Burn-in has been proven effective in identify-ing and removing defective products before they are delivered to customers.Most existing burn-in models adopt a one-shot scheme,which may not be sufficient enough for identification.Borrowing the idea from sequential inspections for remaining useful life prediction and accelerated lifetime test,this study proposes a sequential degradation-based burn-in model with multiple periodic inspections.At each inspection epoch,the posterior probability that a product belongs to a normal one is updated with the inspected degradation level.Based on the degradation level and the updated posterior probability,a product can be disposed,put into field use,or kept in the test till the next inspection epoch.We cast the problem into a partially observed Markov decision process to minimize the expected total burn-in cost of a product,and derive some interesting structures of the optimal policy.Then,algorithms are provided to find the joint optimal inspection period and number of inspections in steps.A numerical study is also provided to illustrate the effectiveness of our proposed model.