Detecting Low-Yield Machines in Batch Production Systems Based on Observed Defective Pieces
提出一种利用最大似然估计和自助法置信区间估算每台机器良率的方法，帮助制造工程师低成本识别批量生产中的低良率机器，以便进行维护。
In batch production systems, detecting low-yield machines is essential for minimizing the production of defective pieces, which is a complex problem that currently requires multiple experts, considerable capital, or a combination of both to overcome. To solve this problem, we proposed a cost-efficient and straightforward method that involves using maximum likelihood estimation and bootstrap confidence intervals to estimate per-machine yield; this method enables identification of low-yield machines and generation of a list of these machines. Manufacturing engineers can use the list to perform necessary verification and maintenance processes. Before implementing this method, a manufacturer with 50–500 machines should build a dataset containing approximately 6–20 times as many batches as there are production machines. When this condition is met, the proposed method can be used effectively to detect up to five low-yield machines.