Classical machine learning technologies have achieved much success in the learning of a single task at a time. However, in many practical applications we may need to learn a number of related tasks or to rebuild the model from new data, for example, in the problem of fault detection and diagnosis of a system that contains a set of equipments a priori identical but working under different conditions. Indeed, it is common to encounter in industrial problems a number of a priori identical plants, such as in the building or maintenance of a fleet of nuclear power plants or of a fleet of their components. In such cases, the learning of the behavior of each equipment can be considered as a single task, and it would be nice to transfer or leverage the useful information between related tasks. Therefore, Multi-Task Learning (MTL) has become an active research topic in recent years. While most machine learning methods focus on the learning of tasks independently, multi-task learning aims to improve the generalization performance by training multiple related tasks simultaneously. We present a new approach to multi-task learning based on one-class Support Vector Machine (one-class SVM). In the proposed approach, we first make the assumption that the model parameter values of different tasks are close to a certain mean value. Then, a number of one-class SVMs, one for each task, are learned simultaneously. Our multi-task approach is easy to implement since it only requires a simple modification of the optimization problem in the single one-class SVM. Experimental results demonstrate the effectiveness of the proposed approach.