The difficulties of failure data collection and operation data changeability hinder the application of fault diagnosis methods to turrets. Hence, an anomaly detection method using non-failure data and considering the change of working conditions was proposed for detecting turrets' anomaly state during operation. The method studied the judgment principle of abnormal data through the multivariate Gaussian distribution(MGD) and the deviation characteristic associated with working conditions. First, the key working conditions and signal characteristics in different turret working processes were determined through statistical analysis. Second, some methods like linear regression, information gain, and generalized regression neural network were selected to model their relationships, respectively. Following that, the deviation of observation from the given signal characteristics is calculated. Finally, the operation data from turret normal state were used to train the model. Many experiments under different working conditions and abnormal simulation were conducted to verify that the proposed model can eliminate the influence of working conditions on abnormal judgment compared to the traditional MGD model.