Monitoring a system is often not an easy task and the best approach to address it would be to develop a monitoring system that uses data, expert knowledge, and mathematical models. Combining these three sources of information on the system is often unpractical of various reasons such as in complex systems. In this paper, a hybrid method for diagnosing single and multiple simultaneous faults, while considering unknown operating conditions, is proposed. This method consists of a Bayesian classifier combining statistical decisions and fault signature matrix. Several scenarios of operating conditions are simulated for illustrative purposes. The results, in terms of classification rates, show the interest of the hybrid classifier. It demostrates higher capabilities to isolating multiple simultaneous faults that the purely data-driven classifier fails to accomplish.

监视系统通常不是一件容易的事，解决该问题的最佳方法是开发一个使用数据，专家知识和数学模型的监视系统。由于各种原因（例如在复杂的系统中），将系统上的这三种信息源结合在一起通常是不切实际的。本文提出了一种在考虑未知工况的同时诊断单个和多个同时故障的混合方法。该方法由结合统计决策和故障签名矩阵的贝叶斯分类器组成。为了说明目的，模拟了几种运行条件。就分类率而言，结果表明了混合分类器的重要性。