RELIABILITY AND RISK ANALYSIS
Ensures system functionality and manage risk
Create reliable products and extend asset performance
Performance and time to market is significantly improved by integrating reliability into product-, system-, and process development from its earliest design stages, whether safety-critical industrial equipment or consumer items.
- Design for reliability: Most companies already practice reliability engineering within design, whether formally or informally. However, implementing a structured reliability programme can significantly improve your business.
- Asset Performance Management: This is becoming more and more important with the increasing complexity of systems and sophistication of the methods available for determining their reliability.
- Reliability, Availability, Maintainability & Safety (RAMS): RAMS Analysis focuses on the availability and safety performance of systems subjected to failure modes.
Reliability Engineering predicts how reliable the products are going to be and strategies for more effective maintenance to optimize and extend asset performance
Capabilities
FMEA / FMECA
Methodologies designed to identify potential failure modes for a product or process before the problems occur.
Failure Mode and Effect Analysis (FMEA) and Failure Modes, Effects and Criticality Analysis (FMECA) are methodologies designed to identify potential failure modes for a product or process before the problems occur, to assess the risk associated with those failure modes and to identify and carry out measures to address the most serious concerns.
Although the purpose, terminology and other details can vary according to type (e.g. ProcessFMEA – PFMEA, Design FMEA – DFMEA, System FMEA, Product FMEA and FMECA), the basic methodology is similar for all. The fundamental steps, from assembling the team and gathering relevant information, to evaluating the risk from issues identified and distributing the information, can be facilitated by our comprehensive FMEA / FMECA training and consulting services and ReliaSoft’s comprehensive XFMEA software.
Our Reliability Engineers have extensive experience of hosting FMEA and FMECA workshops in the Design, Process or Functional context. The workshops are performed to Industry specified requirements either as stand-alone activities or pre-cursors to RCM or Fault Tree activities.
Life Data Analysis / Accelerated Life Testing
Estimate life characteristics of a product such as reliability or probability of failure at a specific time.
Every company encounters problems that need solving, whether they are catastrophic such as; the Chernobyl reactor explosion, the Exxon Valdez oil spill, the Union Carbide cyanide gas leak; or of a more common nature such as; turn-around times are too slow, too many warranty returns, production quality isn’t good enough.
Due to the long life-times of today’s products it can be difficult for an engineer to analyse the time-to-failure data obtained under normal operating conditions. However, Life Data Analysis (Weibull Analysis) enables you to predict the life of a product by fitting a statistical distribution to life data from a representative sample of units. This data set can then be used to estimate important life characteristics of the product such as reliability or probability of failure at a specific time, the mean life and the failure rate. ReliaSoft Weibull++ software is extremely popular for a wide range of statistical analysis.
In typical life data analysis, the practitioner analyzes life data from a sampling of units operated under normal conditions. This analysis allows the practitioner to quantify the life characteristics of the product and make general predictions about all of the products in the population. For a variety of reasons, engineers may wish to obtain reliability results for their products more quickly than they can with data obtained under normal operating conditions. As an alternative, these engineers may use quantitative accelerated life tests to capture life data under accelerated stress conditions that will cause the products to fail more quickly without introducing unrealistic failure mechanisms.
Our Reliability team supports clients through a number of Life Data Analysis and Accelerated Life Testing activities including Warranty Analysis, advice and recommendations on what data to be capturing to enable Life Data Analysis, Determining product reliability, product mean life or failure rate to support System Reliability Modelling. Our engineers support the development of Product Test planning, the determination of the number of samples to be tested and the level to which stressor levels are to be applied.
System Reliability Modeling
Improve or optimise overall system reliability, maintainability and/or availability using logic diagrams.
A system is a collection of subsystems, assemblies and/or components arranged in a specific design in order to achieve desired functions with acceptable performance and reliability. The types of components, their quantities, their qualities and the manner in which they are arranged within the system have a direct effect on the system’s reliability. Therefore, in addition to the reliability of the components, the relationship between these components is also considered and decisions as to the choice of components can be made to improve or optimize the overall system reliability, maintainability and/or availability. This reliability relationship is usually expressed using logic diagrams, such as reliability block diagrams (RBDs) and/or fault trees.
Supporting both product design and asset management functions, our Engineers develop and analyse system Reliability Block Diagrams and Fault Trees, usually as part of a Reliability project. These models can be made-up from a variety of sources of data from standards-based libraries, to real-world test/field data or from Supplier’s product technical specification data.
Using exact computations or discrete event simulation, our Engineers use ReliaSoft’s BlockSim software to undertake a wide variety of analyses for both repairable and non-repairable systems. This includes reliability analysis, reliability optimization, and sensitivity studies, exploring the effect of redundancy on improved reliability against increased cost.
Standards Based Prediction
Predict reliability for systems and components (mostly electronics) based on failure rate estimates.
Standards based reliability prediction is a methodology for predicting reliability for systems and components (mostly electronics) based on failure rate estimates published by globally recognized military or commercial standards. Standards based reliability prediction is especially useful in the initial stages of development when hard failure data is not yet available or when manufacturers are obliged contractually by their customers to use published standards for their reliability predictions.
Using ReliaSoft’s Lambda Predict software, our engineers can predict the reliability of your system to several standards including: MIL 217, FIDES, Bellcore/Telcordia; Siemens SN29500 and NSWC. Results are often subsequently used in System Reliability Models where test or manufacturer’s data is unavailable
Reliability Growth Analysis
Apply reliability growth models to analyze data from both developmental testing and fielded repairable systems.
A well-structured reliability growth programme helps you find reliability problems by testing, incorporating corrective actions and monitoring the increase of the product’s reliability throughout the test phases. Reliability growth analysis is the process of collecting, modeling, analysing and interpreting data from this programme, whether sourced from development testing and/or collected from the field.
Reliability growth is the improvement in the reliability of a product (component, subsystem or system) over a period of time due to changes in the design and/or manufacturing process.
Working with Reliability and Test Engineers, our Reliability team can support the development of reliability goals, processes and test plans, thereby providing confidence in reliability growth projections and calculating optimum overhaul times for repairable systems.
Reliability Centered Maintenance
Analyse functions and potential failures for a physical asset with a focus on preserving system functions.
Reliability Centered Maintenance (RCM) analysis provides a structured framework for analyzing the functions and potential failures for a physical asset (such as an airplane, a manufacturing production line, etc.) with a focus on preserving system functions, rather than preserving equipment. RCM is used to develop scheduled maintenance plans that will provide an acceptable level of operability, with an acceptable level of risk, in an efficient and cost-effective manner.
Our Reliability Engineers have extensive experience of hosting FMEA, FMECA and RCM workshops in the Design, Process or Functional context. The workshops are performed to Industry specified requirements either as stand-alone activities or as part of a Reliability and Asset Management programmes.
Reliability growth is the improvement in the reliability of a product (component, subsystem or system) over a period of time due to changes in the design and/or manufacturing process.
FRACAS / DRACAS
Report failures and/or defects and track their corrective actions
Failure Reporting Analysis and Corrective Action System and Defect Report Analysis and Corrective Action System are commonly used terms for a system that is used to report failures and/or defects and track their corrective actions. Other commonly used names include DRACAS (Data Reporting Analysis and Corrective Action System), DCACAS (Data Collection Analysis and Corrective Action System) and CAPA software (Corrective And Preventive Action software).
For every installation of ReliaSoft’s XFRACAS software for FRACAS or DRACAS applications, our Reliability team works closely with our Clients to help configure and implement the software into the Client’s FRACAS/DRACAS processes. Our team can help develop the process if one isn’t already in place.
A key aspect of our work is the mass import of data into the XFRACAS system from external sources using a number of Data Analytics methods and the management of data that can interact with other Client systems.
Design of Experiments (DOE)
Assess the relationship between different parameters influencing a product or process and their effect on performance.
When variation is anticipated or to be identified during testing, good experimental design is fundamental. By the careful selection of variables and the experiments that are consequently performed, your designs can be investigated and optimised effectively.
For both physical (prototype / shop trial) and virtual (simulation) testing, we offer dedicated software to assist the selection of variables and analysis of results. ReliaSoft software can be applied to a wide range of physical and virtual testing techniques, while there are integrated DOE modules for ANSYS simulation products. For both solutions, we can provide comprehensive training and consulting services.
Logistics & Spares Modeling
Specify both the direct and indirect costs associated with maintenance strategies that you have defined.
Using ReliaSoft’s BlockSim software as our tool of choice, BlockSim allows you to specify both the direct and indirect costs associated with the maintenance strategies that you have defined, including costs related to downtime, maintenance crews, spares, etc. This yields a wide array of simulation results that are instrumental in performing realistic Life-Cycle Cost assessments. With BlockSim’s modeling flexibility, you can:
- Specify what kinds of crew delays are included in cost calculations and what delays should be ignored.
- Specify costs associated with system failure, including cost per incident and downtime rate.
- Specify system uptime revenue and revenue due to throughput so the simulation is able to calculate opportunity costs.
View new cost-related simulation results, including system-level costs, the contributions of different kinds of wait times to block costs and the contribution (criticality) of a block’s cost to the total system costs.
Our Reliability Engineers have extensive experience of hosting FMEA and FMECA workshops in the Design, Process or Functional context. The workshops are performed to Industry specified requirements either as stand-alone activities or pre-cursors to RCM or Fault Tree activities.
Design for reliability
Design for Reliability (DfR) can be described as a systematic, streamlined, concurrent engineering programme that supports product and process design (typically from early in the concept stage all the way through to product obsolescence) to ensure that customer expectations for reliability are fully met throughout the life of the product with low overall life-cycle costs. It relies on an array of reliability engineering tools along with a proper understanding of when and how to use these tools throughout the design cycle.
The DFR process encompasses a variety of techniques and practices, describing the overall order of deployment that an organisation needs to follow in order to design reliability into its products. This process is supported by ReliaSoft software, comprising tools that integrate for storing and transferring data between applications through a centralised database.
Capabilities include:
Failure Modes Effects (Criticality) Analysis (FMEA/FMECA) facilitation, life data analysis (Weibull), MTBF studies, Accelerated Life Testing (ALT), warranty analysis, system-level analysis with Fault Tree Analysis (FTA) and Reliability Block Diagrams (RBDs) and reliability growth prediction.
Industry standards
Support of many FMEA industry standards, including SAE J1739, IEC 60812, MIL-STD-1629A and ISO 14971. For standard-based reliability assessments, we are highly familiar with MIL-HDBK-217F, Bellcore/Telecordia & FIDES in addition to other standards such as MIL-M-38510, NPRD-95 and EPRD-97.
Asset Performance Management
Applying Reliability Engineering Tools and Methods to APM
Understanding when, how and where to use the wide variety of available reliability engineering tools will help to achieve the reliability mission of an organization. This is becoming more and more important with the increasing complexity of systems and sophistication of the methods available for determining their reliability.
With increasing complexity in all aspects of asset performance management, it becomes a necessity to have a well-defined process for integrating reliability activities. Without such a process, trying to implement all of the different reliability activities involved in asset management can become a chaotic situation in which reliability tools may be deployed too late, randomly or not at all. This can result in the waste of time and resources as well as a situation in which the organization is constantly operating in a reactive mode.
Reliability, Availability, Maintainability & Safety (RAMS)
RAMS Analysis focuses on the availability and safety performance of systems subjected to failure modes. By applying effective reliability techniques, together with dedicated software, we can help you make informed decisions regarding risk, efficiency, repair and maintenance during FEED or when addressing limitations of existing or expanding operations.
We are highly experienced with techniques such as FMEA/FMECA, Fault Tree Analysis (FTA), Reliability Block Diagrams (RBDs) and Common Cause Failure analysis. We can identify critical components and their reliability importance measures while determining optimum preventive maintenance intervals and spare parts provisions. System availability analysis enables us calculate uptime and downtime while throughput calculations identify bottleneck and estimate production capacity.
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AR&M for Defence
A seamless flow of data between engineering disciplines for complying with defence standards, ensuring maximum efficiency and enabling an improved way of handling dependability data.
- A complete AR&M and DRACAS software solution
- Satisfies requirements of DEFSTANs 00-40,-42, -44 & -45
- Implementation support & consulting
- Training designed for your specific needs
The need to effectively manage the dependability of complex systems throughout their lifecycle has never been greater, especially in the defence sector where equipment is required to work when called upon. The demand to comply with standards such as Defence Standards 00-42, 00-44 and 00-45 is coming from the UK MOD and the responsibility is being assigned throughout the supply chain.
The influx of large quantities of data from both test environments and the field has helped us gain insight into the shape and scale of our dependability problems but handling this data to ensure that it is accurate, up to date and available to those who need it has created its own difficulties. This can lead to isolated pools of information that are challenging to track throughout the lifecycle.
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