages. The weather-dependent and constant failure rate models are used as input to a state-of-the-art risk assessment tool to determine the sensitivity of such software to weather-dependent threats. The results show the importance of weather-dependent contingency probabilities in risk estimation, and in quantitative assessment of maintenance activities. The results also demonstrate that inclusion of weather dependence in power system reliability assessments affects the overall distribution of risk as a positively skewed distribution, with high-risk periods occurring at low frequency.

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