  We propose distribution-free runs-based control charts for detecting location
shifts. Using the fact that given the number of total successes, the outcomes
of a sequence of Bernoulli trials are random permutations, we are able to
control the conditional probability of a signal detected at current time given
that there is not alarm before at a pre-determined level. This leads to a
desired in-control average run length and data-dependent control limits. Two
common runs statistics, the longest run statistic and the scan statitsic, are
studied in detail and their exact conditional distributions given the number of
total successes are obtained using the finite Markov chain imbedding technique.
Numerical results are given to evaluate the performance of the proposed control
charts.
