The advancements of the telematics and connectivity solutions have provided new opportunities for the field of predictive maintenance. The number of sensors installed on a vehicle is increasing over time, and manufacturers are looking for new ways to improve the uptime of their fleet while at the same time reducing the costs related to unexpected breakdowns. The nature of the aggregated data from vehicles is sequential, and it is interesting to investigate existing methods for modeling partially observable state sequences to detect common patterns of failure. In this paper, we introduce a new approach for predicting turbocharger failures of Volvo trucks. The first step of the method deals with modeling a sequence of readouts from each vehicle using a Markov process. To do so, we identify the most informative signals and then employ spatial similarity clustering on the readouts. We interpret each cluster as a Markov state and further convert the history of a truck into a trajectory of states. This trajectory is then aligned with repairs information to form a standard sequence labeling problem. Finally, we train a hidden Markov model (HMM) classifier for assessing the health condition of the equipment. Empirical evaluations obtained on our realworld dataset of trucks suggest that the proposed method improves the AUC score of the final system up to 6% for predicting failures of a turbocharger.