This paper presents a predictive model for machine failure analysis, aiming to accurately analyze various causes of machine failure. The predictive model was developed in the following three steps: 1) dataset classification, 2) attribute selection, and 3) centroid calculation. In the first step, the dataset is classified into multiple subdatasets according to the cause of machine failure. Each subdataset is denoted by a cluster. In the second step, the mean of each attribute measured at the same time is calculated and compared with that of the normal case. Then, the attribute that changes most after the machine failure is selected. In the last step, the mean and variance of the selected attribute are calculated to create the elements of each cluster, and then the centroid of each cluster that maximizes the cohesion of the cluster is calculated. The causes of machine failure are determined by comparing the distance between the data of the new machine failure with the centroid of each cluster. To verify the feasibility of the predictive model, we conducted an experimental implementation. The results show that the implemented predictive model is feasible for analyzing the causes of machine failure.