Reliability Predictions are used to estimate the predicted failure rate or MTBF of a system based on widely accepted standards that use statistical-based equations to model the failure rates of the system components. In the first part of our series, the standards and their underlying models were explained. In this second part of our series, we’ll discuss the additional features Relyence Reliability Prediction incorporates to make your prediction analyses effective and efficient.
The Relyence Dashboard is one of the highlights of the Relyence tool suite. Providing insight into all your reliability and quality metrics at a glance, the Relyence Dashboards enable you to keep an eye on your system at a high level to monitor any and all aspects you deem critical. Importantly, Relyence Dashboards allow you to act proactively to issues before they are out of control. A key advantage of the Relyence Dashboard is the ability to turn insight into action.
RBDs provide the ability to model complex systems, including those that incorporate redundancy. The high-powered Relyence reliability tool suite supports RBDs through our Relyence RBD module. Relyence RBD combines a visually impressive, easy-to-use diagramming front end with a powerful behind-the-scenes calculation engine.
Reliability Predictions offer a key advantage as part of your overall reliability analysis toolset. Reliability Predictions are used to estimate the predicted failure rate or MTBF of a product or system during any portion of the product lifecycle. Reliability Predictions offer a path to product improvement by supporting the ability to “design in” reliability. Part 1 of our series delves into the specifics of reliability prediction standards.
One of our most anticipated releases, Relyence 2017 Release 2 is now available. This release builds on our solid engineering application toolset to round out our product line with new impressive modules and a host of new helpful features.
We are proud to introduce the exciting new Relyence RBD (Reliability Block Diagram), as well as to announce support for the Telcordia, Chinese, and MIL-HDBK-217 Parts Count reliability prediction standards. These new developments add even more power and capability to our suite of FMEA (Failure Mode and Effects Analysis), FRACAS (Failure Reporting, Analysis, and Corrective Action System), Fault Tree Analysis (FTA), and Reliability Prediction based on MIL-HBDK-217 products.
Review all changes prior to our release of Relyence Reliability Prediction in August of 2017 that includes changes such as reorganization of existing Part Types and Part Subtypes, additional features to our Reliability Prediction solution, and the release of additional Reliability Prediction Calculation Models such as MIL-HDBK-217F Notice 2 Parts Count, Telcordia Issue 4, and Chinese GJB/z 299C.
Fault Tree Analysis, sometime abbreviated as FTA, is a methodology used to determine the probability that an unwanted event will occur. The “unwanted event” is typically considered to be some type of failure of a product, system, process, or an issue of any kind. The undesired events can be major, life-threatening incidents, such as the crash of an airliner; other critical events, such as a cellphone catching fire; or even less crucial failures, such as my personal drone crashing on landing.
Reliability predictions can be a useful tool in your engineering toolkit. They can be part of your overall strategy for ensuring your product reliability goals are being met, and can aid in your continual product improvement objectives. Though reliability predictions tend to be utilized most often in early design stages, they can be used throughout the product lifecycle to provide insight into the reliability of your product.
The Relyence Dashboard provides a concise, visual overview of your quality and reliability metrics allowing you to quickly assess system health. Combining cross-module analytics, the Relyence Dashboard offers the ability to monitor all your quality activities at a high level, while providing drill-down capabilities for in-depth review.
As the amount of data in your Relyence Analyses grows, it becomes helpful to filter your data views. Filtering your views enables you to pare down the amount of information displayed in your Tables at one time. This allows you to work more efficiently by not requiring scrolling through data or reading a lot of extraneous information not critical to the task at hand.
Relyence FRACAS offers two unique and fully customizable capabilities to enable you to control and monitor your corrective action activities: Approvals and Notifications. You can optionally use either or both of these features, and set them up to meet your specific requirements. Used in conjunction with one another, they offer a powerful way to enhance your FRACAS process for your team.
Relyence enables you to effectively manage your team members and their associated permission-based roles through the Manage Users and Groups function. You can add team members, define your Relyence groups, assign team members to the appropriate groups, and control team member access to your Relyence modules, or to specific functions within those modules. The level of granularity is completely customizable, and also easily modifiable – so as needs change, your team member roles can adapt accordingly.
What’s in a name? Or, more appropriately, what’s in an acronym? In the corrective action and quality management realm a host of acronyms abound: CAPA, FRACAS, CAR, PAR, FRB, NCR, SCAR, CPAR, RCA, IFR, DRACAS, PRACA, PRACAS, CI, QMS. To makes sense of this alphabet soup, take a look at our breakdown, along with a deeper dive into two often seen: CAPA (Corrective and Preventive Action or sometimes Corrective Action/Preventive Action) and FRACAS (Failure Reporting, Analysis, and Corrective Action System).
FMEAs, or Failure Mode and Effects Analyses, play a vital role in today’s quality-centric organizations. FMEA goals align directly with the goals of quality control (QC) and continuous improvement (CI) objectives. In some cases, FMEAs are a required component of compliance standards. Even if not required for compliance reasons, oftentimes suppliers are required to perform and submit completed FMEAs as part of contractual requirements. In many cases, organizations adopt FMEAs as part of their own internal procedures in order to meet and improve quality objectives. Whether you are new to FMEAs, or are looking to improve your current analyses, it is helpful to consider three important elements: controls, continuous improvement, and collaboration.
Relyence Corporation has just released the latest version of the Relyence Quality and Reliability platform. Offered as either a cloud-based or on-premise solution, Relyence incorporates the latest in technology with the best in R&M analytics for a comprehensive suite to meet and improve corporate quality objectives.
The Relyence suite is comprised of FMEA (Failure Mode and Effects Analysis), CAPA/FRACAS (Corrective and Preventive Action/Failure Reporting, Analysis, and Corrective Action System), Fault Tree Analysis, and Reliability Prediction modules.
Relyence 2016 Release 2 introduces a host of new powerful features and capabilities to the Relyence suite.