HALT Versus ALT
Chris and Adam discussing HALT and ALT. What are these? HALT stands for Highly Accelerated Life Testing. ALT stands for Accelerated Life Testing. They sound very similar. But they are not. HALT is a destructive test regime. In fact, a good HALT plan will involve that product failing many times. This is done by subjecting the product to stresses (vibration, thermal cycling et cetera) well beyond actual operating stresses. Some of the failures this creates will not be relevant. That is, they will simply never occur when the product is used ‘normally.’ But many failures are relevant. And by undertaking HALT, we now have a good idea of which failure mechanisms and modes are likely to occur when it is used normally. And this information is incredibly valuable to a design team. ALT on the other hand starts with a failure mechanism you know about. And in a short period of time, you can predict how long that failure mechanism will cause your product to fail when used normally.
Still confused? Well listen to this podcast.
Is HALT Just Another Tool in the Tool Bag?
Kirk and Fred discussing the understanding of HALT and the fact that many see as HALT, the finding of operational limits and weaknesses, as “just another tool in the reliability engineering tool bag” and how for Kirk it is most efficient approach to reliability development.
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An Introduction, Basic Steps, and Examples of How HALT Goes Wrong
The promise of HALT is to find the weaknesses in your design early in the design process. Understanding the basic concepts underlying HALT enable you do so effectively. Let’s talk about this essential discovery tool and how it fits into your program.
HALT Sample Size
Kirk and Fred discussing the challenge of getting samples to test during product development. Early prototypes of new products are typically scarce, expensive, and in high demand to many engineering teams to test. HALT testing requires pushing the product to operational limits and discovering what will fail which is scary for management and engineers that do not understand the value of information that is discovered with empirical limit tests.