The detection of faulty machinery and its automated diagnosis is an industrial priority because efficient fault diagnosis implies efficient management of the maintenance times, reduction of energy consumption, reduction in overall costs and, most importantly, the availability of the machinery is ensured. Thus, this paper presents a new intelligent multi-fault diagnosis method based on multiple sensor information for assessing the occurrence of single, combined, and simultaneous faulty conditions in an induction motor. The contribution and novelty of the proposed method include the consideration of different physical magnitudes such as vibrations, stator currents, voltages, and rotational speed as a meaningful source of information of the machine condition. Moreover, for each available physical magnitude, the reduction of the original number of attributes through the Principal Component Analysis leads to retain a reduced number of significant features that allows achieving the final diagnosis outcome by a multi-label classification tree. The effectiveness of the method was validated by using a complete set of experimental data acquired from a laboratory electromechanical system, where a healthy and seven faulty scenarios were assessed. Also, the interpretation of the results do not require any prior expert knowledge and the robustness of this proposal allows its application in industrial applications, since it may deal with different operating conditions such as different loads and operating frequencies. Finally, the performance was evaluated using multi-label measures, which to the best of our knowledge, is an innovative development in the field condition monitoring and fault identification.

故障机械的检测及其自动化诊断是工业优先考虑的问题，因为有效的故障诊断意味着有效管理维护时间、减少能源消耗、降低总成本，最重要的是，确保机械的可用性。因此，本文提出了一种新的基于多传感器信息的智能多故障诊断方法，用于评估感应电机中单个、组合和同时发生的故障情况。所提出方法的贡献和新颖性包括考虑不同的物理量值，例如振动、定子电流、电压和转速，作为机器状态的有意义的信息来源。此外，对于每个可用的物理量级，通过主成分分析减少原始属性数量导致保留数量减少的重要特征，从而允许通过多标签分类树实现最终诊断结果。该方法的有效性通过使用从实验室机电系统获取的完整实验数据进行验证，其中评估了一个健康场景和七个故障场景。此外，对结果的解释不需要任何先验的专业知识，并且该提议的稳健性允许其在工业应用中的应用，因为它可以处理不同的工作条件，例如不同的负载和工作频率。最后，使用多标签度量来评估性能，据我们所知，