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Data fusion is the process of combining data from multiple sources, allowing for a more complete and accurate assessment of a system or an environment than could have been otherwise provided by a single source. This paper considers and empirically evaluates a decision-level data fusion method for enabling reliable ocean turbine state detection based on data from multiple sensors. This method involves first generating a classification model from the data from individual sensor channels. For each new incoming instance, the probability that this new instance belongs to each of the possible system states is then computed individually based on observations made by each source. These probabilities are averaged and the system state with the highest probability is selected as the fused output. In a case study presented in this paper, six accelerometers mounted at different positions along a dynamometer test bed for an ocean turbine measure the vibration of various components while the machine is in operation. Each sensor gives unique information about the dynamometer thus ignoring data from one or more sources (or sensors) means possibly discarding useful information. We apply the decision-level fusion method to combine the decisions made from the individual channels and allow for more informed state detection which considers all sources together. Five popular machine learning algorithms are used to generate the classification models from the individual sensor data to see how the performances of each algorithm is affected by fusion and to determine whether this decision level fusion approach can lead to optimal behavior for an ocean turbine state detection module. All five learners are found to benefit from such an approach. Of all the learners, the k-Nearest Neighbors algorithm produces the best results after fusion.