This disclosure relates generally to improving quality of products and systems and more particularly to predicting the reliability of a product or system.
Generally, reliability is the quality of a product or system over time. This includes the likelihood that the product or system will operate reliably without breaking down and the likelihood that the product or system will last as long as expected. As more companies become concerned with the servicing of their products and systems, it becomes necessary to have an understanding of the reliability of the products and systems. This becomes even more necessary for complex systems such as locomotives, aircraft engines, automobiles, turbines, computers, appliances, etc., where there are many subsystems each having hundreds of replaceable units or components. If there is an understanding of the reliability of the systems, then future failures can likely be anticipated and any downtime associated with correcting the failures can likely be kept to a minimum.
Currently, system engineers address reliability problems using a manual process after the problems have occurred. In this process, system engineers extract data for the system, which includes data from the subsystems and each of their components. The system engineers analyze the data and try to understand the reason or reasons for the failures at the component level. The engineers can then use this understanding to predict future failures of the components. One problem with this manual process is that the prediction of future failures is not very reliable because the results from one engineer to the next will vary because each has their own particular method of analyzing data and understanding failures. Another problem with the manual process is that analyzing data and understanding failures for the components becomes an overwhelming task as the volume of data increases. Still another problem with the manual process is that it cannot deal with problems until they occur. If there was a process that had the capability to predict failures on a consistent and accurate basis, then potential problems could be addressed quickly so that downtime is kept to a minimum.
In order to overcome the above problems, there is a need for an automated approach that can analyze a large amount of data for complex systems and predict failures on a consistent basis before there is actually a problem.