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The present paper is motivated by the application of Predictive Maintenance (PM) techniques in the semiconductor manufacturing environment: such techniques are able, using process data, to make reliable predictions of residual equipment lifetime. The employment of PM yields positive fallouts on the productive process in form of unscheduled downtime reduction, increased spare parts availability and improved overall production quality. One of the main challenges in PM modeling regards the data-driven assessment of relevant process variables when insufficient expert knowledge is available. In this paper, survival models theory is employed jointly with ℓ1 penalization techniques: this allows to obtain sparse models able to select the meaningful process variables and simultaneously predict the remaining lifetime of an equipment. Additionally, frailty modeling techniques are employed to concurrently handle several productive equipments of the same type, exploiting their similarities to increase prediction accuracy. The proposed methodology is validated, illustrating promising results, by means of a semiconductor manufacturing dataset.