Failure Analysis Leveraging Prognostic Technology for Moving to the 100% "Measured" Reliability Domain, 100% Availability, 100% of the Time

if your equipment or products are too expensive or too important to fail, We offer the most robust prognostic (preventive) algorithms for measuring the usable life of electrical, mechanical and electro-mechanical equipment and products for preventing failures

Prognostic technology is for preventing catastrophic failures rather than react to them after they occur. It includes predictive algorithms that relate past behavior to future events.

Our algorithms illustrate the prognostic markers present in normal appearing data from fully functional equipment/products for prognosticians to identify and convert the performance data into a measurement of the equipment remiaming usable life.

Measuring equipment usable life from common performance data from equipment replaces the product reliability paradigm that says that 100%reliability is unachievable. This paradigm was created decades ago by using reliability analysis engineering and quality control and quality improvement programs that offers results in probabilistic values.

Probabilistic reliability is the likelihood of success which is unrelated to real world reliability.

We can embed predictive algorithms to self-prognose usable life offering a new paradigm in high reliability products and services.

Prognostic technology was created by companies wanting to identify the products that were going to fail prematurely that often frustrated customers and reduced profits.

We offer product specific algorithms or we will embed our own algorithms to provide product remaining usable life information external to the product.

100% "measured" reliability means that equipment and products have had their reliability, physically measured invasively, using prognostic algorithms.

Prognostic algorithms illustrate the early signs of premature failure/aging also known in different industries by a variety of other lables such as prognostic markers, prognostic identifiers, failure precursors, deterministic behavior, cannot duplicates (CND), no failure found (NFF), no failure identified (NFI) and accelerated aging. Specially trained personnel will identify the behavior from other normal appearing data from fuly functional equipment.

We offer the only prognostic algorithms for equipment and products working in a full RF and/or electrical noise environment that corrupts the integrity of the product data used for completing a prognostic analysis.

MULTI-INDUSTRY APPLICATIONS

Semi-Conductor Industry: Product reliability is measured before shipping with a prognostic analysis or embedded algorithms so that the company will know with certainty that their products left the factory fully operational and will function normally for at least one year.  Companies that are selling assemblies with several chips that are integrated into other equipment can stop returns, by ensuring that their products do not fail prematurely.

Telecommunications Industry:Companies in the telecommunications industry that produce high-reliability (99.999% availability) public telephone switching network (PTSN) embedded computer servers use prognostic technology to identify the internal PTSN computer server equipment that will be failing in the near future for replacement. This allows the high companies such as Avaya, AT&T and Verizon to meet the stringent industry wide 99.999% availability requirement because equipment that is replaced before it fails is not considered a failure.

Aerospace & Space Industries: Companies are embedding data-driven and model-based prognostic algorithms in equipment so that products and equipment can self prognose their remaining usable life. This offers a new paradigm in maintenance we call condition-based maintenenace, decreasing life cycle cost while increasing reliability to near perfect.

Satellites and launch vehicles of all types still suffer from premature equipment failures getting to space and working in space. These often result in severe financial losses. Today's satellites and launch vehicle were not designed to use prognostic technology but our data-driven prognostic algorithms were developed specifically to search the space vehicle factory test data to identify the on-board equipment that will fail prematurely for replacement by conducting a prognostic analysis of the data. A prognostic analysis is a scientific analysis that illustrates the prognostic markers that indicate the unit will fail prematurely.

For satellites and spacecraft already in space, a prognostic analysis using equipment telemetry will identify the equipment that will be failing within one year of the analysis, stopping surprise equipment failures that often remove spacecraft from service allowing equipment failures to be managed to a positive conclusion.

For satellites that use "single string" equipment, a prognostic analysis completed while the equipment is still on the ground will identify the equipment that will fail prematurely after arriving on orbit for replacement, eliminating the risk for quicker, faster, better, cheaper missions.

Defense Industries: When soldiers and machines are ordered to start marching, the first actions include the shipping of a majority of their vehicles back to the base for repair. Defense industry can meet the 27 hour requirement for infant mortality failures.

Heavy Equipment Industries: In the large equipment industries , prognostic technology and embedded prognostic algorithms allows the identification of heavy equipment that will be failing in the near future so that it can be replaced at a convenient time. This minimizes equipment downtime that often result in huge financial losses associated with equipment that is unproductive.

Nuclear Power Industry: The equipment used to monitor critical processes in nuclear power plants rely on prognostic algorithms for preventing equipment failures that increase risk and uncertainty in the operations of nuclear power plants that are vital the the community.

Financial and Investment Industries: Prognostic algorithms now replace the stock trader on Wall Street to decide which investment is to be purchased and when it should be sold. Companies using these algorithms report that they are no longer susceptable to the financial losses experieneced using current diagnostic tools and probabalistic data.