Detection of short-circuits of dc motor using thermographic images, binarization and K-NN classifier

Zadnjih je godina otkriveno mnogo metoda za otkrivanje greške. Jedna od njih je termografija, sigurna i neinvazivna metoda. U radu se opisuje otkrivanje početnog stanja greške u istosmjernom motoru. Analizirane su termografske slike ispravljača istosmjernog motora. Analizirane su dvije vrste termografskih slika: termografska slika ispravljača ispravnog istosmjernog motora i termografska slika ispravljača istosmjernog motora s pregorjelim zavojnicama rotora. Analiza je provedena za metode obrade slike kao što su: ekstrakcija grimizno ljubičaste boje, binarizacija, zbir vertikalnih piksela i zbir svih piksela na slici. Klasifikacija se provela za klasifikator K-Najbliži Susjed (K-Nearest Neighbour classifier). Rezultati analize pokazuju da je predložena metoda učinkovita. Može se također koristiti u dijagnostičke svrhe u industrijskim pogonima.Many fault diagnostic methods have been developed in recent years. One of them is thermography. It is a safe and non-invasive method of diagnostic. Fault diagnostic method of incipient states of Direct Current motor was described in the article. Thermographic images of the commutator of Direct Current motor were used in an analysis. Two kinds of thermographic images were analysed: thermographic image of commutator of healthy DC motor, thermographic image of commutator of DC motor with shorted rotor coils. The analysis was carried out for image processing methods such as: extraction of magenta colour, binarization, sum of vertical pixels and sum of all pixels in the image. Classification was conducted for K-Nearest Neighbour classifier. The results of analysis show that the proposed method is efficient. It can be also used for diagnostic purposes in industrial plants

On the Estimation of the Useful Lifespan of Lubrication Oil under Constrained Functioning Conditions an ANN / Fuzzy Logic Approach

Lubrication oil in automotives is a multi-billion-pound business but the non-optimization of its lifespan entails colossal harm to societies, global resources and the environment. Losses are caused by premature oil change or by machinery wearing due to deteriorated oil. The actual practice in the automotive field follows a predetermined routine-replacement policy that does not consider the wide spectrum of operating conditions. In this paper a decision support model is developed for the determination of the optimum life span of oil under specific working conditions. A data gathering scheme is set to capture the most relevant oils' characteristics from real samples over specific ranges of operation. The relationship between the causal factors and the resulting condition of oil is programmed in an ANN which is complemented with a fuzzy-logic approach in order to predict the optimum lifespan of oil under any set of causal factors. The approach is applied on a case study in Egypt; the model is tested, validated and is believed to fulfil its objectives satisfactorily

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen