  Change-point detection methods are proposed for the case of temporary
failures, or transient changes, when an unexpected disorder is ultimately
followed by a readjustment and return to the initial state. A base distribution
of the "in-control" state changes to an "out-of-control" distribution for
unknown periods of time. Likelihood based sequential and retrospective tools
are proposed for the detection and estimation of each pair of change-points.
The accuracy of the obtained change-point estimates is assessed. Proposed
methods offer simultaneous control the familywise false alarm and false
readjustment rates at the pre-chosen levels.
