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In this paper, an effective procedure for broken rotor bar (BRB) fault detection in a three-phase squirrel-cage induction machine (SCIM) is proposed. This approach relies on a motor current signature analysis (MCSA) by observing the specific fault-related current component generated by applying the DC injection braking method. Unlike the traditional MCSA, which is commonly focused on the detection of BRB sidebands around the fundamental current component, the proposed methodology introduces a new BRB feature in the current spectrum which makes it much easier for identification. The distinctive time-frequency evolution pattern of this feature provides the reliable identification of BRBs, even under no-load operating conditions, thus overcoming the major drawback of traditional MCSA-based methods. Fault severity classification is easily performed through the magnitude inspection of the BRB fault-related current component. In addition, the proposed approach does not require high-complexity signal processing algorithms to achieve reliable results. The proposed concept is presented theoretically, assisted by a magnetically coupled multiple circuit model of the SCIM, both with healthy and faulty rotor bars. Finally, the experimental tests validate the proposed methodology and demonstrate its effectiveness and usefulness.

Nonprofit, nongovernmental organizations (NGOs) are important development actors that operate in ecosystems of multi and cross-sector stakeholders in order to reach the most vulnerable social groups of people. Having this in mind, our goal was to explore to what extent their social capital influences their knowledge in order to propose a model that could optimize their knowledge management maturity through social resources embedded into their structure. Therefore, we focused on NGOs from the European Union and the Western Balkans operating in the international development and cooperation sector. We first conducted a survey among 215 NGOs and then cross-checked the received data through desk research and in-depth interviews. We utilized collected structured data and devised a binary classification model capable of discriminating high level from the low level of knowledge management maturity in NGOs, and multi-class classification model for estimating the actual category of knowledge management maturity which can simulate the real connection among social capital and existing knowledge management maturity. Our findings provide valuable data to the NGO management, regardless of where the NGO comes from, of the number of employees it has, and the number of projects it runs. The mathematical models based on neural networks used in this paper show with high accuracy what the knowledge management maturity level (for each output) is in case there exists prior information about social capital (inputs).