In industry 4.0, machines generate a lot of data about several kinds of events that occur in the production process. This huge quantity of information contains valuable patterns that allow prediction of important events in the appropriate instant. In this paper, we are interested in mining frequent chronicles in the context of industrial data. We introduce a general approach to preprocess, mine, and use frequent chronicles to predict a special event; the failure (a machine crash). Our approach is validated through a set of experiments performed on the chronicle mining phase as well as the prediction phase. Experiments were achieved on synthetic data in addition to a real industrial data set.